Machine learning techniques and multi-scale models to evaluate the impact of silicon dioxide (SiO2) and calcium oxide (CaO) in fly ash on the compressive strength of green concrete.pdf
Machine learning techniques and multi-scale models to evaluate the impact of silicon dioxide (SiO2) and calcium oxide (CaO) in fly ash on the compressive strength of green concrete.pdf
3D printed geopolymer composites A review.pdfShakerqaidi
This document provides a comprehensive review of 3D printed geopolymer composites. It discusses the advantages of geopolymer materials compared to traditional cement, the performance requirements for 3D printed geopolymers in construction, mix design concepts, and the mechanical and rheological properties of 3D printed geopolymer composites. The review also examines applications and development directions, as well as future challenges. The significance of the study is that it gives an overview of current research on 3D printed geopolymers and identifies gaps to encourage further work on developing these materials for more sustainable building infrastructure.
Prediction of concrete materials compressive strength using surrogate models.pdfShakerqaidi
This document presents a study that developed four surrogate models - linear, quadratic, M5P-tree, and artificial neural network (ANN) - to predict the compressive strength of Ultra-High-Performance Fiber Reinforced Concrete (UHPFRC) based on 306 datasets with 11 input parameters. The models were trained on 192 datasets and validated on 41 testing and 32 experimental datasets. The results showed the ANN model achieved high prediction accuracy. Based on the optimal ANN model, the study presented a closed-form equation for compressive strength prediction found to be reliable and useful for researchers and engineers.
Strength and durability studies on silica fume modified high volume fly ash c...IAEME Publication
This document discusses a study on the strength and durability of silica fume modified high-volume fly ash concrete. Five concrete mixes were tested: a control mix and four mixes where 50% of cement was replaced with fly ash and additional replacement of cement with 5%, 10%, and 15% silica fume. Testing included compressive strength at various ages, rapid chloride permeability, chloride ion diffusion, and carbonation resistance. The addition of silica fume to high-volume fly ash concrete was found to improve mechanical properties and durability compared to fly ash concrete without silica fume.
Optimization of Compressive Strength of Concrete Made with Partial Replacemen...ijtsrd
This research work is aimed at using Scheffe’s Third Degree Model for six component mixtures, Scheffe’s 6,3 to optimize the compressive strength of concrete made through partial replacement of 60 percent of cement with 30 percent of Cassava Peel Ash CPA and 30 percent of Rice Husk Ash RHA . Before now, Nwachukwu and others 2022i has carried out research on the subject matter based on the Scheffe’s second degree model. Due to the upper hand that the third degree has over the second degree in terms of improved compressive strength, this recent work has become very essential. Through the use of Scheffe’s Simplex optimization method, the compressive strengths of the present work based on the third degree model were obtained for 112 different mix proportions. Control experiments were also carried out, and the compressive strengths evaluated. The adequacy of the third degree model was confirmed through the use of the Student’s t test statistics. The highest compressive strength was obtained as 43.75 MPa which is slightly higher than the maximum value obtained by Nwachukwu and others 2022i based on the second degree model. Again, the maximum value is higher than the minimum value specified by the American Concrete Institute ACI , as 20 MPa and also the minimum value specified by ASTM C 39 or ASTM C 469, as 30.75 for good concrete. Thus, the compressive strength value can sustain construction of light weight structures such as construction of Walkways, Pavement slabs etc and some heavy weight structures such as Bridges, Airports etc at the best possible economic and safety advantages. K. C. Nwachukwu | O. Oguaghamba | H. O. Ozioko | B. O. Mama "Optimization of Compressive Strength of Concrete Made with Partial Replacement of Cement with Cassava Peel Ash (CPA) and Rice Husk Ash (RHA) using Scheffe’s (6,3) Model" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-2 , April 2023, URL: https://www.ijtsrd.com.com/papers/ijtsrd55162.pdf Paper URL: https://www.ijtsrd.com.com/engineering/civil-engineering/55162/optimization-of-compressive-strength-of-concrete-made-with-partial-replacement-of-cement-with-cassava-peel-ash-cpa-and-rice-husk-ash-rha-using-scheffe’s-63-model/k-c-nwachukwu
A Review Paper of Prepared Mix Design of M25 and M20 Grade of ConcreteIRJET Journal
This document summarizes several research papers on the use of recycled concrete aggregates and other supplementary cementitious materials in concrete mixes. Specifically, it discusses studies that investigated replacing natural aggregates with recycled concrete aggregates and fly ash, as well as adding metakaolin or glass fibers, to create concrete mixes with sufficient strength and workability. The objectives and findings of each study are briefly described. Overall, the document reviews recent work on developing more sustainable concrete mixes using recycled materials to reduce construction waste.
Rubberized geopolymer composites A comprehensive review.pdfShakerqaidi
This document provides a comprehensive review of rubberized geopolymer composites. It discusses the environmental issues posed by waste rubber tires and methods for recycling tires. The use of crumb rubber as a partial substitute for aggregates in geopolymer concrete is reviewed, with the goal of developing a more sustainable and environmentally friendly building material. The properties of rubberized geopolymer concrete composites are discussed, including fresh/physical properties, mechanical properties, durability properties, and microstructure. The effects of crumb rubber substitution on different properties are addressed. Applications, embodied carbon emissions, and cost analysis of rubberized geopolymer concrete are also highlighted. Knowledge gaps in understanding rubberized geopolymer concrete are identified to guide future research
Experimental evaluation of the durability properties of high performanceIAEME Publication
The document discusses the use of admixtures to improve the durability properties of high performance concrete (HPC). It describes how supplementary cementing materials (SCMs) like fly ash, silica fume, and metakaoline can improve the strength and durability of HPC when used to partially replace cement. The document also examines the acid resistance and sulfate resistance of HPC specimens containing these admixtures through immersion testing in acid and sulfate solutions. Test results showed that HPC with SCMs exhibited greater resistance to chemical attacks compared to plain cement concrete.
3D printed geopolymer composites A review.pdfShakerqaidi
This document provides a comprehensive review of 3D printed geopolymer composites. It discusses the advantages of geopolymer materials compared to traditional cement, the performance requirements for 3D printed geopolymers in construction, mix design concepts, and the mechanical and rheological properties of 3D printed geopolymer composites. The review also examines applications and development directions, as well as future challenges. The significance of the study is that it gives an overview of current research on 3D printed geopolymers and identifies gaps to encourage further work on developing these materials for more sustainable building infrastructure.
Prediction of concrete materials compressive strength using surrogate models.pdfShakerqaidi
This document presents a study that developed four surrogate models - linear, quadratic, M5P-tree, and artificial neural network (ANN) - to predict the compressive strength of Ultra-High-Performance Fiber Reinforced Concrete (UHPFRC) based on 306 datasets with 11 input parameters. The models were trained on 192 datasets and validated on 41 testing and 32 experimental datasets. The results showed the ANN model achieved high prediction accuracy. Based on the optimal ANN model, the study presented a closed-form equation for compressive strength prediction found to be reliable and useful for researchers and engineers.
Strength and durability studies on silica fume modified high volume fly ash c...IAEME Publication
This document discusses a study on the strength and durability of silica fume modified high-volume fly ash concrete. Five concrete mixes were tested: a control mix and four mixes where 50% of cement was replaced with fly ash and additional replacement of cement with 5%, 10%, and 15% silica fume. Testing included compressive strength at various ages, rapid chloride permeability, chloride ion diffusion, and carbonation resistance. The addition of silica fume to high-volume fly ash concrete was found to improve mechanical properties and durability compared to fly ash concrete without silica fume.
Optimization of Compressive Strength of Concrete Made with Partial Replacemen...ijtsrd
This research work is aimed at using Scheffe’s Third Degree Model for six component mixtures, Scheffe’s 6,3 to optimize the compressive strength of concrete made through partial replacement of 60 percent of cement with 30 percent of Cassava Peel Ash CPA and 30 percent of Rice Husk Ash RHA . Before now, Nwachukwu and others 2022i has carried out research on the subject matter based on the Scheffe’s second degree model. Due to the upper hand that the third degree has over the second degree in terms of improved compressive strength, this recent work has become very essential. Through the use of Scheffe’s Simplex optimization method, the compressive strengths of the present work based on the third degree model were obtained for 112 different mix proportions. Control experiments were also carried out, and the compressive strengths evaluated. The adequacy of the third degree model was confirmed through the use of the Student’s t test statistics. The highest compressive strength was obtained as 43.75 MPa which is slightly higher than the maximum value obtained by Nwachukwu and others 2022i based on the second degree model. Again, the maximum value is higher than the minimum value specified by the American Concrete Institute ACI , as 20 MPa and also the minimum value specified by ASTM C 39 or ASTM C 469, as 30.75 for good concrete. Thus, the compressive strength value can sustain construction of light weight structures such as construction of Walkways, Pavement slabs etc and some heavy weight structures such as Bridges, Airports etc at the best possible economic and safety advantages. K. C. Nwachukwu | O. Oguaghamba | H. O. Ozioko | B. O. Mama "Optimization of Compressive Strength of Concrete Made with Partial Replacement of Cement with Cassava Peel Ash (CPA) and Rice Husk Ash (RHA) using Scheffe’s (6,3) Model" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-2 , April 2023, URL: https://www.ijtsrd.com.com/papers/ijtsrd55162.pdf Paper URL: https://www.ijtsrd.com.com/engineering/civil-engineering/55162/optimization-of-compressive-strength-of-concrete-made-with-partial-replacement-of-cement-with-cassava-peel-ash-cpa-and-rice-husk-ash-rha-using-scheffe’s-63-model/k-c-nwachukwu
A Review Paper of Prepared Mix Design of M25 and M20 Grade of ConcreteIRJET Journal
This document summarizes several research papers on the use of recycled concrete aggregates and other supplementary cementitious materials in concrete mixes. Specifically, it discusses studies that investigated replacing natural aggregates with recycled concrete aggregates and fly ash, as well as adding metakaolin or glass fibers, to create concrete mixes with sufficient strength and workability. The objectives and findings of each study are briefly described. Overall, the document reviews recent work on developing more sustainable concrete mixes using recycled materials to reduce construction waste.
Rubberized geopolymer composites A comprehensive review.pdfShakerqaidi
This document provides a comprehensive review of rubberized geopolymer composites. It discusses the environmental issues posed by waste rubber tires and methods for recycling tires. The use of crumb rubber as a partial substitute for aggregates in geopolymer concrete is reviewed, with the goal of developing a more sustainable and environmentally friendly building material. The properties of rubberized geopolymer concrete composites are discussed, including fresh/physical properties, mechanical properties, durability properties, and microstructure. The effects of crumb rubber substitution on different properties are addressed. Applications, embodied carbon emissions, and cost analysis of rubberized geopolymer concrete are also highlighted. Knowledge gaps in understanding rubberized geopolymer concrete are identified to guide future research
Experimental evaluation of the durability properties of high performanceIAEME Publication
The document discusses the use of admixtures to improve the durability properties of high performance concrete (HPC). It describes how supplementary cementing materials (SCMs) like fly ash, silica fume, and metakaoline can improve the strength and durability of HPC when used to partially replace cement. The document also examines the acid resistance and sulfate resistance of HPC specimens containing these admixtures through immersion testing in acid and sulfate solutions. Test results showed that HPC with SCMs exhibited greater resistance to chemical attacks compared to plain cement concrete.
Experimental evaluation of the durability properties of high performanceIAEME Publication
The document discusses the use of admixtures to improve the durability properties of high performance concrete (HPC). It describes how supplementary cementing materials (SCMs) like fly ash, silica fume, and metakaoline can improve the strength and durability of HPC when used to partially replace cement. The document also examines the acid resistance and sulfate resistance of HPC mixtures containing various SCMs through experimental testing of specimens exposed to acids and sulfate solutions.
CHEMICAL AND PHYSICO-MECHANICAL PROPERTIES OF COMPOSITE CEMENTS CONTAINING MI...IAEME Publication
Portland cement is one of the most used materials in the world. Due the environmental problems related to its use, such as CO2 emission and use of non-renewable raw materials, new materials are being researched. In the recent years, there is a great interest in replacing a long time used materials in concrete structure by nanomaterials (NMs) to produce concrete with novel function and better performance at unprecedented levels. NMs are used either to replace part of cement, producing ecological profile concrete or as admixtures in cement pastes. The great reactivity of NMs is attributed to their high purity and specific surface area.
Chemical and physico mechanical properties of composite cemeents containing m...IAEME Publication
This document discusses a study on the chemical and physical properties of composite cements containing micro-silica (SF) and nano-silica (NS). Portland cement was partially substituted with SF up to 15% by mass, then the SF portion was replaced with equal amounts of NS ranging from 2-6% by mass. The hydration behavior, mechanical properties, and microstructure of the cement blends were analyzed. The results showed that both SF and NS improved the hydration and strength compared to plain cement, but NS provided greater improvements due to its higher surface area and pozzolanic activity. The optimum blend was found to be 85% cement, 11% SF, and 4% NS.
Mine tailings-based geopolymers A comprehensive review.pdfShakerqaidi
This document provides a comprehensive review of research on mine tailings-based geopolymers. It discusses how mine tailings, a byproduct of mining, can be used to produce geopolymers as a more sustainable building material. The review summarizes the geopolymerization process, characteristics of mine tailings like their physical and chemical properties, and environmental impacts of tailings disposal. It also examines research on the mechanical, thermal and durability properties of mine tailings-based geopolymer composites and their potential applications. The review identifies remaining knowledge gaps but highlights the promise of this area to both reduce environmental impacts and utilize industrial waste resources.
Experimental Investigations of Mechanical properties on Micro silica (Silica ...IOSR Journals
Abstract : The Now a day, we need to look at a way to reduce the cost of building materials, particularly
cement is currently so high that only rich people and governments can afford meaningful construction. Studies
have been carried out to investigate the possibility of utilizing a broad range of materials as partial replacement
materials for cement in the production of concrete. This study investigated the strength properties of Silica fume
and fly ash concrete. This work primarily deals with the strength characteristics such as compressive, Split
tensile and flexural strength. High performance concrete a set of 7 different concrete mixture were cast and
tested with different cement replacement levels (0%, 2.5%, 5%, 7.5%, 10% 12.5% and15%) of Fly ash (FA) with
silica fume (SF) as addition ( 0%,5%,10 % ,15% ,25and 30%) by wt of Cement and/or each trial super
plasticizer has been added at constant values to achieve a constant range of slump for desired work ability with
a constant water-binder (w/b) ratio of 0.30.Specimens were produced and cured in a curing tank for 3, 7, 14
and 28 days. The cubes were subjected to compressive strength tests after density determination at 3,7,14 and
28 days respectively. The chemical composition and physical composition of micro silica, FlyAsh and cement
were determined. The density of the concrete decreased with increased in percentage of micro silica and Fly ash
replacement up to 15%. Increase in the level of micro silica fume and Fly ash replacement between 30% to 45%
led to a reduction in the compressive strength of hardened concrete. This study has shown that between 15 to
22.5% replacement levels, concrete will develop strength sufficient for construction purposes. Its use will lead
to a reduction in cement quantity required for construction purposes and hence sustainability in the
construction industry as well as aid economic construction.
Keywords: Durability, Fly Ash, High performance Concrete, Silica Fume/Micro Silica, Density, water
absorption
LITERATURE SURVEY ON APPLICATION OF CERAMIC WASTE IN CONCRETEIRJET Journal
This document summarizes literature on using ceramic waste as an aggregate in concrete. Ceramic waste from tile production and demolition sites presents environmental issues. Using ceramic waste as a partial replacement for natural aggregates in concrete can help address these issues while utilizing a waste material. Several studies found that concrete with ceramic waste aggregate can achieve strengths close to or even higher than conventional concrete, especially at lower replacement ratios of 20% or less of natural coarse aggregate. Higher replacement ratios may influence strength properties. Overall, using ceramic waste in concrete has economical and environmental benefits while enabling more sustainable construction materials.
Shear performance of reinforced expansive concrete beams utilizing aluminium ...Shakerqaidi
This study investigated the shear performance of reinforced concrete beams that utilized aluminum waste from computer numerical control (CNC) machining. Twelve reinforced concrete beam specimens were cast and tested with varying amounts of aluminum waste (0%, 1%, 2%, 3% by volume) and shear reinforcement spacing (270, 200, 160 mm). The beams were tested under four-point bending loads. Test results found that beams with aluminum waste had higher load capacities as shear reinforcement spacing decreased, compared to reference beams without waste. Additionally, load capacity decreased as aluminum waste content increased from 0-3%. However, a 1% aluminum waste content produced a tolerable decrease in load capacity. Therefore, the study concluded that aluminum CNC waste can be utilized in
Influence of metakaolin_and_flyash_on_fresh_and_hardened__properties_of_self_...Bpdplanning Madhucon
This document summarizes a study on the effects of metakaolin and fly ash on the fresh and hardened properties of self-compacting concrete. Four concrete mixtures were designed with different binary and ternary combinations of ordinary Portland cement, metakaolin, and fly ash. The fresh properties, including slump flow, V-funnel flow time, and L-box ratio, and the compressive and split tensile strengths at various ages were tested. The results showed that the compressive strength was highest for the binary and ternary blends, while the split tensile strength was highest for the controlled concrete without additions. Incorporating metakaolin and fly ash as binary and ternary blends in self
This document evaluates the performance of steel slag high performance concrete for sustainable pavements. It discusses using electric arc furnace steel slag as a coarse aggregate replacement at different percentages in concrete mixes, along with steel slag powder and silica fume as mineral fillers. Laboratory tests showed that mixes with 50% steel slag replacement had optimal mechanical properties including strength and modulus of elasticity. Field performance modeling also indicated these mixes had reduced cracking and roughness. The results suggest steel slag is a viable sustainable construction material that can improve rigid pavement performance while preserving natural resources.
Effects of alternative ecological fillers on the mechanical, durability, and ...Publ 2022
In this research, the performance of fly ash/GGBS geopolymer mortars made with different quarry waste powder as filler materials by substituted the river sand fine aggregate with different ratios was evaluated based on the mechanical, physical, durability properties and microstructural analysis. Limestone waste, marble waste and basalt waste powder were used as filler materials developing eco-friendly and economical geopolymer from industrial waste as a promising sustainable area of research. A series of tests were conducted such as on strength properties, ultrasonic pulse vel-ocity (UPV), physical properties, abrasion resistance test, splitting tensile strength and microstructure analysis (SEM). The samples were elevated at the high-temperatures of 200 C, 400 C, 600 C and 800 C. Results conducted that the use of limestone waste powder and marble waste powder up to 50% ratio improved the geopolymer composite’s strength. The three filler geopolymer composites positively affected water absorption, strength properties and abrasion ratio results. The current article’s finding has indicated a potential solution, presenting another geopolymer class followed by the successful use of fly ash and quarry waste as significant asset materials. The output of this study is commercially expected to be effective intercession for waste recycled and friendly environmental management conclusions.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
COMPARATIVE STUDY OF USING STEEL SLAG AGGREGATE AND CRUSHED LIMESTONE IN ASPH...IAEME Publication
This document discusses a comparative study of using steel slag aggregate and crushed limestone in asphalt concrete mixtures. The study found the optimal bitumen content was 5.02% for mixtures using crushed limestone aggregate and 5.60% for mixtures using steel slag aggregate. Testing showed the Marshall stability of mixtures using steel slag aggregate was 1.50 higher than those using crushed limestone, indicating steel slag mixtures may better resist rutting in hot climates. Analysis of the aggregates found steel slag had higher voids and required more bitumen to fill those voids compared to crushed limestone.
Study of Mechanical Properties in SCC by Blending Cement Partially With Fly A...IJSRD
The development of self-compacting concrete has been one of the most important materials in the modern building industry. The purpose of this concrete concept is to decrease the risk due to human factor. The use of SCC is spreading worldwide because of its very attractive properties. In the present investigation Blended SCC is the one in which some percentage of cement content used for the concrete is replaced by any of the mineral admixtures. Here, the present study to development of blended self-compacting concrete by replaced in the mineral admixtures using Fly ash 0-30% and metakaolin 0-30% as the weight of cement. Study the rheological properties and mechanical properties of developed blended SCC mixes in the laboratory condition and different curing ages. In recent years, many researchers have established that the use of supplementary cementatious materials (SCMs) like blast furnace slag, silica fume, metakaolin (MK), fly ash (FA) and rice husk ash (RHA) etc. can, not only improve the various properties of concrete both in its fresh and hardened states, but also can contribute to economy in construction costsruning.
EVALUATION OF CEMENTING EFFICIENCY IN QUATERNARY BLENDED SELF-COMPACTING CONC...IAEME Publication
The contribution of metakaolin (MK) to any property of hardened concrete may be
expressed in terms of efficiency factor, k. For this new material to be generally
accepted by the building industry, a good durability must be proven also in
quantitative terms. Therefore a big challenge for researchers within this field is to
determine the strength efficiency of metakaolin (MK) in binary, ternary and
quaternary blended SCC mixes. For calculating the efficiency of Metakaolin,
microsilica and fly ash combination in binary and ternary blended SCC, an equation
has been proposed by author based on the principle of Bolomey’s equation for
predicting the strength of concrete containing mineral admixtures. The strength
efficiency factor ‘k’ is evaluated for three cases in quaternary blended SCC mixes: (1)
micro silica (MS) is singly blended in SCC, (2) micro silica (MS) is blended with fly
ash (FA) in SCC and (3) Metakaolin (MK) is blended with micro silica (MS) and fly
ash (FA) SCC mix. The computed efficiency factors may be incorporated in the design
of a blended concrete mixture, a method known as rational proportioning. The k value
can be used to transform a certain amount of pozzolan to an equivalent amount of
cement in terms of strength contribution; hence, it can be used as a basis for a more
efficient proportioning of blended SCC mixes.
EVALUATION OF CEMENTING EFFICIENCY IN QUATERNARY BLENDED SELF-COMPACTING CON...IAEME Publication
The contribution of metakaolin (MK) to any property of hardened concrete may be expressed in terms of efficiency factor, k. For this new material to be generally accepted by the building industry, a good durability must be proven also in quantitative terms. Therefore a big challenge for researchers within this field is to determine the strength efficiency of metakaolin (MK) in binary, ternary and quaternary blended SCC mixes. For calculating the efficiency of Metakaolin, microsilica and fly ash combination in binary and ternary blended SCC, an equation has been proposed by author based on the principle of Bolomey’s equation for predicting the strength of concrete containing mineral admixtures. The strength efficiency factor ‘k’ is evaluated for three cases in quaternary blended SCC mixes: (1) micro silica (MS) is singly blended in SCC, (2) micro silica (MS) is blended with fly ash (FA) in SCC and (3) Metakaolin (MK) is blended with micro silica (MS) and fly ash (FA) SCC mix. The computed efficiency factors may be incorporated in the design of a blended concrete mixture, a method known as rational proportioning. The k value can be used to transform a certain amount of pozzolan to an equivalent amount of cement in terms of strength contribution; hence, it can be used as a basis for a more efficient proportioning of blended SCC mixes.
The document summarizes research into developing sustainable high-performance self-compacting concrete using ladle slag as a cement replacement. Ladle slag, a steel industry waste material, was used to replace cement at levels of 5%, 10%, 15% and 25% in self-compacting concrete mixtures. The fresh properties, mechanical properties such as compressive and splitting tensile strength, and simple durability properties of the mixtures were evaluated based on standard tests and compared to a control mixture without ladle slag. The results generally showed improvements in properties for replacements up to 15% ladle slag compared to the control mixture. The research aims to evaluate the performance of ladle slag as a supplementary cementitious material in producing sustainable high-performance
Constructing low budget house using recycled concrete.pptxJonSnow618412
This document discusses the use of recycled coarse aggregate from construction and demolition waste in concrete. It was found that replacing natural coarse aggregates with recycled aggregates up to 50% by dosage had little effect on the basic properties of recycled aggregates compared to natural aggregates. Using recycled aggregates in concrete helps reduce waste and protects the environment while not compromising the properties of the concrete. Several recycling techniques used internationally including heating, rubbing and grinding methods are discussed. The conclusions are that recycled aggregates have potential for use in concrete and recycled concrete can be used for high-value applications as it has improved engineering and durability properties.
This document summarizes research on geopolymer concrete, which is an environmentally friendly alternative to traditional Portland cement concrete. It can be created using industrial byproducts such as fly ash and alkaline liquids instead of cement. Several studies discussed found that geopolymer concrete requires high temperature curing but can achieve high strength. The document reviews research showing that replacing fly ash partially with ground granulated blast furnace slag in geopolymer concrete increases strength without the need for oven curing. While research has made progress in developing geopolymer concrete, more information is still needed regarding some aspects of the geopolymerization process.
Strength Study of copper slag & Fly Ash With Replacement Of Aggregate's In Co...IRJET Journal
This document discusses a study on using industrial byproducts like fly ash and copper slag to replace aggregates in concrete for road construction. The study aims to address issues with excessive sand usage by finding sustainable alternatives. Concrete samples of different grades were produced by replacing natural sand with copper slag at varying percentages. The samples were tested for load carrying capacity and flexural strength. The results showed that concrete with 100% copper slag replacement performed similarly to normal concrete, indicating that copper slag can successfully replace sand in concrete for roads. The document also reviews several other studies on using industrial wastes in construction and their findings.
Sustainable utilization of red mud waste (bauxite residue) and slag for the p...Shakerqaidi
Sustainable utilization of red mud waste (bauxite residue) and slag for the production of geopolymer composites A review.pdf
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Experimental evaluation of the durability properties of high performanceIAEME Publication
The document discusses the use of admixtures to improve the durability properties of high performance concrete (HPC). It describes how supplementary cementing materials (SCMs) like fly ash, silica fume, and metakaoline can improve the strength and durability of HPC when used to partially replace cement. The document also examines the acid resistance and sulfate resistance of HPC mixtures containing various SCMs through experimental testing of specimens exposed to acids and sulfate solutions.
CHEMICAL AND PHYSICO-MECHANICAL PROPERTIES OF COMPOSITE CEMENTS CONTAINING MI...IAEME Publication
Portland cement is one of the most used materials in the world. Due the environmental problems related to its use, such as CO2 emission and use of non-renewable raw materials, new materials are being researched. In the recent years, there is a great interest in replacing a long time used materials in concrete structure by nanomaterials (NMs) to produce concrete with novel function and better performance at unprecedented levels. NMs are used either to replace part of cement, producing ecological profile concrete or as admixtures in cement pastes. The great reactivity of NMs is attributed to their high purity and specific surface area.
Chemical and physico mechanical properties of composite cemeents containing m...IAEME Publication
This document discusses a study on the chemical and physical properties of composite cements containing micro-silica (SF) and nano-silica (NS). Portland cement was partially substituted with SF up to 15% by mass, then the SF portion was replaced with equal amounts of NS ranging from 2-6% by mass. The hydration behavior, mechanical properties, and microstructure of the cement blends were analyzed. The results showed that both SF and NS improved the hydration and strength compared to plain cement, but NS provided greater improvements due to its higher surface area and pozzolanic activity. The optimum blend was found to be 85% cement, 11% SF, and 4% NS.
Mine tailings-based geopolymers A comprehensive review.pdfShakerqaidi
This document provides a comprehensive review of research on mine tailings-based geopolymers. It discusses how mine tailings, a byproduct of mining, can be used to produce geopolymers as a more sustainable building material. The review summarizes the geopolymerization process, characteristics of mine tailings like their physical and chemical properties, and environmental impacts of tailings disposal. It also examines research on the mechanical, thermal and durability properties of mine tailings-based geopolymer composites and their potential applications. The review identifies remaining knowledge gaps but highlights the promise of this area to both reduce environmental impacts and utilize industrial waste resources.
Experimental Investigations of Mechanical properties on Micro silica (Silica ...IOSR Journals
Abstract : The Now a day, we need to look at a way to reduce the cost of building materials, particularly
cement is currently so high that only rich people and governments can afford meaningful construction. Studies
have been carried out to investigate the possibility of utilizing a broad range of materials as partial replacement
materials for cement in the production of concrete. This study investigated the strength properties of Silica fume
and fly ash concrete. This work primarily deals with the strength characteristics such as compressive, Split
tensile and flexural strength. High performance concrete a set of 7 different concrete mixture were cast and
tested with different cement replacement levels (0%, 2.5%, 5%, 7.5%, 10% 12.5% and15%) of Fly ash (FA) with
silica fume (SF) as addition ( 0%,5%,10 % ,15% ,25and 30%) by wt of Cement and/or each trial super
plasticizer has been added at constant values to achieve a constant range of slump for desired work ability with
a constant water-binder (w/b) ratio of 0.30.Specimens were produced and cured in a curing tank for 3, 7, 14
and 28 days. The cubes were subjected to compressive strength tests after density determination at 3,7,14 and
28 days respectively. The chemical composition and physical composition of micro silica, FlyAsh and cement
were determined. The density of the concrete decreased with increased in percentage of micro silica and Fly ash
replacement up to 15%. Increase in the level of micro silica fume and Fly ash replacement between 30% to 45%
led to a reduction in the compressive strength of hardened concrete. This study has shown that between 15 to
22.5% replacement levels, concrete will develop strength sufficient for construction purposes. Its use will lead
to a reduction in cement quantity required for construction purposes and hence sustainability in the
construction industry as well as aid economic construction.
Keywords: Durability, Fly Ash, High performance Concrete, Silica Fume/Micro Silica, Density, water
absorption
LITERATURE SURVEY ON APPLICATION OF CERAMIC WASTE IN CONCRETEIRJET Journal
This document summarizes literature on using ceramic waste as an aggregate in concrete. Ceramic waste from tile production and demolition sites presents environmental issues. Using ceramic waste as a partial replacement for natural aggregates in concrete can help address these issues while utilizing a waste material. Several studies found that concrete with ceramic waste aggregate can achieve strengths close to or even higher than conventional concrete, especially at lower replacement ratios of 20% or less of natural coarse aggregate. Higher replacement ratios may influence strength properties. Overall, using ceramic waste in concrete has economical and environmental benefits while enabling more sustainable construction materials.
Shear performance of reinforced expansive concrete beams utilizing aluminium ...Shakerqaidi
This study investigated the shear performance of reinforced concrete beams that utilized aluminum waste from computer numerical control (CNC) machining. Twelve reinforced concrete beam specimens were cast and tested with varying amounts of aluminum waste (0%, 1%, 2%, 3% by volume) and shear reinforcement spacing (270, 200, 160 mm). The beams were tested under four-point bending loads. Test results found that beams with aluminum waste had higher load capacities as shear reinforcement spacing decreased, compared to reference beams without waste. Additionally, load capacity decreased as aluminum waste content increased from 0-3%. However, a 1% aluminum waste content produced a tolerable decrease in load capacity. Therefore, the study concluded that aluminum CNC waste can be utilized in
Influence of metakaolin_and_flyash_on_fresh_and_hardened__properties_of_self_...Bpdplanning Madhucon
This document summarizes a study on the effects of metakaolin and fly ash on the fresh and hardened properties of self-compacting concrete. Four concrete mixtures were designed with different binary and ternary combinations of ordinary Portland cement, metakaolin, and fly ash. The fresh properties, including slump flow, V-funnel flow time, and L-box ratio, and the compressive and split tensile strengths at various ages were tested. The results showed that the compressive strength was highest for the binary and ternary blends, while the split tensile strength was highest for the controlled concrete without additions. Incorporating metakaolin and fly ash as binary and ternary blends in self
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Effects of alternative ecological fillers on the mechanical, durability, and ...Publ 2022
In this research, the performance of fly ash/GGBS geopolymer mortars made with different quarry waste powder as filler materials by substituted the river sand fine aggregate with different ratios was evaluated based on the mechanical, physical, durability properties and microstructural analysis. Limestone waste, marble waste and basalt waste powder were used as filler materials developing eco-friendly and economical geopolymer from industrial waste as a promising sustainable area of research. A series of tests were conducted such as on strength properties, ultrasonic pulse vel-ocity (UPV), physical properties, abrasion resistance test, splitting tensile strength and microstructure analysis (SEM). The samples were elevated at the high-temperatures of 200 C, 400 C, 600 C and 800 C. Results conducted that the use of limestone waste powder and marble waste powder up to 50% ratio improved the geopolymer composite’s strength. The three filler geopolymer composites positively affected water absorption, strength properties and abrasion ratio results. The current article’s finding has indicated a potential solution, presenting another geopolymer class followed by the successful use of fly ash and quarry waste as significant asset materials. The output of this study is commercially expected to be effective intercession for waste recycled and friendly environmental management conclusions.
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The development of self-compacting concrete has been one of the most important materials in the modern building industry. The purpose of this concrete concept is to decrease the risk due to human factor. The use of SCC is spreading worldwide because of its very attractive properties. In the present investigation Blended SCC is the one in which some percentage of cement content used for the concrete is replaced by any of the mineral admixtures. Here, the present study to development of blended self-compacting concrete by replaced in the mineral admixtures using Fly ash 0-30% and metakaolin 0-30% as the weight of cement. Study the rheological properties and mechanical properties of developed blended SCC mixes in the laboratory condition and different curing ages. In recent years, many researchers have established that the use of supplementary cementatious materials (SCMs) like blast furnace slag, silica fume, metakaolin (MK), fly ash (FA) and rice husk ash (RHA) etc. can, not only improve the various properties of concrete both in its fresh and hardened states, but also can contribute to economy in construction costsruning.
EVALUATION OF CEMENTING EFFICIENCY IN QUATERNARY BLENDED SELF-COMPACTING CONC...IAEME Publication
The contribution of metakaolin (MK) to any property of hardened concrete may be
expressed in terms of efficiency factor, k. For this new material to be generally
accepted by the building industry, a good durability must be proven also in
quantitative terms. Therefore a big challenge for researchers within this field is to
determine the strength efficiency of metakaolin (MK) in binary, ternary and
quaternary blended SCC mixes. For calculating the efficiency of Metakaolin,
microsilica and fly ash combination in binary and ternary blended SCC, an equation
has been proposed by author based on the principle of Bolomey’s equation for
predicting the strength of concrete containing mineral admixtures. The strength
efficiency factor ‘k’ is evaluated for three cases in quaternary blended SCC mixes: (1)
micro silica (MS) is singly blended in SCC, (2) micro silica (MS) is blended with fly
ash (FA) in SCC and (3) Metakaolin (MK) is blended with micro silica (MS) and fly
ash (FA) SCC mix. The computed efficiency factors may be incorporated in the design
of a blended concrete mixture, a method known as rational proportioning. The k value
can be used to transform a certain amount of pozzolan to an equivalent amount of
cement in terms of strength contribution; hence, it can be used as a basis for a more
efficient proportioning of blended SCC mixes.
EVALUATION OF CEMENTING EFFICIENCY IN QUATERNARY BLENDED SELF-COMPACTING CON...IAEME Publication
The contribution of metakaolin (MK) to any property of hardened concrete may be expressed in terms of efficiency factor, k. For this new material to be generally accepted by the building industry, a good durability must be proven also in quantitative terms. Therefore a big challenge for researchers within this field is to determine the strength efficiency of metakaolin (MK) in binary, ternary and quaternary blended SCC mixes. For calculating the efficiency of Metakaolin, microsilica and fly ash combination in binary and ternary blended SCC, an equation has been proposed by author based on the principle of Bolomey’s equation for predicting the strength of concrete containing mineral admixtures. The strength efficiency factor ‘k’ is evaluated for three cases in quaternary blended SCC mixes: (1) micro silica (MS) is singly blended in SCC, (2) micro silica (MS) is blended with fly ash (FA) in SCC and (3) Metakaolin (MK) is blended with micro silica (MS) and fly ash (FA) SCC mix. The computed efficiency factors may be incorporated in the design of a blended concrete mixture, a method known as rational proportioning. The k value can be used to transform a certain amount of pozzolan to an equivalent amount of cement in terms of strength contribution; hence, it can be used as a basis for a more efficient proportioning of blended SCC mixes.
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Machine learning techniques and multi-scale models to evaluate the impact of silicon dioxide (SiO2) and calcium oxide (CaO) in fly ash on the compressive strength of green concrete.pdf
2. Construction and Building Materials 400 (2023) 132604
2
economy, market and consumerism, and population increase. Overall,
climate change is a clear result of these factors resulting in dangerous
issues for humanity [3]. The rapid development of globalization and
population growth has caused an increase in building construction [4].
The construction business is growing exponentially, resulting in an
increasing demand for construction materials, especially cement [5].
Nowadays, the construction industry is one of the largest environmental
impacts among all human activities [6]. The negative environmental
impacts associated with the widespread use of cement have encouraged
researchers to find a satisfactory solution for reducing the demand for
cement. As the greatest strategy to reduce CO2 emissions is to replace
cement with a suitable substitute in percentage, several studies have
been done to find viable replacements for cement in concrete mixtures.
Supplementary cementitious materials (SCMs) like fly ash [7], silica
fume [8], ground granulated blast furnace slag [9], rice husk ash [10],
and metakaolin [11] can be added to the concrete as a partial replace
ment. Fly ash is a widely available material worldwide [12]. It is a very
fine powder with a fineness more than cement. Fly ash is commonly a
by-product material found in coal industries and collected in a baghouse
or on an electrostatic precipitator. Fly ash is one of the best alternatives
for cement in concrete; it provides excellent mechanical properties [13].
Utilizing fly ash in concrete economizes the mix, protects the environ
ment from disposal, conserves more land, and reduces cement produc
tion; overall, it can improve the properties of green and hardened
concrete [14–16].
Fly ash is a pozzolanic material rich in aluminous and siliceous
substances. Generally, the chemical compositions and their ratios are
source-dependent. According to the ASTM C619 [17], fly ash is divided
into two classes based on the quantity of chemical compositions; class C
and F [18,19]. Both classes of fly ash can be added to concrete in a
specific range to enhance mechanical properties and durability.
Numerous studies have been conducted on cement-based concrete
modified with different types, classes, and quantities of fly ash. The
improvement greatly depends upon the type and ratio of fly ash. The fly
ash type correspondence to the number of chemical substances and their
impact on the concrete properties. Among the chemical compositions of
fly ash, the effect of various silicon dioxide (SiO2) and calcium oxide
(CaO) ratios on the mechanical properties of concrete has been inves
tigated in previous studies. Class C of fly ash provides a greater
compressive strength value for the same cement dosage and fly ash
percentage than class F [19]. Fly ash with a higher CaO content tends to
have a higher reactivity [20]. The fly ash compounds such as CaO at
early ages and SiO2 at later ages are associated with the strength gain of
concrete [21].
Compressive strength is a significant mechanical property in con
crete considered in all concrete structure works. Concrete is a hetero
geneous material comprising different substances, such as cement,
aggregate, water, and admixtures, resulting in different compressive
strength values [22]. All the physical, chemical and mechanical char
acteristics of these ingredients and their quantity impact the compres
sive loading capacity of concrete [23]. The compressive strength value is
achieved in the laboratory during the testing of the concrete samples by
crushing the cylinders or cubes of standard dimension [24]. The
experimental method is standardized all over the world. However,
nowadays, laboratory tests are inefficient and uneconomical since it is
costly and time-consuming. Recently, with the advancement of Artificial
Intelligence (AI), Machine learning algorithm (ML), has been applied to
predict several mechanical properties in concrete [25]. ML techniques
such as regression, clustering, and classification can estimate various
factors with varying efficiency and accurately forecast concrete
compressive strength value [26]. Modeling the properties of concrete
can be developed in various ways, including statistical techniques,
computational modeling, and newly created tools like artificial neural
networks (ANN), regression analysis, and the M5P-tree model [27–31].
In the current study, 236 experimental values of compressive
strength have been collected from the literature. The concrete mixtures
included fly ash content (FA) ranging (from 71 – 316 kg/m3
), CaO (0.31
– 32%), SiO2 (30.5 – 62.54%), cement content (C) (67 – 356 kg/m3
),
cement replacement (CR) (18 – 100%), coarse aggregate (CA) (801 –
1246 kg/m3
), fine aggregate (S) (522 – 905 kg/m3
), water-to-binder
ratio (w/b) (0.28 – 0.6), and curing time (t) (3 – 365 days). As well as
the compressive strength (CS) ranged from 7.98 to 92.93 MPa. To check
the reliability and applicability of the models, CS is divided into three
different ranges; 5 to 34 MPa, 35 to 64 MPa, and 65 to 95 MPa. Then the
most effective model is applied to those ranges. This study proposes
models to predict compressive strength values using different analyzing
approaches. The influence of CaO and SiO2 contents at various mix
proportions on the compressive strength of cement-based concrete
modified with fly ash was studied and qualified in this study. In the
modeling part, full quadratic (FQ), nonlinear (NLR), multi-linear (MLR),
and artificial neural network (ANN) models are used to develop pre
dictive models. In addition, different statistical tools such as Correlation
coefficient (R2
), Root mean squared error (RMSE), Mean absolute error
(MAE), Scatter index (SI), Objective function (OBJ), and an a20-index
are used to evaluate the reliability and accuracy of the developed
models.
2. Research significant
The following are the general objectives of this study:
i. Statistically analyzing the collected data and evaluating the effect
of CaO and SiO2 on the compressive strength of fly ash-modified
concrete, such as coarse aggregate, fine aggregate, cement,
cement replacement, water-to-binder ratio (w/b), fly ash, cal
cium oxide (CaO,%), silicon dioxide (SiO2,%), and curing time.
ii. Creating an accurate and reliable model for predicting the
compressive strength of fly ash-based concrete.
iii. Conducting sensitivity analysis to determine the most important
parameter in predicting the compressive strength of concrete
modified with various types and classes of fly ash.
3. Methodology
The methodology of the current study includes a series of steps which
has been drawn as a flowchart and shown in Fig. 1. The main steps
involve the following;
i. Creating and collecting data on the cement-based concrete in
different strength ranges of (5 – 34 MPa), (35 – 64 MPa), and (65
– 95 MPa) from the literature.
ii. Considering coarse aggregate (CA), fine aggregate (S), cement
content (C), fly ash content (FA), cement replacement (CR),
water-to-binder ratio (w/b), calcium oxide (CaO), silicon dioxide
(SiO2), and curing time (t) as predictors for the models whereas,
the compressive strength (CS) is considered as a target value of
the models.
iii. Randomly mixing the collected data into two groups, 70% of the
data as training and 30% as testing.
iv. Developing predictive models using Full Quadratic (FQ),
Nonlinear Regression (NLR), Multi-linear Regression (MLR), and
Artificial Neural Networks (ANN) models.
v. Evaluating the proposed models based on R2
, RMSE, MAE, OBJ,
SI, and the a-20 index.
vi. Conducting sensitivity analysis to find the essential parameter in
predicting compressive strength for the concrete modified with
fly ash.
D. Kakasor Ismael Jaf et al.
3. Construction and Building Materials 400 (2023) 132604
3
3.1. Data collection
To develop a reliable model to predict the compressive strength of
concrete modified with fly ash, 236 data were collected from the liter
ature, including various fly ash ratios with class F and C, resulting in
different ratios of main chemical compositions such as; CaO and SiO2. As
well as different coarse and fine aggregate content, cement content, and
the curing regime was involved. Table 1 shows a summary of the
collected data. A total of 236 data were selected in this study, and based
on the information from the literature [32–34], the data were divided
into two groups using RAND Function.
Training data was included;0.70*No.ofdataset(236) = 165 Data.
The training data was used to develop the models.
Tested data; 0.30*No.ofdatatset(236)= 71 Data The testing dataset
was used to validate (Check) the developed equations based on 165
datasets.
3.2. Pre-processing
Pre-processing is required before using the dataset. Pre-processing
techniques improve the performance of computational models. As a
result, all discontinuous features (independent variables) were con
verted to dummy variables using Pandas, an open-source Python pack
age. As demonstrated in the box plot (Fig. 2), some features in the
created dataset had values that spanned an exceptionally wide range,
which was incompatible with model learning. Therefore, the average of
the upper and lower bounds was employed instead. Furthermore, using
the original non-normalized data may have influenced the ML model’s
performance [35]. This study pre-processed the database by converting
each independent variable to a value between zero and one using the
following equation, Eq. (1).
Xf =
(Xi − Ximin)
(
Xf max − Xf min
)
(Ximax − Ximin)
+ Xf min (1)
where; Xi is the old value and Xf is the new value. The Xf min is zero and
Xf max is one.
3.3. Statistical analysis
The collected data is statistically analyzed to determine the distri
bution of each independent variable with the dependent variable
(compressive strength). Therefore, statistical parameters such as Mean,
Standard Deviation (SD), Variance (Var), Kurtosis (Kur), Skewness
(Skew), minimum (Min), and Maximum (Max) are obtained. For kur
tosis, the negative value indicated a short distribution tail, while the
positive indicated a longer tail. However, skewness shows the distribu
tion of the variable, whether in the right or the left tail, with the right for
positive and the left for negative values.
The relation between compressive strength and the mentioned in
dependent variables is plotted in Fig. 3. The compressive strength data is
shown in a frequency histogram in Fig. 4. The statistical analysis is
summarized in Table 2.
Fig. 1. Methodology of Research; ANN, artificial neural network; FQ, full quadratic; NLR, nonlinear; MLR, multi-linear; R2
, correlation coefficient; RMSE, root mean
squared error; MAE, mean absolute error; SI, scatter index; OBJ, objectives.
D. Kakasor Ismael Jaf et al.
4. Construction and Building Materials 400 (2023) 132604
4
3.4. Specimen sizes
The specimen parameters such as size and shape are important fac
tors in the compressive strength of concrete. Those factors were inves
tigated in the normal and high-strength concrete in the previous study
[36,37]. In the current study, the database utilized to create the models
comprised test samples of different sizes and shapes for assessing
compressive strength. Standard cylinders with S1 (100 × 200) and S2
(150 × 300) mm, and cubes with S3 (100 × 100 × 100) and S4 (150 ×
150 × 150) mm were included.
The samples were all utilized to create predictive models. Using
different models, the constructed models were evaluated using various
specimen sizes to determine the efficiency of predicted compressive
strength. 39% of the data were S1, and 18%, 41%, and 2% were S2, S3,
and S4, respectively. The total number for all specimen sizes in the
training and testing datasets is shown in Fig. 5.
Fig. 2. Box plot of the independent variables.
Table 1
Summary of collected data for cement-based concrete modified with fly ash.
Reference No.
of
Data
Coarse
aggregate,
CA (kg/m3
)
Sand, S
(kg/m3
)
Cement, C
(kg/m3
)
Fly ash
(kg/m3
)
Cement
replacement,
CR (%)
Water-to-
binder
ratio, (w/
b)
CaO (%) SiO2 (%) Curing
time, t
(Days)
Compressive
strength, CS
(MPa)
[58] 53 801–––1090 687–905 144–356 92–168 35–100 0.41–0.60 2.32 52.1 3, 7, 14,
28, 56 and
91
11.78–45.99
[15] 60 1270 522–671 160–320 80–240 20–60 0.30–0.40 1.54 62.54 7, 28, 56,
90,180,
256 and
365
16.99–92.93
[59] 42 1016–1207 607–854 107–328 71–316 18–74 0.3–0.4 27.6–31.9 30.5–34.9 3, 7, 28,
90 and
365
15.4–72.4
[60] 13 1089–1124 726–749 179–182 219–222 55 0.3–0.4 0.3–15.8 36.9–56.8 3, 7, 28,
56, and 91
21.2–44.6
[61] 9 843–864 842–866 161–247 155–254 40–60 0.4–0.5 13.4 52.4 7 and 28 15.6–48.3
[62] 23 1200 600 120–200 200–280 50–70 0.3–0.4 2.4–2.6 44.9–50.2 3, 7, 28,
90, 180,
and 365
7.98–81.54
[63] 15 1181–1246 600–819 66.9–200 167–280 50–80 0.28–0.40 2.6–26.3 35.2–50.2 7, 14, 28,
90, 180
and 365
16–67.5
[64] 17 1095–1195 740–797 150–274 117–204 30–52 0.4–0.5 4.2–13.4 42.7–52.4 7, 28, 56,
and 96
25.5–42.5
[65] 4 1114–1135 727–740 157–238 159–236 40–60 0.3 2.9 52 14 and 28 38–62
Remark 236 Ranged
between 801
and 1200
Ranged
between
522 and
905
Ranged
between
107 and
356
Varied
between
71 and
316
Ranged
between 18
and 100
Varied
between
0.28 and
0.6
Ranged
between
0.3 and
31.9
Ranged
between
30.5 and
62.54
Ranged
between 3
and 365
Ranged
between 7.98
and 92.93
D. Kakasor Ismael Jaf et al.
5. Construction and Building Materials 400 (2023) 132604
5
Fig. 3. Marginal plots for the compressive strength of concrete with (a) coarse aggregate (kg/m3
) (b) fine aggregate (kg/m3
) (c) cement (kg/m3
) (d) fly ash (kg/m3
)
(e) cement replacement (%) (f) water-to-binder ratio (g) CaO (%) (h) SiO2 (%), and (i) curing time (days).
D. Kakasor Ismael Jaf et al.
6. Construction and Building Materials 400 (2023) 132604
6
Fig. 3. (continued).
D. Kakasor Ismael Jaf et al.
7. Construction and Building Materials 400 (2023) 132604
7
Fig. 3. (continued).
D. Kakasor Ismael Jaf et al.
8. Construction and Building Materials 400 (2023) 132604
8
Fig. 3. (continued).
D. Kakasor Ismael Jaf et al.
9. Construction and Building Materials 400 (2023) 132604
9
Fig. 3. (continued).
Fig. 4. Histogram for compressive strength (MPa) of concrete modified with fly ash.
D. Kakasor Ismael Jaf et al.
10. Construction and Building Materials 400 (2023) 132604
10
3.5. Modeling
The correlation between dependent and independent variables es
tablishes a direct relationship between the concrete mixture proportion
and compressive strength. The datasets were randomly divided into
training and testing datasets [38,39]. The training dataset is used to
develop the models, whereas the testing dataset checks those developed
models against unobserved data during training. Based on the correla
tion matrix, a poor correlation between independent variables such as
CA, S, C, FA, CR, w/b, CaO, and SiO2 and the dependent variable which
is CS is observed as shown in Fig. 6. The correlations are 0.47, − 0.30,
0.40, − 0.23, − 0.53, − 0.58, − 0.08, and − 0.34, respectively. However,
an acceptable correlation coefficient between CS and t is noticed by
0.75. Therefore, different multivariable models are advanced in the
following section to establish an analytical model to predict the
compressive strength of concrete.
3.5.1. Full quadratic (FQ) model
Equation (2) shows the full quadratic formula, representing a rela
tionship between compressive strength, the first and second degrees of
each independent variable, and the interaction between independent
variables [32,40–44]. This model is found to have a complex combina
tion of mathematical terms [45,46].
CS = β1 + β2(CA) + β3(S) + β4(C) + β5(FA) + β6(CR) + β7
(w
b
)
+ β8(CaO) + β9(SiO2) + β10(t) + β11(CA)(S) + β12(CA)(C)
+ β13(CA)(FA) + β14(CA)(CR) + β15(CA)
(w
b
)
+ β16(CA)(CaO)
+ β17(CA)(SiO2) + β18(CA)(t) + β19(S)(C) + β20(S)(FA)
+ β21(S)(CR) + β22(S)
(w
b
)
+ β23(S)(CaO) + β24(S)(SiO2) + β25(S)(t)
+ β26(C)(FA) + β27(C)(CR) + β28(C)
(w
b
)
+ β29(C)(CaO)
+ β30(C)(SiO2) + β31(C)(t) + β32(FA)(CR) + β33(FA)
(w
b
)
+ β34(FA)(CaO) + β35(FA)(SiO2) + β36(FA)(t) + β37(CR)
(w
b
)
+ β38(CR)(CaO) + β39(CR)(SiO2) + β40(CR)(t) + β41
(w
b
)
(CaO)
+ β42
(w
b
)
(SiO2) + β43
(w
b
)
(t) + β44(CaO)(SiO2) + β45(CaO)(t)
+ β46(SiO2)(t)+β47(CA)2
+ β48(S)2
+ β49(C)2
+β50(FA)2
+β51(CR)2
+ β52
(w
b
)2
+ β53(CaO)2
+β54(SiO2)2
+ β55(t)2
(2)
CS, CA, S, C, FA, CR, w/b, CaO, SiO2, and t are compressive strength,
coarse aggregate content, fine aggregate content, cement content, fly
ash content, cement replacement, water-to-binder ratio, calcium oxide
Table 2
Summary of statistical analysis of the concrete parameters.
Variables Statistical parameters
Mean Standard deviation Variance Kurtosis Skewness Min Max
CA (kg/m3
) 1132.2 120.73 14575.73 − 0.265 − 0.666 801 1270
S (kg/m3
) 709.1 100.57 10114.55 − 1.2776 − 0.0785 522 905.4
C (kg/m3
) 211.2 59.809 3577.099 − 0.432 0.240 67 356
FA (kg/m3
) 173.7 58.262 3394.479 − 0.827 0.278 71 316
CR (%) 49.9 15.506 240.440 0.264 0.119 18 100
w/b 0.39 0.083 0.007 − 0.324 0.732 0.28 0.60
CaO (%) 8.75 10.929 119.444 − 0.252 1.232 0.31 31.9
SiO2 (%) 49.42 10.755 115.676 − 0.890 − 0.463 30.5 62.54
t (Dyas) 86.9407 110.848 12287.359 1.071 1.520 3 365
CS (MPa) 40.75 18.52 342.98 0.0174 0.77 7.98 92.93
Fig. 5. A summary of the sample sizes utilized in training and testing for developing models.
D. Kakasor Ismael Jaf et al.
11. Construction and Building Materials 400 (2023) 132604
11
ratio, silicon dioxide rato, and curing time, respectively. Furthermore,
the β1 to β55 are model parameters.
3.5.2. Nonlinear regression (NLR) model
The following formula is a typical form to develop a nonlinear
regression model [32,47,48] for predicting the compressive strength of
concrete containing different types of fly ash with various quantities of
CaO and SiO2, Eq. (3). NLR model has a disadvantage which is the
complex mathematical operation [49].
CS, CA, S, C, FA, CR, w/b, CaO, SiO2, and t are compressive strength,
coarse aggregate content, fine aggregate content, cement content, fly
ash content, cement replacement, water-to-binder ratio, calcium oxide
ratio, silicon dioxide ratio, and curing time, respectively. The α1 to α18 is
defined as the model parameters.
3.5.3. Multi-linear regression (MLR) model
The compressive strength of the concrete containing different CaO
and SiO2 ratios is predicted using the multi-linear regression model.
Equation (4) represents the product of the variables affecting the
compressive strength of the concrete in exponential and constant terms.
The MLR model has some advantages, such as simple mathematical
operation and easy implementation resulting in more accurate results
compared to the LR model. However, the MLR model greatly depends on
the number of data; fewer number provides less accuracy. Therefore,
this model has poor quality [32].
CS = α1(CA)α2
(S)α3
(C)α4
(FA)α5
(CR)α6
(w
b
)α7
(CaO)α8
(SiO2)α9
(t)α10
(4)
Where α1toα10 are defined as the model parameters
βn = an(CA) + bn(S) + cn(C) + dn(FA) + en(CR) + fn
(w
b
)
+ gn(CaO)
+ hn(SiO2) + in(t) + b
(5a)
CS =
Node1
1 + e− β1
+
Node2
1 + e− β2
+ ⋯ +
Noden
1 + e− βn
+ Threshold (5b)
3.5.4. Artificial neural network (ANN model)
The Artificial Neural Network (ANN) is a computational system that
analyzes data like the human brain. This model is also a machine
learning system used in construction engineering for various numerical
forecasts and challenges [27,29,31,42]. ANN is made up of three layers;
input, hidden, and output, which are linked by biases and weights
[44,50], Eq. (5). This study designed a multi-layer feed-forward network
with concrete components (CA, S, C, FA, CR, w/b, CaO, SiO2, and t) as
input and CS as output. In the output layer, a sigmoid activation function
is utilized.
Output = f
(
∑
n
j=1
wjxj + bias
)
(5)
j is the number of input variables, xj is the input parameter, and bias is
the threshold for the sigmoid activation function.
Fig. 7 shows the typical process of the ANN result. As well as the
typical procedure for ANN calculation in a single hidden layer is illus
trated in Eq. (5).
The Node1 to Noden, and Threshold are directly calculated.
3.6. Metrics to evaluate developed models
The developed models are evaluated using different parameters such
as correlation coefficient (R2
), root mean squared error (RMSE), mean
absolute error (MAE), scatter index (SI), objectives (OBJ), and a20-
index. The values of the a20-index are predicted to be unity for a per
fect predictive model. The proposed a20-index has the advantage of
having physical engineering meaning; it declares the number of samples
that satisfy expected values with a 20% variance from experimental
values [51]. The following equations (6) – (11) are utilized to determine
each mentioned criterion [46,52–54].
CA
S
C
FA
CR
w/b
CaO
t
CS
CA S C FA CR w/b CaO t CS
SiO2
SiO2
Fig. 6. Correlation matrix plot between the dependent and independent variables of concrete modified with fly ash.
CS = α1(CA)α2
+ α3(S)α4
+ α5(C)α6
+α7(FA)α8
+α9(CR)α10
+ α11
(w
b
)α12
+ α13(CaO)α14
+α15(SiO2)α16
+ α17(t)α18
(3)
D. Kakasor Ismael Jaf et al.
12. Construction and Building Materials 400 (2023) 132604
12
R2
=
⎛
⎜
⎜
⎝
∑
i(yp − x) × (yi − y)
̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅
∑
i
(yp − x)2
√
×
̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅
∑
i
(yi − y)2
√
⎞
⎟
⎟
⎠
2
(6)
RMSE =
̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅
∑
n
i=1
(yi − yp)2
N
√
√
√
√
√
(7)
MAE =
∑n
i=1(|yi − yp|)
n
(8)
SI =
RMSE
yi
(9)
OBJ =
(
ntr
nall
×
RMSEtr + MAEtr
R2
tr + 1
)
+
(
ntst
nall
×
RMSEtst + MAEtst
R2
tst + 1
)
(10)
a20 − index =
N20
N
(11)
where;
yp = predicted value of compressive strength.
yi = experimental value of compressive strength.
x = mean of predicted compressive strength values.
y = mean of experimental compressive strength data.
ntr = number of the training dataset.
ntst = number of the testing dataset.
nall = a total number of datasets, including training and testing.
N = total number of data in the dataset.
N20 = total number of predicted to the experimental data of
compressive strength ratio ranged from 0.8 to 1.2.
The value of R2
and a-20 index is commonly between (0to1), and the
best value is considered 1. The RMSE, MAE, and OBJ values are between
(0to∞), the value is recommended to be low as possible, and zero is the
best value. As well as, if the value of SI was smaller than 0.1, the model is
qualified as an excellent performance. Whereas the SI value is between
(0.1 to 0.2), (0.2 to 0.3), and greater than 0.3, indicating the good, fair,
and poor performance of the model, respectively [44,55].
4. Results and discussion
4.1. Variation of predicted and measured compressive strength
4.1.1. FQ model
The full quadratic model is one of the most effective models due to its
advanced mathematical expression. This model was used to predict the
compressive strength of concrete modified with various fly ash types. It
was derived based on mathematical parameters such as constant, linear,
variable product terms/interaction, and squared variables. Equation
(12) shows the formula for the FQ model for predicting the compressive
strength of concrete. The correlation between predicted and measured
compressive strength for the FQ model is illustrated in Fig. 8. The value
of R2
and RMSE was 0.94 and 4.627 for training and 0.887 and 4.57 for
testing data, respectively. The training dataset contains an error line of
15 to − 15%, meaning that 85% of the data falls between 0.85 and 1.15
for the predicted to measured compressive strength ratio.
CS = − 1.26 − 0.17(CA) − 0.03(S) + 0.9(C) − 0.35(FA) − 1.36(CR)
+ 1.2
(w
b
)
− 2.8(CaO) + 5.46(SiO2) + 0.4(t) + 0.0002(CA)(S)
− 0.0002(CA)(C) − 0.0002(CA)(FA) + 0.001(CA)(CR)
− 0.05(CA)
(w
b
)
+ 0.0001(CA)(CaO) − 0.003(CA)(SiO2)
− 0.0003(CA)(t) − 0.0004(S)(C) − 0.001(S)(FA) + 0.00003(S)(CR)
− 0.19(S)
(w
b
)
+ 0.003(S)(CaO) + 0.0012(S)(SiO2) − 0.0004(S)(t)
+ 0.002(C)(FA) − 0.0003(C)(CR) − 0.18(C)
(w
b
)
− 0.0007(C)(CaO)
− 0.0097(C)(SiO2) − 0.0002(C)(t) + 0.004(FA)(CR) + 0.48(FA)
(w
b
)
− 0.004(FA)(CaO) − 0.005(FA)(SiO2) − 0.001(FA)(t)
− 0.16(CR)
(w
b
)
+ 0.04(CR)(CaO) + 0.002(CR)(SiO2)
+ 0.003(CR)(t) + 1.95
(w
b
)
(CaO) + 0.04
(w
b
)
(SiO2) − 0.26
(w
b
)
(t)
− 0.03(CaO)(SiO2) + 0.001(CaO)(t) + 0.6(SiO2)(t)
+ 0.0001(CA)2
− 0.000002(S)2
− 0.0005(C)2
+ 0.001(FA)2
− 0.0003(CR)2
+ 0.0001
(w
b
)2
+ 0.0001(CaO)2
+ 0.0004(SiO2)2
− 0.0003(t)2
(12)
Fig. 7. Typical ANN output technique in a single node.
D. Kakasor Ismael Jaf et al.
13. Construction and Building Materials 400 (2023) 132604
13
No. of training dataset = 165, R2
= 0.94, RMSE = 4.627 MPa.
4.1.2. NLR model
The nonlinear regression model was also used to predict the
compressive strength of fly ash-modified concrete. The NLR model result
is shown in Eq. (13). The relationship between predicted and measured
compressive strength is displayed in Fig. 9. The training dataset has an
R2
and RMSE value of 0.816 and 8.36 MPa. Also, an R2
of 0.47 and RMSE
of 9.62 MPa for the testing dataset were observed. According to the
RMSE results, the NLR model performance is fair. The FQ model results
provide better performance than the NLR model. The error line in the
training dataset is 30 to − 30%, which means that 70% of the data falls
between 0.7 and 1.3 for the predicted to measured compressive strength
ratio.
No. of training dataset = 165, R2
= 0.816, RMSE = 8.36 MPa.
4.1.3. MLR model
The multi-linear regression model is another model utilized to pre
dict the compressive strength of concrete modified with fly ash. It is
counted as one of the least effective models due to its elementary
mathematical expression. The MLR model formula consists of constant
terms with terms that rose to the power of constant variables. The
following equation presents the variables and their relationship in
detail; Eq. (14). The relation between predicted and measured
compressive strength is illustrated in Fig. 10. The R2
and RMSE were
0.79 and 9.22 for training, 0.61 and 8.25 for testing data, respectively.
The error line in the training dataset is 25 to − 25%, which means that
75% of the data falls between 0.75 and 1.25 for the projected to
measured compressive strength ratio.
No. of training dataset = 165, R2
= 0.79, RMSE = 9.22 MPa.
4.1.4. ANN model
The Optimum ANN network structure is the last model used to pre
dict the compressive strength of concrete. Five trials were selected
depending on the RMSE and MAE values, as shown in Fig. 11. Among
those five trials, the lowest RMSE and MAE values were observed in the
CS = 140.66 (CA)− 0.008
+ 0.68(S)− 2.47
+ 1.06(C)0.0002
− 0.041(FA)0.002
− 3.096(CR)− 6.87
+ 13.94
(w
b
)− 1.14
− 26.95 (CaO)− 0.004
− 227(SiO2)− 0.146
+ 5.32 (t)0.35
(13)
Fig. 8. Comparison between measured and predicted CS using FQ model for training and testing dataset.
CS= 1.3 (CA)0.8
(S)0.35
(C)0.16
(FA)0.18
(CR)− 0.74
(w
b
)− 0.05
(CaO)0.37
(SiO2)0.2
+ (t)0.0000002
(14)
D. Kakasor Ismael Jaf et al.
14. Construction and Building Materials 400 (2023) 132604
14
network containing one hidden layer with 19 neurons. The ANN
network having one hidden layer with 19 neurons with 0.1 of mo
mentum, a learning rate of 0.2, and 2000 learning time, is chosen,
Fig. 12. The ANN formula, including weights and biases, is shown in Eq.
(15). Fig. 13 shows the relation between predicted compressive strength
by the ANN having 19 neurons with the experimental compressive
strength values for both training and testing data. ANN network analysis
has an R2
of 0.987 and RMSE of 3.3 MPa when the training data are
applied. The model provides an R2
of 0.986 and RMSE of 3.52 MPa when
testing data is used. The training dataset has an error line of 10 to − 10%,
indicating that 90% of the data are laid between 0.9 and 1.1 for the ratio
of predicted compressive strength to measured compressive strength. As
well as Fig. 13 shows the variation between measured and predicted
compressive strength values plotted for different strength ranges based
on the training dataset. The highest value of R2
was found in the CS of 65
to 95 MPa.
CS = −
0.1
1 + e− β1
−
0.02
1 + e− β2
−
0.15
1 + e− β3
− ⋯
0.95
1 + e− β19
+ 0.475 (15)
No. of training dataset = 165, R2
= 0.987, RMSE = 3.3 MPa.
4.2. Effect of CaO and SiO2 content on the compressive strength of
concrete
The FQ model was used to examine the influence of cement
replacement percentage, CaO (%), and SiO2 (%) on cement-based con
crete’s compressive strength using a fly-ash modified concrete dataset.
All independent parameters were held constant, and the CR was
increased from 18 to 100%; the compressive strength grew as the cement
replacement increased. The SiO2 (%) was then increased from 30.5 to
62.54% while the CA, S, C, FA, CR, w/b, CaO, and t remained constant.
According to the model results, increasing SiO2 causes a gradual in
crease in compressive strength, while CaO (%) ranges between 0.3 and
0
20
40
60
80
100
0 20 40 60 80 100
Training Testing
Compressive Strength,σc Measured (MPa)
Compresive
Strength,
σc
Predicted
(MPa)
NLR Model (Eq.13)
Training Data = 165
R2 = 0.816
RMSE = 8.36 MPa
-30%
Error
+
3
0
%
E
r
r
o
r
Y=X
line
NLR Model (Eq.13)
Testing Data = 71
R2 = 0.47
RMSE = 9.62 MPa
Fig. 9. Comparison between measured and predicted CS using NLR model for training and testing dataset.
− 0.699 − 0.034 0.187 0.099 0.278 0.126 0.156 − 0.032 0.423 − 1.770 β1
− 0.674 − 0.024 0.141 0.107 0.324 − 0.007 0.186 0.074 0.431 − 1.815 β2
− 0.719 − 0.082 0.065 0.552 0.144 0.343 0.227 − 0.089 0.294 − 1.644 β3
− 0.9 0.062 0.136 − 0.093 0.332 0.132 − 0.098 − 0.26 0.482 − 1.673 β4
− 2.249 − 0.093 0.085 0.059 − 0.422 − 0.77 − 1.588 1.53 0.384 − 2.852 β5
− 0.787 0.044 0.099 0.052 0.353 0.371 − 0.033 − 0.262 0.355 − 1.656 CA β6
− 0.618 0.074 − 0.137 0.399 0.459 0.824 − 0.182 − 0.597 0.042 − 1.569 S β7
− 0.73 − 0.084 0.111 0.382 0.201 0.36 0.248 − 0.048 0.415 − 1.618 C β8
1.067 2.077 0.468 − 1.587 − 0.042 − 2.883 − 0.566 − 0.952 0.889 − 3.131 FA β9
− 0.406 − 0.46 − 0.089 0.705 − 0.152 − 0.003 1.294 − 0.354 − 2.561 − 1.826 × CR = β10
− 0.152 − 0.16 1.543 0.415 − 1.948 1.465 − 1.387 0.046 − 1.439 − 2.373 w/b β11
− 1.143 0.245 0.73 − 0.905 0.052 − 0.04 − 0.104 0.063 1.198 − 1.805 CaO β12
− 0.738 − 0.034 0.023 0.399 0.242 0.285 0.069 − 0.22 0.281 − 1.661 SiO2 β13
− 0.668 − 0.034 0.176 0.143 0.353 − 0.051 0.166 0.082 0.432 − 1.823 t β14
− 0.78 − 0.06 0.129 0.086 0.294 0.113 0.087 − 0.125 0.394 − 1.743 1 β15
0.151 0.712 − 1.587 2.137 0.13 1.045 − 1.958 1.349 0.004 − 3.212 β16
− 0.698 − 0.092 0.07 0.326 0.213 0.276 0.283 0.036 0.479 − 1.556 β17
− 0.714 − 0.083 0.061 0.538 0.143 0.278 0.338 0.059 0.446 − 1.637 β18
− 0.734 − 0.071 0.145 0.186 0.0.274 0.293 0.151 − 0.129 0.373 − 1.678 β19
D. Kakasor Ismael Jaf et al.
15. Construction and Building Materials 400 (2023) 132604
15
Fig. 10. Comparison between measured and predicted CS using MLR model for training and testing dataset.
Fig. 11. Selecting optimum ANN based on RMSE and MAE.
D. Kakasor Ismael Jaf et al.
16. Construction and Building Materials 400 (2023) 132604
16
Fig. 12. Optimal ANN network structures containing one hidden layer and 19 hidden neurons.
D. Kakasor Ismael Jaf et al.
17. Construction and Building Materials 400 (2023) 132604
17
Fig. 13. Comparison between measured and predicted CS using ANN model for training and testing dataset.
D. Kakasor Ismael Jaf et al.
18. Construction and Building Materials 400 (2023) 132604
18
24% but contributes a reduction above that. To determine the influence
of CaO (%), all other parameters were fixed, including CA, S, C, FA, CR,
w/b, and SiO2, and the CaO (%) varied from 0.3 to 31.9%. Concrete
compressive strength improved as the CaO (%) increased, but only when
the cement replacement was between 52 and 100% and dropped below
that. Fig. 14 depicts the performance of CaO and SiO2.
5. Evaluation of developed models
The study attempted to determine the effect of two major chemical
compositions of fly ash, CaO, and SiO2, on the compressive strength of
cement-based concrete modified with various types and amounts of fly
ash. The experiment includes forecasting concrete compressive strength
using four alternative models; FQ, NLR, MLR, and ANN. Each model
supplied a formula based on several mathematical parameters. To
evaluate the performance of each constructed model, various assessment
criteria were applied.
According to the R2
, RMSE, and MAE statistics, the ANN model has the
maximum accuracy and reliability for prediction using the training and
testing dataset. Table 3 summarizes the statistical criteria outcomes for
the created models. The ANN model has an R2
of 0.987, RMSE of 3.3 MPa,
and MAE of 2.662 MPa based on the training dataset, as well as R2
of
0.986, RMSE of 3.52 MPa, and MAE of 2.96 MPa for the testing dataset.
Furthermore, more data are along the Y = X line for the ANN model,
which has an error line of 10 to − 10% for the training dataset, referring
to the fact that 90% of the data are between 0.90 and 1.1 (predicted CS/
measured CS). The second-ranked model is the FQ model; it has an R2
of
0.94, RMSE of 4.627 MPa, and MAE of 3.496 MPa for the training
dataset, and R2
of 0.887, RMSE of 4.57 MPa, and MAE of 3.457 for the
testing dataset. The FQ model has an error line of ± 15 for the training
dataset. The NLR and MLR model have error lines of ± 30, and ± 25,
respectively, indicating the model’s poor performance. Fig. 15 compares
created models based on the testing dataset; model values are within the
± 20 and ± 30% error lines.
The training dataset for the models based on three different CS
ranges was plotted, and the RMSE values were provided. Table 4 shows
an overview of the findings. There were 63 datasets for the strength
range of 5 to 34 MPa, 78 in the range of 65 to 95 MPa, and 24 data for the
65 to 95 MPa. According to the findings, the ANN model was more
appropriate than other models for all CS ranges. The model provided an
RMSE of 3.576, 3.16, and 2.616 MPa for 5 to 34, 35 to 65, and 65 to 95
MPa, respectively.
The same models were employed to identify the ideal specimen
shape and size. The dataset for each size specimen was modeled based
on training and testing datasets. Table 5 shows RMSE values for different
models. According to the table, based on a satisfied number of data, the
cylinder specimens with dimensions of 100*200 mm have the lowest
RMSE value, 0.124 MPa, for the training dataset using the ANN model.
Although, the cylinder specimens with 150*300 mm dimensions have
the lowest value of RMSE for the testing dataset, which is 0.01 MPa.
Furthermore, the created models using the training and testing
dataset were also evaluated by comparing the OBJ function and SI
values. The highest value of SI is 0.104 for training, and the ANN model,
Fig. 16, concluded 0.074 for testing. The model also has the lowest OBJ
function compared to the other models, indicating that the model is
more accurate in forecasting the compressive strength of concrete
modified with fly ash. The model has an OBJ value of 3.09 and 1.0 MPa
based on the training and testing dataset, respectively, Fig. 17.
Although, the highest a-20 index value was observed for the MLR model;
104.4% for the training and 104.8% for the testing dataset, Fig. 18.
Fig. 14. The effect of CaO and SiO2 content of fly ash on the compressive
strength of concrete.
Table 3
Summary of performance evaluation parameters for the developed models.
Models Fig. (No) Eq. (No.) Training Testing Ranking
R2
RMSE (MPa) MAE (MPa) R2
RMSE (MPa) MAE (MPa)
FQ 8 12 0.94 4.63 3.496 0.887 4.57 3.457 2
NLR 9 13 0.816 8.36 6.698 0.47 9.62 7.522 3
MLR 10 14 0.79 9.22 7.199 0.62 8.25 6.665 4
ANN 13 15 0.987 3.3 2.662 0.986 3.52 2.96 1
Fig. 15. Variation of measured and predicted CS for developed models using
the testing dataset.
Table 4
Root mean squared error (RMSE) for the developed models in different ranges of
compressive strength.
Compressive
Strength range
(MPa)
No.
of
data
Models Best Model
Performance
FQ NLR MLR ANN
5 to 34 63 3.731 7.723 6.335 3.576 ANN
35 to 64 78 4.312 8.231 10.599 3.16 ANN
65 to 95 24 7.087 10.21 10.686 2.616 ANN
D. Kakasor Ismael Jaf et al.
19. Construction and Building Materials 400 (2023) 132604
19
Fig. 16. Comparison of developed models using (a) SI and (b) MAE.
Table 5
Root mean squared error (RMSE) of the developed models using different specimen sizes and conducting.
Dataset Sample size (mm) No. of Samples Models Best Model Performance
FQ NLR MLR ANN
Training S1(100*200) 58 3.746 4.757 5.479 1.124 ANN
S2(150*300) 30 2.363 6.592 6.008 2.512 ANN
S3(100*100*100) 74 3.663 8.48 5.428 1.94 ANN
S4(150*150*150) 3 NA 3.673 11.2 0 ANN
Testing S1(100*200) 34 3.864 6.696 5.287 1.116 ANN
S2(150*300) 12 10.179 6.592 5.196 0.01 ANN
S3(100*100*100) 24 6.108 12.186 8.377 1.94 ANN
S4(150*150*150) 1 N.A 6 3.54 0 ANN
D. Kakasor Ismael Jaf et al.
20. Construction and Building Materials 400 (2023) 132604
20
In addition, the created models were compared using residual error,
as shown in Fig. 19. The residual error value is obtained by subtracting
the expected compressive strength from the measured compressive
strength. The results show that the FQ model has the lowest error value,
ranging from − 16.58 to + 17.14 MPa. At the same time, the residual
error for the NLR, MLR, and ANN models is − 17.81 to + 24.64 MPa,
− 24.61 to + 24.85 MPa, and − 6.80 to + 8.28 MPa, respectively.
6. Sensitivity investigation
A sensitivity analysis was performed to evaluate the influence of each
independent parameter on the value of modeled dependent variable
[32,44,56,57]. To determine the sensitivity index of each parameter on
the developed model, one parameter was extracted each time for the
training data. The ANN was used for the sensitivity investigation as it
was observed as the best model for predicting the compressive strength
of concrete compared to the other conducted models. All R2
, RMSE, and
MAE values were reported for each trial. The sensitivity results are
shown in Table 6. The findings indicate that the curing time is the most
significant and influential parameter for predicting the compressive
strength of concrete, Fig. 20.
7. Conclusions
To find an accurate and reliable model to predict the compressive
strength of cement concrete modified with different fly ash types and
quantities, 236 data samples for fly ash-modified cement concrete with
different mixture proportions, CaO and SiO2 quantity of fly ash, aggre
gate content, cement content, cement replacement, water-to-binder
ratio, and curing time were collected from previous researches. Ac
cording to the collected data and the result of four different model ap
proaches, the following can be drawn:
The cement replacement percentage ranged between 18 and 100%.
The compressive strength of concrete gradually increased when the
replacement (%) was increased.
The data collected from various research studies show that CaO (%)
varied between 0.3 and 31.9%. SiO2 (%) ranged between 30.5 and
62.54%.
Increasing CaO (%) in fly ash caused an increase in compressive
strength only when the cement replacement ranged between 52 and
100%. The CS also improved with increasing SiO2 (%), where; the
CaO (%) varied between 0.3 and 24%.
Based on multiple assessment criteria such as R2
, RMSE, and MAE,
the ANN model showed the best accuracy and performance for pre
dicting the compressive strength of concrete. The model had the
highest value of R2,
which was 0.987 and 0.986 for training and
testing datasets, respectively. The lowest value of RMSE and MAE
was found as 3.3 and 2.66 for training, and 3.53 and 2.96 for testing,
respectively.
Related to other statistical tools, such as SI and OBJ, the ANN model
was first ranked. The SI and OBJ function values were 0.104 and
3.09 MPa for training and 0.074 and 1.0 MPa for the testing dataset,
respectively. Although the highest a-20 index was observed for the
MLR model, the value was 104.4% for the training dataset.
According to the shape and size of specimens, the cylinder specimens
having 100*200 mm were obtained for the best performance using
the ANN model.
Fig. 17. Comparison of developed models using the OBJ function.
99.7%
100.4%
104.4%
97.2%
101.8%
96.9%
104.8%
91.4%
0%
20%
40%
60%
80%
100%
120%
FQ NLR MLR ANN
Training Testing
Models
a-20
index
(%)
(b)
Fig. 18. Comparison of developed models using a20-index.
D. Kakasor Ismael Jaf et al.
21. Construction and Building Materials 400 (2023) 132604
21
Fig. 19. Variation between measured and predicted CS for developed models based on residual error (a) ANN and FQ, (b) ANN and NLR, and (c) ANN and MLR.
D. Kakasor Ismael Jaf et al.
22. Construction and Building Materials 400 (2023) 132604
22
Fig. 19. (continued).
Table 6
Sensitivity analysis using the ANN model applied to the training dataset.
No. Combination Removed Parameter R2
RMSE (MPa) MAE (MPa) Ranking based on RMSE and MAE
1 CA, S, C, FA, CR, w/b, CaO, SiO2, t – 0.987 3.30 2.662 –
2 S, C, FA, CR, w/b, CaO, SiO2, t CA 0.987 3.984 3.254 3
3 CA, C, FA, CR, w/b, CaO, SiO2, t S 0.989 3.045 2.497 7
4 CA, S, FA, CR, w/b, CaO, SiO2, t C 0.990 3.800 3.165 4
5 CA, S, C, CR, w/b, CaO, SiO2, t FA 0.989 3.127 2.514 6
6 CA, S, C, FA, w/b, CaO, SiO2, t CR 0.990 3.800 3.165 4
7 CA, S, C, FA, CR, CaO, SiO2, t w/b 0.976 4.424 3.446 2
8 CA, S, C, FA, CR, w/b, SiO2, t CaO 0.990 2.800 2.217 8
9 CA, S, C, FA, CR, w/b, CaO, t SiO2 0.989 3.523 2.824 5
10 CA, S, C, FA, CR, w/b, CaO, SiO2 t 0.876 10.450 8.109 1
Fig. 20. The percentage contribution of input variables in predicting compressive strength of concrete using the ANN model.
D. Kakasor Ismael Jaf et al.
23. Construction and Building Materials 400 (2023) 132604
23
The ANN model was applied to different CS ranges of concrete. The
best performance of the model was observed in the range of 35 to 64
MPa.
Sensitivity analysis illustrates the most effective parameter for pre
dicting the compressive strength of concrete: the curing time.
Using multiple models provides construction professionals with
broader information and insights. This enhances decision-making
processes by considering different perspectives and approaches,
leading to more informed and effective decisions throughout the
project lifecycle.
Employing multiple models allows for the validation and cross-
verification of results. By comparing the predictions and outcomes
of different models, construction practitioners can assess the accu
racy and reliability of the models in different scenarios, increasing
confidence in the results.
The construction industry is diverse, and no single model can address
all situations. Applying multiple models acknowledges this diversity
and provides the flexibility to choose the most appropriate model for
specific projects, materials, or contexts. It enables the construction
industry to adapt to evolving needs and challenges.
The use of multiple models encourages continuous improvement in
construction practices. By comparing and evaluating different
models, areas for improvement can be identified, leading to ad
vancements in modeling techniques, data collection, and analysis
methodologies. This fosters innovation and progress within the
industry.
Discuss the advantages of machine learning models in the context of
compressive strength prediction. These may include their ability to
capture complex relationships, handle large datasets, adapt to
different scenarios, and potentially provide better accuracy than
traditional analytical models.
Funding
This work had no finding.
CRediT authorship contribution statement
Dilshad Kakasor Ismael Jaf: . Payam Ismael Abdulrahman: .
Ahmed Salih Mohammed: . Rawaz Kurda: Investigation, Visualiza
tion. Shaker M.A. Qaidi: Validation. Panagiotis G. Asteris: Supervi
sion, Writing – original draft, Writing – review & editing.
Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Data availability
Data will be made available on request.
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