Concrete is the safest and sustainable construction material which is most widely used in the world as it provides superior fire resistance, gains strength over time and gives an extremely long service life. Unfortunately high performance concrete is undoubtedly one of the most innovative materials in construction. Its Designing involves the process of selecting suitable ingredients of concrete (water, cement, fine and aggregates and a number of additives like mineral and chemical admixture) and determining their relative amounts with the objective of producing a high performance concrete of the required, strength, durability, and workability as economically as possible. Their proportions have a high influence on the final strength of the product. These relations do not seem to follow a mathematical formula and yet their knowledge is crucial to optimize the quantities of raw materials used in the manufacture of high performance concrete. Therefore, it would be important to have a tool to numerically model such relationships, even before processing. In this aspect the main purpose of this paper is to predict the compressive strength of the high performance concrete by using classification algorithms. For building these models, training and testing using the available experimental results for 1030 specimens produced with 8 different mixture proportions are used. The result from this study suggests that weighted Support Vector Machines (wSVM) based models perform remarkably well in predicting the compressive strength of the concrete mix.
The purpose of this paper is to perform a structural optimization of a flat thermoplastic plate (tile). This task is developed computationally through the interface between an optimization algorithm and the finite element method with the goal of minimizing the equivalent stress with specified target stress of 2 MPa when applied with a load intensity of 1000N. A 300 x 300 x 20 mm thermoplastic plate was selected for the optimization, which was performed with a tool in MATLAB R2012b known as genetic algorithm accompanied with static analysis in ANSYS 15. The results produced the optimum equivalent stress (δopt) of 2.136 MPa with the optimum dimensions of 305 x 302 x 20 mm. Also, the dimensions of the plate with the optimum value of the equivalent stress were discovered to be within the lower and upper bound dimensions of the plate. The thermoplastic plate object of the optimization was a square plate of 300 x 300mm, and 20 mm thick with isotropic properties and a particular load and boundary conditions were applied on the entire plate.
Compressive Strength of Ready Mix Concrete Using Soft Computing TechniquesIJERA Editor
Ready mixed concrete (RMC) is an essential material in contemporary construction and engineering projects. Compressive strength of concrete is a major and perhaps the most important mechanical property, which is usually measured after a standard curing of 28 days. In this research work, 28-day compressive strength of Ready Mix Concrete has been estimated by using feed forward back propagation neural network, Fuzzy Logic and Adaptive Neuro Fuzzy Inference System (ANFIS) modeling. The data for the ready mixed concretes (RMC) were collected from RMC batching plant. Various models has been has been developed for different input scenarios. The compressive strength was modeled as a function of five variables, the effects of each parameter on networks were studied for Artificial Neural Network (ANN), Fuzzy Logic and ANFIS models.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
There are many parameters of concrete that
influence its strength gaining characteristics. This study is
an attempt to use the early compressive strength test result to
estimate compressive strength at different ages. Potential
utilization of the early day compressive strength result to
predict characteristic strength of normal weight concrete has
been investigated. A simple mathematical model capable of
predicting the compressive strength of concrete at any age is
proposed for both stone and local aggregate concrete. The
basic model consists of a rational polynomial equation having
only two coefficients. This study also proposes a simple reliable
relationship between the coefficient p (strength at infinite
time) with the strength values of concrete of a particular day.
This relation greatly simplifies the concrete strength
prediction model. The developed model is validated for
commonly used stone aggregate concrete and also for local
(brick) aggregate concrete. Data used in this study are collected
from some previous studies and recent experimental works.
The analysis carried with the model using different data
exhibit reliable prediction of concrete strength at different
ages (7, 14, 28 days etc.) with excellent efficiency.
Multi objective game theoretic scheduling of bag-of-tasks workflows on hybri...Nexgen Technology
Ecruitment Solutions (ECS) is one of the leading Delhi based Software Development & HR Consulting Firm, which is assessed at the level of ISO 9001:2008 standard. ECS offers an awesome project and product based solutions to many customers around the globe.
In addition, ECS has also widened its wings by the way consummating academic projects especially for the final year professional degree students in India. ECS consist of a technical team that has solved many IEEE papers and delivered world-class solutions .
Optimum designing of a transformer considering lay out constraints by penalty...INFOGAIN PUBLICATION
Optimum designing of power electrical equipment and devices play a leading role in attaining optimal performance and price of equipments in electric power industry. Optimum transformer design considering multiple constraints is acquired using optimal determination of geometric parameters of transformer with respect to its magnetic and electric properties. As it is well known, every optimization problem requires an objective function to be minimized. In this paper optimum transformer design problem comprises minimization of transformers mean core mass and its windings by satisfying multiple constraints according to transformers ratings and international standards using a penalty-based method. Hybrid big bang-big crunch algorithm is applied to solve the optimization problem and results are compared to other methods. Proposed method has provided a reliable optimization solution and has guaranteed access to a global optimum. Simulation result indicates that using the proposed algorithm, transformer parameters such as core mass, efficiency and dimensions are remarkably improved. Moreover simulation time using this algorithm is quit less in comparison to other approaches.
The purpose of this paper is to perform a structural optimization of a flat thermoplastic plate (tile). This task is developed computationally through the interface between an optimization algorithm and the finite element method with the goal of minimizing the equivalent stress with specified target stress of 2 MPa when applied with a load intensity of 1000N. A 300 x 300 x 20 mm thermoplastic plate was selected for the optimization, which was performed with a tool in MATLAB R2012b known as genetic algorithm accompanied with static analysis in ANSYS 15. The results produced the optimum equivalent stress (δopt) of 2.136 MPa with the optimum dimensions of 305 x 302 x 20 mm. Also, the dimensions of the plate with the optimum value of the equivalent stress were discovered to be within the lower and upper bound dimensions of the plate. The thermoplastic plate object of the optimization was a square plate of 300 x 300mm, and 20 mm thick with isotropic properties and a particular load and boundary conditions were applied on the entire plate.
Compressive Strength of Ready Mix Concrete Using Soft Computing TechniquesIJERA Editor
Ready mixed concrete (RMC) is an essential material in contemporary construction and engineering projects. Compressive strength of concrete is a major and perhaps the most important mechanical property, which is usually measured after a standard curing of 28 days. In this research work, 28-day compressive strength of Ready Mix Concrete has been estimated by using feed forward back propagation neural network, Fuzzy Logic and Adaptive Neuro Fuzzy Inference System (ANFIS) modeling. The data for the ready mixed concretes (RMC) were collected from RMC batching plant. Various models has been has been developed for different input scenarios. The compressive strength was modeled as a function of five variables, the effects of each parameter on networks were studied for Artificial Neural Network (ANN), Fuzzy Logic and ANFIS models.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
There are many parameters of concrete that
influence its strength gaining characteristics. This study is
an attempt to use the early compressive strength test result to
estimate compressive strength at different ages. Potential
utilization of the early day compressive strength result to
predict characteristic strength of normal weight concrete has
been investigated. A simple mathematical model capable of
predicting the compressive strength of concrete at any age is
proposed for both stone and local aggregate concrete. The
basic model consists of a rational polynomial equation having
only two coefficients. This study also proposes a simple reliable
relationship between the coefficient p (strength at infinite
time) with the strength values of concrete of a particular day.
This relation greatly simplifies the concrete strength
prediction model. The developed model is validated for
commonly used stone aggregate concrete and also for local
(brick) aggregate concrete. Data used in this study are collected
from some previous studies and recent experimental works.
The analysis carried with the model using different data
exhibit reliable prediction of concrete strength at different
ages (7, 14, 28 days etc.) with excellent efficiency.
Multi objective game theoretic scheduling of bag-of-tasks workflows on hybri...Nexgen Technology
Ecruitment Solutions (ECS) is one of the leading Delhi based Software Development & HR Consulting Firm, which is assessed at the level of ISO 9001:2008 standard. ECS offers an awesome project and product based solutions to many customers around the globe.
In addition, ECS has also widened its wings by the way consummating academic projects especially for the final year professional degree students in India. ECS consist of a technical team that has solved many IEEE papers and delivered world-class solutions .
Optimum designing of a transformer considering lay out constraints by penalty...INFOGAIN PUBLICATION
Optimum designing of power electrical equipment and devices play a leading role in attaining optimal performance and price of equipments in electric power industry. Optimum transformer design considering multiple constraints is acquired using optimal determination of geometric parameters of transformer with respect to its magnetic and electric properties. As it is well known, every optimization problem requires an objective function to be minimized. In this paper optimum transformer design problem comprises minimization of transformers mean core mass and its windings by satisfying multiple constraints according to transformers ratings and international standards using a penalty-based method. Hybrid big bang-big crunch algorithm is applied to solve the optimization problem and results are compared to other methods. Proposed method has provided a reliable optimization solution and has guaranteed access to a global optimum. Simulation result indicates that using the proposed algorithm, transformer parameters such as core mass, efficiency and dimensions are remarkably improved. Moreover simulation time using this algorithm is quit less in comparison to other approaches.
A Staffing Tool to Improve Efficiency at a Nursing DepartmentIJERA Editor
The paper suggests a staffing tool to improve efficiency at a nursing department of a local hospital. The managers consider they are understaffed and try to overwhelm the staffing deficit problem through overtime, rather than hiring additional nurses. The estimates indicate that the shortage at the hospital level corresponds to 300 full time equivalent (FTE) nurses. However, the huge amount of allocated budget for overtime becomes a concern since the deficit is not accurately estimated. Indeed, the suggested staffing tool shows that some nursing units are unnecessarily overstaffed. Moreover, the current study reveals that the real deficit is of only 215 FTE resulting in a potential saving of 28%.
Study of Adsorption Isotherm Model and Kinetics on Removal of Zinc Ion from I...IJERA Editor
The removal of Zinc (Zn) metal ion from aqueous solution by using novel bioadsornbent. The impact of beginning metal particle fixation and adsorbent measurements on the adsorption of Zinc (zn) by waste water was researched. The leftover zinc ions was then broke down utilizing Atomic Absorption Spectrophotometer (AAS) (240AA). The adsorption harmony was accomplished when zinc arrangement was 800mg/L. The rate of metal evacuation is of most prominent criticalness for building up a characteristic adsorbent-based watertreatment innovation. The greatest evacuation rate is to be 95.37%. The harmony was accomplished essentially at pH of 7 at 120 minutes and 250 rpm evacuation effectiveness of zinc at steady beginning fixation with 1.25gm measurement infers the capability of gooseberry seeds to adsorb and recoup substantial metals from watery arrangement was effectively exhibited with zinc (zn) test arrangements. The adsorption isotherm studies was done by using Langmuir, Freundlich, temkin, Hill, Jovanovich models and kinetics reaction was studied by pseudo 1st and 2 nd order kinetic reaction. The bioadsorption information fit well with the Temkin isotherm model than the other isotherm model. The kinetics 2nd order reaction was fit to this bioadsorbent than the first order kinetics. Removal of zinc ions from crackers industry waste water was found to be 84%. These outcomes have exhibited the gigantic capability of waste water as an option adsorbent for dangerous metal particles remediation in contaminated wastewater. This paper surveys and investigation the innovative parts of expulsion of zinc from the industrial waste water
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
A Staffing Tool to Improve Efficiency at a Nursing DepartmentIJERA Editor
The paper suggests a staffing tool to improve efficiency at a nursing department of a local hospital. The managers consider they are understaffed and try to overwhelm the staffing deficit problem through overtime, rather than hiring additional nurses. The estimates indicate that the shortage at the hospital level corresponds to 300 full time equivalent (FTE) nurses. However, the huge amount of allocated budget for overtime becomes a concern since the deficit is not accurately estimated. Indeed, the suggested staffing tool shows that some nursing units are unnecessarily overstaffed. Moreover, the current study reveals that the real deficit is of only 215 FTE resulting in a potential saving of 28%.
Study of Adsorption Isotherm Model and Kinetics on Removal of Zinc Ion from I...IJERA Editor
The removal of Zinc (Zn) metal ion from aqueous solution by using novel bioadsornbent. The impact of beginning metal particle fixation and adsorbent measurements on the adsorption of Zinc (zn) by waste water was researched. The leftover zinc ions was then broke down utilizing Atomic Absorption Spectrophotometer (AAS) (240AA). The adsorption harmony was accomplished when zinc arrangement was 800mg/L. The rate of metal evacuation is of most prominent criticalness for building up a characteristic adsorbent-based watertreatment innovation. The greatest evacuation rate is to be 95.37%. The harmony was accomplished essentially at pH of 7 at 120 minutes and 250 rpm evacuation effectiveness of zinc at steady beginning fixation with 1.25gm measurement infers the capability of gooseberry seeds to adsorb and recoup substantial metals from watery arrangement was effectively exhibited with zinc (zn) test arrangements. The adsorption isotherm studies was done by using Langmuir, Freundlich, temkin, Hill, Jovanovich models and kinetics reaction was studied by pseudo 1st and 2 nd order kinetic reaction. The bioadsorption information fit well with the Temkin isotherm model than the other isotherm model. The kinetics 2nd order reaction was fit to this bioadsorbent than the first order kinetics. Removal of zinc ions from crackers industry waste water was found to be 84%. These outcomes have exhibited the gigantic capability of waste water as an option adsorbent for dangerous metal particles remediation in contaminated wastewater. This paper surveys and investigation the innovative parts of expulsion of zinc from the industrial waste water
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
Shear Strength Prediction of Reinforced Concrete Shear Wall Using ANN, GMDH-N...Pouyan Fakharian
To provide lateral resistance in structures as well as buildings, there are some types of structural systems such as shear walls. The utilization of lateral loads occurs on a plate on the wall's vertical dimension. Conventionally, these sorts of loads are transferred to the wall collectors. There is a significant resistance between concrete shear walls and lateral seismic loading. To guarantee the building's seismic security, the shear strength of the walls has to be prognosticated by using models. This paper aims to predict shear strength by using Artificial Neural Network (ANN), Neural Network-Based Group Method of Data Handling (GMDH-NN), and Gene Expression Programming (GEP). The concrete's compressive strength, the yield strength of transverse reinforcement, the yield strength of vertical reinforcement, the axial load, the aspect ratio of the dimensions, the wall length, the thickness of the reinforced concrete shear wall, the transverse reinforcement ratio, and the vertical reinforcement ratio are the input parameters for the neural network model. And the shear strength of the reinforced concrete shear wall is considered as the target parameter of the ANN model. The results validate the capability of the models predicted by ANN, GMDH-NN, and GEP, which are suitable for use as a tool for predicting the shear strength of concrete shear walls with high accuracy.
PREDICTION OF COMPRESSIVE STRENGTH OF HIGH PERFORMANCE CONCRETE CONTAINING IN...IAEME Publication
This paper presents artificial neural network (ANN) based model to predict the compressive strength of concrete containing Industrial Byproducts at the age of 28, 56, 90 and 120 days. A total of 71 specimens were casted with twelve different concrete mix proportions. The experimental results are training data to construct the artificial neural network model. The data used in the multilayer feed forward neural network models are arranged in a format of ten input parameters that cover the age of specimen, cement, Fly ash, Silica fume, Metakaolin, bottom ash, sand, Coarse aggregate, water and Superplasticizer. According to these parameter in the neural network models are predicted the compressive strength values of concrete containing Industrial Byproducts. T
A new proposed approach for moment capacity estimation of ferrocement members...Pouyan Fakharian
Ferrocement composites are widely used as a novel method for many different structural purposes recently. The uniform distribution and the high surface area-to-volume ratio of the reinforcement of such composites would improve the crack-arresting mechanism. Given these properties, ferrocement is an ideal option as a replacement for some traditional structures methods. In members with axially loaded reinforced concrete ferrocement composite, it would be the best alternative to use ferrocement members. Lack of sufficient research in this approach is the cause of not well defining this field for RC structures. This study has aimed to evaluate the moment capacity of ferrocement members using the GMDH method. Mechanical and geometrical parameters including the width of specimens, total depth specimens, compressive strength of ferrocement, ultimate strength of wire mesh and volume fraction of wire mesh are considered as inputs to predict the moment capacity of ferrocement members. For evaluating this model, mean absolute error (MAE), root mean absolute error (RMAE), normalized root mean square error (NRMSE) and mean absolute percentage error (MAPE) were carried out. The results conducted that the GMDH model is significantly better than some previous models and comparable to some other methods. Moreover, a new formulation for moment capacity of ferrocement members based on GMDH approach is presented. Finally, Sensitivity analysis is operated to understand the influence of each input parameters on moment capacity of ferrocement members.
ADVANCED CIVIL ENGINEERING OPTIMIZATION BY ARTIFICIAL INTELLIGENT SYSTEMS: RE...Journal For Research
Artificial intelligence is the ability of computer systems to perform tasks which otherwise need human brain. Those tasks include visual perception, decision-making, speech recognition and translation between languages. Large amount computing resources is required to traditionally design and optimize complex civil structure in traditional method. This can be effectively eased by using intelligent systems. This paper lists out some of the methods and theories in the application of artificial intelligent systems in the field of civil engineering.
Optimizing and Predicting Compressive Strength of One-Part Geopolymer ConcreteAnoop Meshram
Developed and implemented a machine learning model to optimize and predict the compressive strength of one-part geopolymer concrete, improving concrete performance and reducing material waste.
Today, retrofitting of the old structures is important. For this purpose, determination of capacities for these buildings, which mostly are non-ductile, is a very useful tool. In this context, non-ductile RC joint in concrete structures, as one of the most important elements in these buildings are considered, and the shear capacity, especially for retrofitting goals can be very beneficial. In this paper, three famous soft computing methods including artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS) and also group method of data handling (GMDH) were used to estimating the shear capacity for this type of RC joints. A set of experimental data which were a failure in joint are collected, and first, the effective parameters were identified. Based on these parameters, predictive models are presented in detail and compare with each other. The results showed that the considered soft computing techniques are very good capabilities to determine the shear capacity.
Construction Management (CM) has to deal with a variety of uncertainties related to Time, Cost, Quality, and Safety, to name a few. Such uncertainties make the entire construction process highly unpredictable. It, therefore, falls under the purview of artificial neural networks (ANNs) in which the given hazy information can be effectively interpreted in order to arrive at meaningful conclusions. This paper reviews the application of ANNs in construction activities related to the prediction of costs, risk, and safety, tender bids, as well as labor and equipment productivity. The review suggests that the ANN’s had been highly beneficial in correctly interpreting inadequate input information. It was seen that most of the investigators used the feed forward back propagation type of the network; however, if a single ANN architecture was found to be insufficient, then hybrid modeling in association with other machine learning tools such as genetic programming and support vector machines were much useful. It was however clear that the authenticity of data and experience of the modeler are important in obtaining good results.
LINEAR REGRESSION MODEL FOR KNOWLEDGE DISCOVERY IN ENGINEERING MATERIALScscpconf
Nowadays numerous interestingness measures have been proposed to disclose the relationships
of attributes in engineering materials database. However, it is still not clear when a measure is
truly elective in large data sets. So there is a need for a logically simple, systematic and
scientific method or mathematical tool to guide designers in selecting proper materials while
designing the new materials. In this paper, linear regression model is being proposed for
measuring correlated data and predicating the continues attribute values from the large
materials database. This method helps to find the relationships between two sub properties of
mechanical property of different types of materials and helps to predict the properties of
unknown materials. The method presenting here effectively satisfies for engineering materials
database, and shows the knowledge discovery from large volume of materials database.
Studying on regression analysis suggests that data mining techniques can contribute to the
investigation on materials informatics, and for discovering the knowledge in the materials
database, which make the manufacturing industries to hoard the waste of sampling the newly
materials
The final cost of public school building projects, like other construction projects, is unknown
to the owner till the account closure. Artificial Neural Networks (ANN) is used in an attempt to
predict the final cost of two story (12 classes) school projects under lowest bid system of award
before work starts. A database of (65) school projects records completed in (2007-2012) are used to
develop and verify the ANN model. Based on expert opinions, nine out of eleven parameters are
considered to have the most significant impact on the magnitude of final cost. Hence they are used as
model inputs while the output of the model is going to be the final cost (FC). These parameters are;
accepted bid price, average bid price, estimated cost, contractor rank, supervising engineer
experience, project location, number of bidders, year of contracting, and contractual duration. It was
found that ANN has the ability to predict the final cost for school projects with very good degree of
accuracy having a coefficient of correlation (R) of (91%), and an average accuracy percentage of
(99.98%).
ARTIFICIAL NEURAL NETWORK MODELING AND OPTIMIZATION IN HONING PROCESSIAEME Publication
Determination of process parameters and maximum utility of the resources are the two main concerns while designing the manufacturing process for the products. The process parameters affect the final product shape and aesthetic look; whereas the utility refers to the output. The present study is devoted to the development of ANN models for the analysis of the honing process applied to an actual industrial component, namely the connecting rod of a motor bike. The surface quality of the honed components is measured with the help of a Talysurf Intra machine.
Similar to High-Performance Concrete Compressive Strength Prediction Based Weighted Support Vector Machines (20)
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
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Hierarchical Digital Twin of a Naval Power SystemKerry Sado
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High-Performance Concrete Compressive Strength Prediction Based Weighted Support Vector Machines
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ISSN : 2248-9622, Vol. 7, Issue 1, (Part -1) January 2017, pp.68-75
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High-Performance Concrete Compressive Strength Prediction
Based Weighted Support Vector Machines
Rguig Mustapha*, EL Aroussi Mohamed**
*(LETI Laboratory, EHTP Casablanca)
**(LETI Laboratory, EHTP Casablanca)
ABSTRACT
Concrete is the safest and sustainable construction material which is most widely used in the world as it
provides superior fire resistance, gains strength over time and gives an extremely long service life.
Unfortunately high performance concrete is undoubtedly one of the most innovative materials in construction.
Its Designing involves the process of selecting suitable ingredients of concrete (water, cement, fine and
aggregates and a number of additives like mineral and chemical admixture) and determining their relative
amounts with the objective of producing a high performance concrete of the required, strength, durability, and
workability as economically as possible. Their proportions have a high influence on the final strength of the
product. These relations do not seem to follow a mathematical formula and yet their knowledge is crucial to
optimize the quantities of raw materials used in the manufacture of high performance concrete. Therefore, it
would be important to have a tool to numerically model such relationships, even before processing. In this
aspect the main purpose of this paper is to predict the compressive strength of the high performance concrete by
using classification algorithms. For building these models, training and testing using the available experimental
results for 1030 specimens produced with 8 different mixture proportions are used. The result from this study
suggests that weighted Support Vector Machines (wSVM) based models perform remarkably well in predicting
the compressive strength of the concrete mix.
Keywords: HPC (High-Performance Concrete), Weighted Support Vector Machines, Compressive Strength
Prediction
I. INTRODUCTION
In [1] yeh et al stat that High-performance
concrete (HPC) is a new type of concrete used in the
construction industry. HPC works better in terms of
performance characteristics and uniformity
characteristics than high-strength concrete [2,3].
Prediction of High-performance concrete strength is
important for concrete construction as it gives an
idea about the time for concrete form removal,
project scheduling and quality control. The major
difference between HPC and conventional concrete
is essentially the use of mineral and chemical
admixtures [4,5]. Therefore, Apart from the four
conventional cement ingredients, Portland Cement
(PC), water, fine aggregates, and coarse aggregates,
HPC further incorporates cementitious materials, fly
ash, blast furnace slag, and a chemical admixture
[1]. These additional ingredients make HPC mix
proportion calculations and HPC behavior modeling
significantly more complicated than corresponding
processes for conventional cement. Chou et al [6]
stated that certain properties of HPC are not fully
understood since the relationship between
ingredients and concrete properties is highly
nonlinear. Therefore, traditional model of concrete
properties is inadequate for analyzing HPC
compressive strength.
There are popular methods of mix
proportion of HPC such method proposed by [7,8,9]
among other methods [10]. However, to obtain
required mix proportions of HPC most commonly
based on trial mixes as stated in relevant standards,
experience, and rules of thumb approach [11,12].
Twenty-eighth day compressive strength is
the most widely used objective function in the
mixture design. However, as pointed out previously,
the result depends on ingredient combinations and
proportions, mixing techniques and other factors that
must be controlled during manufacturing.
Kasperkicz et al. [13] stated that the introduction of
new ingredients and technologies implies that the
number of parameters for HPC mix design may
extend to 10-, 20- or even higher dimensional
decision space numbers. Waiting 28 days to get 28-
day compression strength is time consuming and not
a common practice in the construction industry.
Therefore, many researchers have worked to
establish prediction tools able to obtain an early
determination of compressive strength, ideally well
before concrete is laid down at a construction site.
Prediction of concrete compressive strength is one
area of active research in the civil engineering field,
and a considerable number of relevant studies have
been carried out over the past 30 years. Zain and
Abd [14] attempted to categorize methods into three
RESEARCH ARTICLE OPEN ACCESS
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types, i.e., those using statistical techniques,
computational modeling and artificial neural
networks. Statistical techniques represent a
conventional approach, and are used primarily to
predict conventional concrete compressive strength
by establishing linear and nonlinear regression
equations. Several approaches using regression
functions have been proposed for predicting the
concrete strength [15,16,17,18].
Traditional modeling approaches are based
on empirical relationships derived from the
experimental data. The approach starts with an
analytical equation assumption, followed by
regression analysis that employs limited
experimental data to determine an unknown
coefficient. While many regression models have
been suggested, obtaining a suitable regression
equation is not an easy task. Moreover in this
prediction effort, the early compressive strength at 6-
hour, 1-day and 3-day is usually embodied in a
prediction equation that necessitates some time delay
in prediction [19]. Furthermore, for HPC, where the
number of influencing factors is greater than for
conventional concrete, this regression model is
neither suitable nor adequate to predict compressive
strength [20]. As traditional methods handle
complex non-linear and uncertain materials (like
HPC) poorly, many researchers have sought better
prediction tools. Many studies have proposed
artificial neural networks (ANNs) and ANN
variations to map non-linear relationships among
factors of influence on 28-day HPC compressive
strength. Kasperkicz et al. [13] proposed an artificial
neural network of the fuzzy-ATMAP to predict HPC
strength properties. It was found that concrete
property prediction could be effectively modeled
using a neural system without being affected by data
complexity, incompleteness, or incoherence. In
1998, Yeh et al [1] demonstrated the superiority of
ANNs in predicting HPC compressive strength that
produced better results than regression analysis. Yeh
also showed how easily ANNs could adapt to
different numerical experiment settings in order to
review the effect on the concrete mix of each
variable proportion. Akkurt et al. [21] also noted the
use of fuzzy logic to predict concrete compressive
strength.
Within last decade, machine learning and
AI are attracting increasing attention in academic
and empirical fields for their potential application to
civil engineering problems [22]. In the field of civil
engineering, much research has focused on
prediction techniques. Therefore researchers have
explored the potential of artificial neural networks
(ANNs), a nonlinear modeling approach, in
predicting the compressive strength of the concrete
due to its ability to learn input-output relation for
any complex problem in an efficient way.
Several work reported the use of neural
network based modeling approach in predicting the
concrete strength (Sergio Lai and Mauro Serra,
1997; Yeh, 1998a, 1998b, 1999; Kasperkiewicz et
al., 1995; Sebastia et al., 2003;
Kim et al., 2004; Dias and Pooliyadda,
2001; Nehdi et al.,2001; Oh et al., 1999). In most of
the studies a back propagation neural network was
used. A neural network model requires no functional
relationship among the variables, as is the case with
most of other regression analysis techniques. A
neural network based modeling algorithm requires
setting up of different learning parameters (like
learning rate, momentum), the optimal number of
nodes in the hidden layer and the number of hidden
layers so as to have a less complex network with a
relatively better generalization capability. A large
number of training iterations may force ANN to over
train, which may affect the predicting capabilities of
the model.
Also many papers have reported on hybrid
techniques that are able to predict HPC to a high
degree of accuracy (Cheng et al., 2012; Peng et al.,
2009; Yeh, 1999).
Within last few years, another modeling
technique called Support Vector Machines (SVMs)
(Vapnik 1995) is being applied in the field of civil
engineering (Dibike et. al. 2001, Pal and Mather
2003). SVM, which represents a new AI technique,
has been shown to deliver comparable or higher
performance than traditional learning machines and
has been introduced as a powerful tool to solve
classification and regression problems [23,24]. In
[25] Gupta investigates the potential of support
vector machines in predicting the compressive
strength of high strength concrete. Radial basis
function (RBF) and polynomial kernels are used
with support vector machines. However, SVM
presents several inherent shortcomings. Firstly,
SVM is unable to provide high prediction accuracy
for either the penalty parameter (C) or kernel
parameter settings. Secondly, SVM considers all
training data points equally in order to establish the
decision surface. Therefore, Ling and Wang [26]
proposed a modified version of SVM, known as
fuzzy SVM (FSVM) or weighted SVM (wSVM), to
weight all training data points in order to allow
different input points to contribute differently to the
learning decision surface. The objective of present
study is to examine the potential of support vector
machines (wSVMs) for predicting the compressive
strength of high strength concrete.
We evaluate the effectiveness of the
proposed method using an experimental database
originally generated by Yeh [27] and posted to the
University of California, Irvine machine learning
repository website. To verify and validate the
proposed system, wSVM performance was
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compared against original SVMs and has shown
better recognition performance.
The rest of this paper is organized as
follows. A Brief introduction to SVM and weighted
SVMs is introduced in section 2. Experimental
results are presented and discussed in Section 3.
Finally, conclusions are stated in the last section.
II. BRIEF INTRODUCTION TO SVM
AND WEIGHTED SVMS
1. SVM
This section addresses support vector
machines (SVM), the classifier used in this
dissertation for the detection of concrete surface
cracks.
An SVM classifier, just like other learning
algorithms, is composed by training and testing
stages [28]. In the training stage the selected features
are extracted and typically mapped into a higher
dimensional space in order to efficiently separate
crack features from non-crack features. Since the
ground-truth of the training set is supplied, the
features that correspond to cracks and to non-cracks
can be determined. Then, as illustrated in Fig.1,
SVM selects the set of points in each class (support
vectors) that are the nearest to the other class and
through them computes a hyperplane that separates
the two classes, being as far as possible from the
support vectors. This hyperplane is often call
maximum-margin hyperplane and makes SVM
robust.
Once the system has been trained, the
following phase is the testing stage. In this stage
each testing sample is classified as belonging to one
of the two pattern classes. For that the testing set
features are mapped into the same dimensional space
produced in the training stage and, according to the
hyperplane side they fall, the corresponding pattern
class is attributed. Finally, the classifier accuracy
can be evaluated by comparison against a set of
manually labeled data.
Fig. 1. SVM feature space example that selects the
support vectors to separate the two pattern classes
through a hyperplane.
The example illustrated in Fig.8is very
simple and there is no need to map the extracted
features into a higher dimensional space since they
can be easily separated by a hyperplane. However
the typical case is much more complex and the two
classes are often mixed, being necessary to map the
features to separate better the two classes. Note that
in Fig.1 several different hyperplanes could separate
the two classes. However the hyperplane computed,
tries to be as far as possible from the support
vectors.
Given a training set of instance-label pairs
{xi,yi}, i=1,...,l where xi ∈ R and y ∈ {-1,1}1
, the
support vector machines require the solution of the
following optimization problem:
(1)
Subject to:
Here training vectors xi are mapped into a
higher (maybe infinite) dimensional space by the
function Φ. Then SVM finds a linear separating
hyper plane with the maximal margin in this higher
dimensional space. C > 0 is the penalty parameter of
the error term. We can define, K(xi,xj) =
Φ(xi)TΦ(xj) called the kernel function. Though new
kernels are being proposed by researchers, the most
common four basic kernels are: Linear, Polynomial,
Radial basis function (RBF) and Sigmoid.
Linear: K(xi,xj) = xiT
xj (2)
Polynomial :
K(xi,xj) = (ϒxi xj+r)d
,ϒ>0 (3)
Radial basis function (RBF) :
K(xi,xj) = exp(-ϒ||xi - xj||2
),ϒ>0 (4)
Sigmoid : K(xi,xj) = tanh (ϒxiT
xj +r) (5)
Where ϒ, r and d correspond to kernel parameters
that can be defined or estimated.
2. Weighted SVMS
The weighted support vector machine
(WSVM) is a SVM adaptation to the cost sensitive
learning framework. This supervised learning
technique has been successfully applicable for
imbalanced classification.
The term ―weighted support vector
machines‖ (wSVMs) was proposed by Fan and
Ramamo hanarao [29] as a synonym for Fuzzy
Support Vector Machines (FSVMs) to draw
attention to the effective weighting of fuzzy
memberships at each FSVM training point.
Fan and Ramamo hanarao [29] stated that
different input vectors make different contributions
to the learning of decision surface. Thus, the
important issue in training weighted SVMs is how to
develop a reliable weighting model to reflect the true
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noise distribution in the training data. Fan and
Ramamo hanarao [29] developed emerging patterns
(EPs) to weight the training data. Lin and Wang [30]
developed FSVMs to enhance support vector
machine (SVM) abilities to reduce the effects of
outliers and noise in data points. While SVMs a
recent AI paradigm developed by Vapnik [30] that
has been used in a wide range of applications, treat
all training points of a given class uniformly,
training points in many real world applications bear
different importance weightings for classification on
purposes. To solve this problem, Lin and Wang [31]
applied a fuzzy member to each SVM input point,
thus allowing different input points to contribute
differently to the learning decision surface. In such
time series prediction problems, older training points
are associated with lower weights, so that the effect
of older training points is reduced when the
regression function is optimized.
In sequential learning and inference
methods such as time series problems, where a point
from the recent past may be given greater weight
than a point from further in the past, function of
timeti can be selected as the weighted SVM scheme.
Lin and Wang [31] proposed three time functions,
linear, quadratic, and exponential, as shown in Eqs.
(6)–(8). Those three time functions were used by
Khemchandaniet al. [32] on financial time series
forecasting problems, who demonstrated their
abilities to bring about better results than SVM.
(6)
(7)
(8)
However, as the wSVM was developed
from SVM, it presents the user with similar
problems. Schlkopf and Smola (2002) expressed that
SVM bandwidth and penalty parameter C, which
determines the trade-off between margin
maximization and violation error minimization,
represent an issue that requires attention and
handling. Another point of concern is the setting of
kernel parameters, such as gamma (γ), on the radial
basis function, which must also be set properly to
improve prediction accuracy. In addition, using
wSVM requires users to set a further parameter, i.e.,
weighting data parameterσ. Therefore, three
different parameters must be optimized, including
the penalty parameter (C), kernel parameter (i.e.γ, if
the RBF kernel is employed), and σ. To overcome
this challenge, an optimization technique (e.g.,
fmGA) may be used to identify best parameters
simultaneously [24]
III. EXPERIMENTAL RESULTS AND
DISCUSSION
1. Database
This section verifies and validates the
performance of our system using wSVM in
predicting HPC compressive strength. The model
proposed herein predicts the compressive strength of
HPC using an experimental database originally
collected by Yeh [13] and furnished from various
university research labs, which was posted to the
University of California, Irvine machine learning
repository website. The database includes a total of
1030 concrete samples and covers 9 attributes, 8 of
which are quantitative input variables and 1 of which
is an output variable. Each instance includes the
amount of cements, fly ash, blast furnace slag, water,
superplasticizers, coarse aggregate, fine aggregate,
age of testing and the compressive strength (in
MPa).
Table 2 shows the general details of the
nine attributes used in this study. However, the
database often contains unexpected inaccuracies
[24], as for instance, the class of fly ash may not be
indicated.
Another problem is related to
superplasticizer as chemical admixture produced by
different manufactures which may have different
chemical compositions [6,20]. Moreover, Chou et al.
[6] identified that such inaccuracies induce another
difficulty related to the compressive strength which
can be classified into a specific class such as high or
low concrete compressive strength.
The HPC database often contains
inaccuracies due to mixing proportions, mixing
techniques and ingredient characteristics (e.g.,
varying degrees offinesses, classes offly ash, and
types of superplasticizer). Such makes prediction of
HPC compressive strength a highly uncertain task.
Also HPC databases contains compressive strength
measures representing 14 different testing ages
ranging from 1 day to 365 days as shown in Table 3)
2. Experiment Setup
The experiments were carried out in Matlab
(R2012a), on a 64-bit, PC with an i5 microprocessor
with 4 cores, 4 GB of RAM and a hard disk of 250
GB.
To develop the HPC compressive strength
prediction system, the1030 samples were divided
randomly into training and testing sets 90% or 927
samples were assigned to the training set and the
remainder, 10% or 103 samples, were assigned to
the testing set. As the wSVM was to be compared
against SVM result accuracies, SVM parameter
setting procedures followed previous researcher
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suggestions and settings. In this study, as suggested
by Hsu et al. [33] parameter settings for SVMs,
herein C and γ were set to 1 and 1/k respectively,
with k representing number of input patterns. This
study employed four performance measures, namely
root mean square error (RMSE), coefficient
correlation (r), coefficient of determination (R2) and
mean absolute error (MAE) to verify and validate
the accuracy of the proposed system and other AI
models. Table 4 shows RSME, r, R2, and MAE
results of the proposed wSVM system (linear,
quadratic and exponential time series functions)
compared against SVM.
3. Results and discussion
This section presents the results of
comparing wSVM to other prediction technique
including such as SVM.
The performance of the proposed system
was evaluated by three statistical measures namely
root mean square error (RMSE), coefficient of
determination (R2) and mean absolute error (MAE)
to verify and validate the accuracy of the proposed
method
(9)
(10)
Here, Ai = Actual value, M=mean of actual
value; Pi = Predicted value; n = number of data (1, 2,
3 …).
Table 4 shows RSME, R2, and MAE
results of the proposed system using wSVM with
polynomial and RBF kernel functions compared
against the other AI systems (SVM and BPN)
Based on the three different evaluation
methods for both training and testing datasets,
wSVM provided the best satisfactory result. In
comparing SVM based on RSME and MAE, SVM
performed slightly better than wSVM, but only on
training data (not on the testing data set). However,
in terms of coefficient of determination (R2) for the
training data set, wSVM is comparable to SVM.
Fig.2 presents scatter diagrams of SVMs and wSVM
using polynomial and RBF (Radial basis Function)
kernels for the training data set. Better results were
achieved by wSVM in terms of predicting testing
dataset results, which shows that the wSVM training
data learning process provides a prediction model
superior to SVM. Such confirms that wSVM
(polynomial ann RBF kernels) delivers comparable
or higher performance than SVM.
This better learning ability demonstrates
wSVM ability to cope with uncertain characteristics
inherent in HPC databases. Moreover wSVM model
is also able to map the complex relationship between
input and output variables as well as manage time
series characteristics inherent to HPC databases.
While wSVM employed three different time series
functions (linear, quadratic and exponential) to
weigh data points, one preferable time series
function should be chosen based on performance
achieved by each time series function, both in the
training and testing datasets. As shown in Table 4,
the wSVM using quadratic functions generally
provides slightly better performance, especially on
the testing data set, in comparison with the wSVM
using polynomial and RBF kernel functions.
However, it should be noted that differences in
performance obtained between the two kernel
functions were not significant. This shows that there
remains room for improvement to find a better time
series function to predict HPC compressive strength.
The proposed model, wSVM, offers the potential to
predict HPC compressive strength. The practitioners
can obtain early, applicable and reliable prediction
of concrete compressive strength for pre-design and
quality control, as waiting 28 days to get 28-day
compressive strength or later-age compressive
strength is time-consuming. In accordance with Zain
and Abd [34] and Chou et al. [35] the rapid
prediction would enable the adjustment of mix
proportion to avoid situation where concrete does
not reach the required compressive strength, which
would save time and construction costs.
Table I
HPC database: input and output variables
Table II
HPC database Examples
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Table III
Comparison of results using SVMs and weighted
SVMs (Polynomial and RBF kernels)
Fig. 2
Scatter diagrams of SVMs and wSVM (Polynomial
and RBF kernels)
IV. CONCLUSION
This paper proposed wSVM as a hybrid AI
system to predict HPC compressive strength, a
mechanical property critical to measuring HPC
quality. wSVM was developed by fusing FL.
Therefore wSVMs was used to address uncertainties
inherent in HPC and to deal with complex
relationships related to fuzzy input–output mapping
data in the HPC database (e.g., compressive
strength) with regard to testing age. In comparison
with SVMs, the accuracy of the proposed wSVM
was significantly better for different evaluation
measurements.
Such results demonstrate the superior
ability of wSVM to manage 1) time series data
characteristics inherent in HPC experimental data, 2)
complex relationships between input and output
variables, and 3) uncertainties inherent in HPC
databases. Therefore, wSVM offers strong potential
as a predictive tool for HPC compressive strength.
REFERENCES
[1]. I.C. Yeh, Modelling of strength of high-
performance concrete using artificial neural
networks, Cement and Concrete Research 28
(12) (1998) 1797–1808.
[2]. Mousavi, S. M., Aminian, P., Gandomi, A.
H., Alavi, A. H., & Bolandi, H. (2012). A
new predictive model for compressive
strength of HPC using gene expression
programming. Advances in Engineering
Software, 45(1), 105-114.
[3]. Peng, C. H., Yeh, I. C., & Lien, L. C. (2010).
Building strength models for high-
performance concrete at different ages using
genetic operation trees, nonlinear regression,
and neural networks. Engineering with
Computers, 26(1), 61-73.
[4]. Bharatkumar, B. H., Narayanan, R.,
Raghuprasad, B. K., & Ramachandramurthy,
D. S. (2001). Mix proportioning of high
performance concrete. Cement and concrete
composites, 23(1), 71-80
[5]. C.H. Lim, Y.S. Yoon, J.H. Kim, Genetic
algorithm in mix proportioning ofhigh-
performance concrete, Cement and Concrete
Research 34 (2004) 409–420.
[6]. Chou, J. S., & Tsai, C. F. (2012). Concrete
compressive strength analysis using a
combined classification and regression
technique. Automation in Construction, 24,
52-60.
[7]. ACI Committee 363. (1984, July). State of the
Art Report on High-Strength Concrete. In ACI
Journal Proceedings (Vol. 81, No. 4). ACI.
[8]. P.C. Aïtcin, High Performance Concrete,
E&FN SPON, New York, 1998.
7. Rguig Mustapha. Int. Journal of Engineering Research and Application www.ijera.com
ISSN : 2248-9622, Vol. 7, Issue 1, (Part -1) January 2017, pp.68-75
www.ijera.com 74 | P a g e
[9]. Mehta, P. K., & Aitcin, P. C. (1990).
Microstructural basis of selection of materials
and mix proportions for high-strength
concrete. ACI Special Publication, 121.
[10]. A.I. Laskar, S. Talukdar, A new mix design
method for high performance concrete, Asian
Journal of Civil Engineering (Building and
Housing) 9 (1) (2008) 15–23.
[11]. Arhras, G., & Foo, H. C. (1994). A
knowledge-based system for selecting
proportions for normal concrete. Expert
systems with applications, 7(2), 323-335.
[12]. C.H. Lim, Y.S. Yoon, J.H. Kim, Genetic
algorithm in mix proportioning ofhigh-
performance concrete, Cement and Concrete
Research 34 (2004) 409–420.
[13]. J. Kasperkiewicz, J. Racz, A. Dubrawski,
HPC strength prediction using artificial neural
network, Journal of Computing in Civil
Engineering 9 (4) (1995)279–284.
[14]. M.F.M. Zain, S.M. Abd, Multiple regression
model for compressive strength prediction of
high performance concrete, Journal of
Applied Science 9 (1) (2009)155–160.
[15]. Alilou, V. K., & Teshnehlab, M. (2010).
Prediction of 28-day compressive strength of
concrete on the third day using artificial
neural networks. International Journal of
Engineering, 3(6), 565-576.
[16]. ACI Committee 211, 1991 ―Standard Practice
for selecting properties for normal, heavy
weight concrete‖, (ACI211.1-91) American
Concrete Institute, Detroit.Chengju G., 1989.
―Maturity of concrete: Method for predicting
early stage strength‖. ACI Materials Journal,
Vol.86 (4), pp. 341–353.
[17]. Oluokun F.A., Burdette E.G., Harold
Deatherage J., 1990. ―Early-age concrete
strength prediction by maturity — another
look‖. ACI Materials Journal, Vol. 87(6), pp.
565–572.
[18]. Popovics S., 1998. ―History of a mathematical
model for strength development of Portland
cement concrete‖. ACI Materials Journal,
Vol. 95(5), pp. 593–600.
[19]. H.G. Ni, J.Z. Wang, Prediction of
compressive strength of concrete by neural
networks, Cement and Concrete Research 30
(2000) 1245–1250.
[20]. I.C. Yeh, L.C. Lien, Knowledge discovery of
concrete material using genetic operation
trees, Expert System with Applications 36
(2009) 5807–5812.
[21]. S. Akkurt, S. Ozdemir, G. Tayfur, B. Akyol,
The use of GA-ANNs in the modeling of
compressive strength of cement mortar,
Cement and Concrete Research 33(2003)
973–979.
[22]. Mousavi, S. M., Aminian, P., Gandomi, A.
H., Alavi, A. H., & Bolandi, H. (2012). A
new predictive model for compressive
strength of HPC using gene expression
programming. Advances in Engineering
Software, 45(1), 105-114.
[23]. BURGES, Christopher JC. A tutorial on
support vector machines for pattern
recognition. Data mining and knowledge
discovery, 1998, vol. 2, no 2, p. 121-167
[24]. Cheng, M. Y., & Wu, Y. W. (2009).
Evolutionary support vector machine
inference system for construction
management. Automation in Construction,
18(5), 597-604.
[25]. GUPTA, S. M. Support vector machines
based modelling of concrete strength. World
Academy of Science, Engineering and
Technology, 2007, vol. 36, p. 305-311.
[26]. Lin, C. F., & Wang, S. D. (2002). Fuzzy
support vector machines. Neural Networks,
IEEE Transactions on, 13(2), 464-471.
[27]. I.C. Yeh, Modelling of strength of high-
performance concrete using artificial neural
networks, Cement and Concrete Research 28
(12) (1998) 1797– 1808.
[28]. Hutchinson, T. C., & Chen, Z. (2006).
Improved image analysis for evaluating
concrete damage. Journal of Computing in
Civil Engineering.
[29]. Fan, H., & Ramamohanarao, K. (2005, July).
A weighting scheme based on emerging
patterns for weighted support vector
machines. In Granular Computing, 2005
IEEE International Conference on (Vol. 2,
pp. 435-440). IEEE.
[30]. Vapnik, V. (2013). The nature of statistical
learning theory. Springer Science & Business
Media.
[31]. C.F. Lin, S.D. Wang, Fuzzy support vector
machines, IEEE Transactions on Neural
Networks 13 (2) (2002) 464–471.
[32]. Khemchandani, R., & Chandra, S. (2009).
Regularized least squares fuzzy support
vector regression for financial time series
forecasting. Expert Systems with Applications,
36(1), 132-138.
[33]. Hsu, C. W., Chang, C. C., & Lin, C. J. (2003).
A practical guide to support vector
classification.
[34]. Zain, M. F. M., & Abd, S. M. (2009).
Multiple regression model for compressive
strength prediction of high performance
concrete. Journal of applied sciences, 9(1),
155-160.
[35]. Cheng, M. Y., Chou, J. S., Roy, A. F., & Wu,
Y. W. (2012). High-performance concrete
compressive strength prediction using time-
8. Rguig Mustapha. Int. Journal of Engineering Research and Application www.ijera.com
ISSN : 2248-9622, Vol. 7, Issue 1, (Part -1) January 2017, pp.68-75
www.ijera.com 75 | P a g e
weighted evolutionary fuzzy support vector
machines inference model. Automation in
Construction, 28, 106-115.
Authors’ information
1
LETI Laboratory, EHTP Casablanca
Rguig Mustapha received the Master’s
degree (DEA) from Louis Pasteur University and
PHD degree in Civil Ingineering from Nantes
University in 2001 and 2005, respectively. He is
currently a professor at the Department of civil
engineering, EHTP Casablanca.
Mohamed El AROUSSI received his
Bachelors degree (License es Sciences) in science
computing automatic electronic electrotechnic, his
Masters degree (DESA) in computer science and
telecommunication from the Faculty of Science,
University Mohamed V-Agdal, Rabat, Morocco, in
2004 and his Ph.D. degree in the University of
Mohammed V-Agdal, Rabat, Morocco, in 2009. He
is currently a professor at the Department of
electrical engineering, EHTP Casablanca.