Mix design and mechanical properties of self compacting light weight concreteYahaya Hassan Labaran
A presentation based on a research paper review assignment
A.A. Maghsoudi1, Sh. Mohamadpour2, M. Maghsoudi, Mix design and mechanical properties of self compacting light
weight concrete:International Journal of civil Engineering, Vol 9, No 3. september 2011
Experimental studies in Ultrasonic Pulse Velocity of rollerJoel 'almeida
This paper presents the experimental investigation results of Ultrasonic Pulse Velocity (UPV) tests conducted on roller compacted
concrete pavement (RCCP) material containing Class F fly ash of as mineral admixture. River sand, M-sand and combination of Msand
and River sand are used as fine aggregate in this experimental work. Three types of fly ash roller compacted concrete mixes are
prepared using above three types of fine aggregates and they are designated as Series A (River sand), Series B (manufactured sand)
and Series C (combination of River sand and M-sand). In each series the fly ash content in place of cement is varied from 0% to
60%. In each series and for different ages of curing (i.e 3, 7, 28 and 90 days) forty two cube specimens are cast and tested for compressive
strength and UPV. The UPV results of fly ash containing roller compacted concrete pavement (FRCCP) show lower values at all ages
from 3 days to 90 days in comparison with control mix concrete (0% fly ash) in all mixes. However, it is also observed that Series B and C
mixes containing fly ash show better results in UPV values, compressive strength and Dynamic Elastic Modulus in comparison to Series
A mixes with fly ash. Relationships between compressive strength of FRCCP and UPV and Dynamic Elastic Modulus are proposed for
all series mixes. A new empirical equation is proposed to determine the Dynamic Elastic Modulus of FRCCP.
This manuscript investigate the quality of concrete using non-destructive in-situ testing.
The in-situ testing is a process by which different test are carried out such as rebound hammer, ultrasonic pulse veloc-ity, initial surface absorption test and fig air, to determine the in-situ strength, durability and deterioration, air permeability, concrete quality control and performance. Additionally, the quality of concrete was researched using test methods with experimental results. Moreover, this research has found that (1) the increase in w/c ra-tio leads to a decrease in compressive strength and ultrasonic pulse velocity. Thus, lower w/c ratio gives a bet-ter concrete strength in terms of quality, (2) the quicker the ultrasonic pulse travels through concrete indicates that the concrete is denser, therefore, better quality, (3) the lower initial surface absorption value indicates a better concrete with respect to porosity and (4) the w/c ratio plays an important role in the strength and per-meability of concrete.
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.
Mix design and mechanical properties of self compacting light weight concreteYahaya Hassan Labaran
A presentation based on a research paper review assignment
A.A. Maghsoudi1, Sh. Mohamadpour2, M. Maghsoudi, Mix design and mechanical properties of self compacting light
weight concrete:International Journal of civil Engineering, Vol 9, No 3. september 2011
Experimental studies in Ultrasonic Pulse Velocity of rollerJoel 'almeida
This paper presents the experimental investigation results of Ultrasonic Pulse Velocity (UPV) tests conducted on roller compacted
concrete pavement (RCCP) material containing Class F fly ash of as mineral admixture. River sand, M-sand and combination of Msand
and River sand are used as fine aggregate in this experimental work. Three types of fly ash roller compacted concrete mixes are
prepared using above three types of fine aggregates and they are designated as Series A (River sand), Series B (manufactured sand)
and Series C (combination of River sand and M-sand). In each series the fly ash content in place of cement is varied from 0% to
60%. In each series and for different ages of curing (i.e 3, 7, 28 and 90 days) forty two cube specimens are cast and tested for compressive
strength and UPV. The UPV results of fly ash containing roller compacted concrete pavement (FRCCP) show lower values at all ages
from 3 days to 90 days in comparison with control mix concrete (0% fly ash) in all mixes. However, it is also observed that Series B and C
mixes containing fly ash show better results in UPV values, compressive strength and Dynamic Elastic Modulus in comparison to Series
A mixes with fly ash. Relationships between compressive strength of FRCCP and UPV and Dynamic Elastic Modulus are proposed for
all series mixes. A new empirical equation is proposed to determine the Dynamic Elastic Modulus of FRCCP.
This manuscript investigate the quality of concrete using non-destructive in-situ testing.
The in-situ testing is a process by which different test are carried out such as rebound hammer, ultrasonic pulse veloc-ity, initial surface absorption test and fig air, to determine the in-situ strength, durability and deterioration, air permeability, concrete quality control and performance. Additionally, the quality of concrete was researched using test methods with experimental results. Moreover, this research has found that (1) the increase in w/c ra-tio leads to a decrease in compressive strength and ultrasonic pulse velocity. Thus, lower w/c ratio gives a bet-ter concrete strength in terms of quality, (2) the quicker the ultrasonic pulse travels through concrete indicates that the concrete is denser, therefore, better quality, (3) the lower initial surface absorption value indicates a better concrete with respect to porosity and (4) the w/c ratio plays an important role in the strength and per-meability of concrete.
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.
Destructive and Non- Destructive Testing for Concrete in Sudan - A Comparativ...iosrjce
IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of mechanical and civil engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in mechanical and civil engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Investigation on fine aggregate by broken tiles in concreteIJARIIT
This examination manages the effect on the concrete by the partial replacement of fine aggregate by ceramic
aggregate. Studies were done on a concrete, with various replacement. The impacts of various replacements 0, 10, 20, 30&40
percent of the waste ceramic tile by weight of concrete with M-30 review. At last, it was reasoned that all the strength qualities
(compressive strength, flexural strength & split tensile strength) of concrete increases with the various replacements.
A Proposed Equation for Elastic Modulus of High-Strength Concrete Using Local...IJERA Editor
There several of equations to determine the modulus of elasticity by codes of practice and researchers. They
differ in the form of the equations and their parameter functions. Many codes and researches advise engineers of
the dependence of the modulus of elasticity on the aggregate type, size and shape; and, hence, it is wise to
determine the concrete properties for the specified mix from the trial batches. This paper considers the Iraqi
aggregates used in producing the HSC to develop and equation for prediction the modulus of elasticity for HSC.
Modulus of elasticity of high strength concrete using Iraqi aggregate with a wide range of 41 to 83.3 MPa has
been studied and by analyzing 69 tests from the available literature. An empirical equation has been proposed
for prediction of modulus elasticity presents the local aggregate in Iraq. The predicted values are compared with
the predictions by codes of practice like ACI 318-02, EC2-02 and a practical equation by Noguchi et al. It has
been found that there are differences in the predictions between them and the proposed equation. The ACI
overestimates the modulus of elasticity for these tests and 80% of the tests are below it, while EC2 values are
over conservative as they are below 78% of test values. The proposed equation is lie between the two codes. The
prediction by Noguchi et al. showed better results as they are very close to the proposed equation.
Ultimate Behavior of Lightweight High Strength Concrete Filled Steel Tube (LW...IOSR Journals
Strength and ductility of concrete members can be significantly improved with lateral confinement, usually achieved by using a steel tube casing. The concrete confinement can be utilized to make bridge lighter and have longer spans. In addition, a significant portion of the load carried concrete bridge girders is the self-weight of the girders and deck. If all or part of the girder and deck can be made using high strength lightweight concretes, there is a potential for appreciable economic savings since the self-weight could be reduced by as much as 15-20%. The study described herein investigates the static nonlinear behavior of lightweight high strength concrete filled steel tube (LWHCFST) bridges up to failure. The current study had two specific goals. The first was to experimentally determine the static modulus of elasticity of confined high strength lightweight concrete mixture. The second was to develop a nonlinear finite element computer program to study the ultimate behavior of a filled tube (LWHCFST) example bridge. The nonlinear stress-strain behavior of confined high strength lightweight concrete is evaluated experimentally by the authors and is used to help establish a comparison between the ultimate behavior of the bridge using confined normal weight concrete and confined high strength lightweight concrete. The ultimate strength of the bridge is related to the occurrence of an equivalent failure mechanism. The study indicated that the use of (LWHCFST) is beneficial for extending bridge girder lengths
Destructive and Non- Destructive Testing for Concrete in Sudan - A Comparativ...iosrjce
IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of mechanical and civil engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in mechanical and civil engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Investigation on fine aggregate by broken tiles in concreteIJARIIT
This examination manages the effect on the concrete by the partial replacement of fine aggregate by ceramic
aggregate. Studies were done on a concrete, with various replacement. The impacts of various replacements 0, 10, 20, 30&40
percent of the waste ceramic tile by weight of concrete with M-30 review. At last, it was reasoned that all the strength qualities
(compressive strength, flexural strength & split tensile strength) of concrete increases with the various replacements.
A Proposed Equation for Elastic Modulus of High-Strength Concrete Using Local...IJERA Editor
There several of equations to determine the modulus of elasticity by codes of practice and researchers. They
differ in the form of the equations and their parameter functions. Many codes and researches advise engineers of
the dependence of the modulus of elasticity on the aggregate type, size and shape; and, hence, it is wise to
determine the concrete properties for the specified mix from the trial batches. This paper considers the Iraqi
aggregates used in producing the HSC to develop and equation for prediction the modulus of elasticity for HSC.
Modulus of elasticity of high strength concrete using Iraqi aggregate with a wide range of 41 to 83.3 MPa has
been studied and by analyzing 69 tests from the available literature. An empirical equation has been proposed
for prediction of modulus elasticity presents the local aggregate in Iraq. The predicted values are compared with
the predictions by codes of practice like ACI 318-02, EC2-02 and a practical equation by Noguchi et al. It has
been found that there are differences in the predictions between them and the proposed equation. The ACI
overestimates the modulus of elasticity for these tests and 80% of the tests are below it, while EC2 values are
over conservative as they are below 78% of test values. The proposed equation is lie between the two codes. The
prediction by Noguchi et al. showed better results as they are very close to the proposed equation.
Ultimate Behavior of Lightweight High Strength Concrete Filled Steel Tube (LW...IOSR Journals
Strength and ductility of concrete members can be significantly improved with lateral confinement, usually achieved by using a steel tube casing. The concrete confinement can be utilized to make bridge lighter and have longer spans. In addition, a significant portion of the load carried concrete bridge girders is the self-weight of the girders and deck. If all or part of the girder and deck can be made using high strength lightweight concretes, there is a potential for appreciable economic savings since the self-weight could be reduced by as much as 15-20%. The study described herein investigates the static nonlinear behavior of lightweight high strength concrete filled steel tube (LWHCFST) bridges up to failure. The current study had two specific goals. The first was to experimentally determine the static modulus of elasticity of confined high strength lightweight concrete mixture. The second was to develop a nonlinear finite element computer program to study the ultimate behavior of a filled tube (LWHCFST) example bridge. The nonlinear stress-strain behavior of confined high strength lightweight concrete is evaluated experimentally by the authors and is used to help establish a comparison between the ultimate behavior of the bridge using confined normal weight concrete and confined high strength lightweight concrete. The ultimate strength of the bridge is related to the occurrence of an equivalent failure mechanism. The study indicated that the use of (LWHCFST) is beneficial for extending bridge girder lengths
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
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.
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.
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.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
1. Hyperspectral Imaging Features for Mortar Classification and Compressive Strength
1
Assessment
2
Liang Fan 1, Ming Fan 2, Abdullah Alhaj 1, Genda Chen 1,* and Hongyan Ma 1
3
1
Department of Civil, Architectural and Environmental Engineering, Missouri University
4
of Science and Technology, Rolla, MO 65401, United States; lf7h2@mst.edu;
5
ahanbc@mst.edu; mahon@mst.edu
6
2
Department of Mining and Minerals Engineering, Virginia Polytechnic Institute and
7
State University, Blacksburg, VA 24060; mingfan@vt.edu
8
* Correspondence: gchen@mst.edu; Tel.: +01-573-341-4462
9
Abstract: In this study, hyperspectral imagery with two computational algorithms are
10
proposed to classify the type of mortar and assess the in-situ strength of fresh mortar in near
11
real time. Each scanning on a mortar surface includes 30 spatial pixels selected for analysis,
12
each assigned with a light reflectance spectrum over 400 - 2500 nm. Three groups of mortar
13
samples with a water-to-cement (W/C) ratio of 0.6, 0.5 and 0.4, respectively, were cast and
14
scanned from Day 1 to 14 of curing. Reflectance data at a wavelength range of 1920 nm to
15
1980 nm, associated with the O-H chemical bond, were extracted and averaged to classify
16
the different mortar types with K-Nearest Neighbors (KNN) and Support Vector Machine
17
(SVM) algorithms and to predict their compressive strength from a regression equation. The
18
results showed that the average reflectance increased with time due to water molecules
19
reaction during curing process. The KNN classification model with K=5 had a prediction
20
accuracy of 70% to 75%, and the SVM classification model with C=1000 and σ=10 showed
21
a prediction accuracy of approximately 90%. Therefore, the SVM classification algorithm
22
is recommended for use in mortar classification. The compressive strength is well correlated
23
with the average reflectance with a coefficient of over 0.98.
24
Keywords: Hyperspectral imaging; W/C ratio; reflectance; KNN; SVM; compressive
25
strength
26
27
1. Introduction
28
Concrete is a mixture of aggregate, sand, cement and water in a certain proportion. The
29
cement and water together, referred to as cement paste, hardens through hydration reactions
30
and binds the aggregate and sand to achieve the strength of concrete in a curing process over
31
time [1]. Concrete hydration process starts immediately after concrete casting. Cement reacts
32
2. with water to generate hydration products like C-S-H gel and calcium hydroxide. The
33
hydration products grow, interconnect, and bond aggregate and sand. Concrete is formless
34
and shaped to various forms of interest, when newly mixed, and durable, when cured and
35
hardened. In the construction of highway pavements, ACI Code 301-72 requires a minimum
36
of curing period for concrete pavement to ensure that the constructed roadway is safe to
37
traffic without damage [2]. In the repair and resurface of existing roadways and their
38
transportation network in an urban environment, it is imperative to determine the early-age
39
strength of concrete pavements so that the impact of roadway construction on traffic is
40
minimized.
41
Coring and pullout test are two of the conventional approaches that have been used for
42
on-site evaluation of the compressive strength of concrete. With the coring method, concrete
43
cores are acquired by drilling a concrete structure at selected locations, and tested for their
44
compressive strength [3]. During the pullout test, a metal disk is attached to the concrete
45
surface with super glue. After a short curing period, the metal disk is pulled perpendicularly
46
off the surface and the pullout force can be used to calculate the compressive strength of the
47
concrete structure [4]. The pullout force can be related to the compressive strength of
48
concrete based on a pre-determined calibration curve. Both the coring and pullout test are
49
destructive, potentially compromising the integrity of concrete structures.
50
Nondestructive approaches such as the maturity method and the ultrasonic pulse velocity
51
(UPV) have also been used to determine the compressive strength of concrete. The maturity
52
method allows the estimate of early-age compressive strength of in-place concrete in real
53
time. A maturity index as a function of curing time and temperature is determined according
54
to the ASTM C1074 Standards [5]. In applications, a reference strength-maturity curve must
55
be developed for each project-specific material in advance. With the UPV method, the
56
velocity of an ultrasonic pulse that travels through concrete is measured and converted to the
57
strength of concrete based on their pre-determined calibration curve [6]. The field application
58
of this method is limited due to the effects of voids, cracks and steel bars.
59
Hyperspectral imagery has been used to assess various conditions of concrete by imaging
60
a concrete surface and analyzing the light reflectance as a function of wavelength for each
61
pixel in an image. Such a reflectance-wavelength spectrum can be divided into many narrow
62
and continuous wavelength bands for their correlation to specific materials on the concrete
63
surface [7]. By analyzing the change of reflectance values at these prominent bands, different
64
materials can be discriminated and classified. For instance, dark gray, light gray and
65
dolomitic limestone were distinguished in the selection of Portland cement clinkers based on
66
3. the reflectance variations of carbonate (CO3) and Al-OH in wavelength ranges of 2125–2400
67
nm and 2170–2250 nm, respectively [8]. The carbonation degradation depth of concrete was
68
estimated from reflectance values at a wavelength of 440 nm, 1500 nm, and 2340 nm [9].
69
The total chloride content in mortar specimens was linearly related to the reflectance at a
70
wavelength of approximately 2260 nm [10]. The status of concrete (hydration, curing and
71
hardening) was determined by constructing a logistic regression model with reflectance
72
spectra [11].
73
In the past decade, hyperspectral imaging has also been used to estimate the compressive
74
strength of concrete. For example, a partial least square regression model was developed to
75
establish the relation between concrete strength (7, 14 and 28 days) and its corresponding
76
reflectance over the entire wavelength range [12]. The reflectance spectrum of eight concrete
77
samples with various W/C ratios and curing ages moved upward with an increase of
78
compression strength [13]. In both studies, the mix designs of concrete were not introduced
79
and the relation between the compression strength and the reflectance at a characteristic
80
wavelength range was not clearly interpreted. Three groups of 28-day cured concrete
81
specimens with a W/C ratio of 0.5, 0.65 and 0.8 were differentiable by comparing absorbance
82
values (complimentary to reflectance) in a wavelength range of 1940-1970 nm [14]. In that
83
study, the relation between absorbance and compression strength was not discussed.
84
The ultimate goal of this study is to rapidly classify the type of concrete with various
85
W/C ratios in pavement construction of highways through hyperspectral imaging from an
86
unmanned aerial vehicle, and determine the early-age compressive strength of concrete
87
pavements from light reflectance spectra. The focus of this paper is to develop a dataset with
88
light reflectance and its corresponding compressive strength of mortar of various types, a
89
classification model for mortar type, and a regression curve of reflectance versus compressive
90
strength corresponding to a specific mortar type. Specifically, three groups of mortar cuboid
91
samples with a W/C ratio of 0.4, 0.5 and 0.6 were cast. For each group, five mortar samples
92
were tested for compressive strength after 1, 3, 5 7, 9, 11, 13, or 14 days of curing. Another
93
nine samples were scanned using a hyperspectral camera from Day 1 to 14. A large set of
94
reflectance data were extracted from the scanned images and used to train Nearest Neighbors
95
(KNN) and Support Vector Machine (SVM) classifiers for discrimination of three mortar
96
types. The compressive strength of each type of mortar samples was measured corresponding
97
to the hyperspectral imaging schedule and related to the light reflectance by an exponential
98
regression model developed.
99
100
4. 2. Experiment Setup
101
2.1. Sample preparation
102
Three types of mortar samples were prepared and designated as C1, C2, and C3 in Table
103
1. They are a mixture of water, ordinary Portland cement and Missouri river sand with a
104
W/C/Sand weight ratio of 0.194/0.324/1.0, 0.182/0.364/1.0, and 0.165/0.415/1.0, or a W/C
105
ratio of 0.6, 0.5, and 0.4, respectively. Type I Portland cement was used as detailed in Table
106
2 for its chemical composition. The Missouri river sand used had the maximum particle size
107
of 4.75 mm, a specific gravity of 2.64, and a fineness modulus of 2.71. Freshly mixed mortar
108
was poured into standard cubic steel molds that are 50 mm × 50 mm × 50 mm in size. After
109
casting, the specimens were covered with wet burlaps and plastic sheets to prevent surface
110
cracking due to shrinkage. After 24 hours of curing, they were demolded for compressive
111
tests and hyperspectral scanning. For each type of mortar mixture, compressive tests of 40
112
samples were conducted according to the ASTM Standard C39 [15], 5 samples tested after
113
1, 3, 5, 7, 9, 11, 13, and 14 days of curing and hardening. All the samples were cured in air
114
with a temperature of 23 ± 1.7 °C and a relative humidity (RH) of 50 ± 5%. Hyperspectral
115
scanning on 9 samples with each mortar mixture was conducted continuously for 13 days
116
from the end of 1st day to 14th day of curing and hardening. For each cuboid sample, only
117
the four vertical side surfaces were scanned since the horizontal top surface was relatively
118
uneven. In addition, the top surface had a thin layer of cement paste due to water bleeding
119
during mortar settlement, which made its composition different from the side surfaces [16].
120
Table 1. Mix proportions of three types of mortar samples by weight (kg/m3
)
121
Types of mortar samples C1 C2 C3
Water 288 270 245
Ordinary Portland cement 480 540 615
Missouri river sand 1482 1482 1482
Table 2. Mass percentage (%) of oxides in cement
122
SiO2 CaO Al2O3 Fe2O3 MgO SO3 Loss of ignition
19.8 64.2 4.5 3.2 2.7 3.4 2.6
123
5. 2.2. Hyperspectral scanning
124
A wideband hyperspectral camera (Headwall Hyperspec VNIR-SWIR dual sensor) was
125
used to scan the mortar samples. The co-aligned VNIR-SWIR sensor has a broad wavelength
126
range of 400 - 2500 nm. The VNIR sensor has a spectral range of 400-1000 nm with 2.2 nm
127
in spectral resolution and the SWIR sensor has a spectral range of 900-2500 nm with 6 nm
128
in spectral resolution. Figure 1 shows the experimental setup of a cuboid mortar sample. A
129
light source (LED illumination) was set at 0.5 m away from the mortar sample and lit the
130
sample from one side (left in the photo). The hyperspectral camera was set right in front of
131
the mortar sample at 1.2 m standoff distance from the front vertical side of the mortar sample
132
for better resolution of near-distance imaging. The camera was installed on a tripod, both
133
connected to a laptop installed with Hyperspec III software to control the camera’s rotation
134
(±5°) in the horizontal plane and collect images continuously. A grey tarp was set right behind
135
the mortar sample as a reference.
136
137
138
Figure 1. Test setup of a cuboid mortar sample with illumination light, a hyperspectral
139
camera, a laptop computer, and a grey tarp.
140
Prior to each sample scanning, the hyperspectral camera was calibrated through the
141
collection and processing of dark and white reference data. Measuring electric current in the
142
camera system, the dark reference was collected with the camera lens covered, and deducted
143
from any scanned image to cleanse noise. The white reference was used to get a white balance
144
to enhance imaging quality. It was collected by aiming the camera lens at the grey tarp with
145
a reflectance of 32%. The grey tarp was chosen in this study since its color was close to that
146
of the mortar samples. Frame period and exposure time were adjusted to ensure that 60% of
147
the saturated light intensity was detected by using the grey tarp since a lack of light intensity
148
6. can generate too many bad pixels to correct mathematically. The rotation angle was adjusted
149
so that the camera can scan the mortar surface area of interest at a fixed standoff distance of
150
1.2 m. The rotation speed of the camera was also adjusted until no distorted shapes or forms
151
were seen in the captured image.
152
At the completion of each sample scanning, the scanned data files were transferred from
153
the camera (480 GB solid-state drive) to the laptop computer. SpectralView software was
154
then used to extract the reflectance spectrum for each pixel in the image by:
155
Calibrated Reflectance = 𝑅𝑎𝑤−𝐷𝑎𝑟𝑘
𝑊ℎ𝑖𝑡𝑒−𝐷𝑎𝑟𝑘
× 𝑊ℎ𝑖𝑡𝑒 𝑅𝑒𝑓𝑒𝑟𝑒𝑛𝑐𝑒 𝑅𝑒𝑓𝑙𝑒𝑐𝑡𝑎𝑛𝑐𝑒 C𝑎𝑙𝑖𝑏𝑟𝑎𝑡𝑖𝑜𝑛 (1)
where Raw is the raw reflectance spectrum without processing, Dark means the dark
156
reference spectrum, White means the white reference spectrum, and White Reference
157
Reflectance Calibration denotes the maximum reflectance of white reference spectrum to
158
ensure no saturation in measurement. The software SpectralView automatically calculates
159
the normalized reflectance using the dark/white reference spectra.
160
2.3 Data classification techniques
161
Two classification models with KNN and SVM algorithms were established to
162
distinguish various types (W/C ratios) of mortar samples from the reflectance dataset as
163
shown in Figure 2. The reflectance dataset is a group of data with each datum showing a
164
reflectance value and its corresponding class label (W/C ratio). In this study, 80% of the
165
reflectance data were used for training and the remaining 20% were used for testing of the
166
classification models. Both KNN and SVM algorithms were trained in Python to construct
167
the classification models. The established classification models were then used to predict the
168
label (Class A, Class B or Class C) for a W/C ratio of 0.6, 0.5, or 0.4 given a test example of
169
known reflectance value.
170
171
Figure 2. Reflectance data classification using classification algorithms.
172
2.3.1 KNN
173
The KNN algorithm computes the proximity of a test example z to K data points in the
174
7. training set, which are closest to z. The test example is classified based on the majority class
175
label of its K nearest neighbors [17]. Weights are assigned to the contributions of the
176
neighbors so that the impact of data depends on their distances to the test example. Choosing
177
the right parameter K is important to ensure a better accuracy in classification. A small K can
178
result in overfitting due to noise in the training data, whereas a large K can lead to
179
misclassification because the nearest neighbors may include data that are located far away
180
from its neighborhood [17].
181
2.3.2 SVM
182
The SVM algorithm creates a line in two-dimensional planes, a plane in three-
183
dimensional spaces, or more generally a hyperplane to divide the data into several classes
184
[18]. Support vectors are the data points nearest to the hyperplane. The distance between the
185
hyperplane and the nearest data is called margin [18]. The goal of SVM is to choose a
186
hyperplane with the maximum margin. To briefly describe the SVM technique, a linear
187
classifier is introduced first and then extended to the nonlinear classifier. Next, the maximum
188
margin of a hyperplane is described.
189
Linear classifier is used to find a line or a plane (hyperplane) to separate dataset
190
{𝐱𝑖, 𝑦𝑖}𝑖=1
𝑛
into two classes. Here, 𝐱𝑖 is the 𝑖𝑡ℎ
vector in the given dataset, 𝑦𝑖 is the label
191
associated with 𝐱𝑖. The hyperplane is defined as [18]:
192
𝑓(𝐱) = 𝐰𝐱 + 𝑏 = 0, 𝐰𝐱 = ∑ 𝑤𝑖𝑥𝑖
𝑖
(2)
where w is a weight vector, and b is a bias. As illustrated in Figure 3 for the case of two-
193
dimensional plane x1x2, f(x) =0 is a line that divides the entire dataset into two classes: f(x)
194
>0 and f(x) <0.
195
196
Figure 3. A linear classifier with maximum margins that divides the data into two sets.
197
198
8. When the data cannot be separated by a linear classifier, they can be mapped to a higher
199
dimension and converted to linearly separable data through a projection function 𝜑 [18, 19].
200
The classifier then becomes:
201
𝑓(𝐱) = 𝐰𝜑(𝐱) + 𝑏 (3)
As the high-dimensional projection function is complicated to compute, this classifier is
202
projected back to the original dimension through a transformation known as the kernel
203
function. In this case, the weight vector can be expressed into a linear combination of the
204
training data [18]:
205
𝐰 = ∑ 𝛼𝑖
𝑛
𝑖=1
𝜑(𝐱𝑖) (4)
where 𝛼𝑖 is the coefficient related to a decision boundary. The kernel function is defined as
206
[18]:
207
𝑘(𝐱𝑖, 𝐱) = 𝜑(𝐱𝑖)𝜑(𝐱) (5)
The classifier then transforms to:
208
𝑓(𝐱) = ∑ 𝛼𝑖
𝑛
𝑖=1 𝜑(𝐱𝑖)𝜑(𝐱) + 𝑏 = ∑ 𝛼𝑖
𝑛
𝑖=1 𝑘(𝐱𝑖, 𝐱) + 𝑏 (6)
Two kernels are widely used in the literature for various applications: polynomial kernel and
209
Gaussian kernel. A polynomial kernel is defined as [18]:
210
𝑘(𝐱𝑖, 𝐱) = (𝐱𝐱𝑖 + 1)𝑑 (7)
A Gaussian kernel is defined as [18, 19]:
211
𝑘(𝐱𝑖, 𝐱) = exp (−
‖𝐱−𝐱𝑖‖𝟐
2𝜎2
)
(8)
where d is the degree of polynomial kernel and σ is a parameter that controls the width of
212
Gaussian kernel. Both parameters control the flexibility of the classifier. When σ is increased,
213
a greater curvature is introduced to the decision boundary but overfitting will occur if σ is
214
too large.
215
SVM looks for a higher margin to get a better classification result for the testing data.
216
The margin of a hyperplane f (x) is defined as:
217
9. 𝑚(𝑓) =
1
‖𝐰‖
(9)
As indicated in Equation (9), to maximize the margin of the classifier is equivalent to
218
minimize‖𝐰‖2
. The maximum margins are the margins that push up against the support
219
vectors. To ensure that the linearly-separable data are classified correctly, the maximum
220
margin and its constraint are defined as [18, 19]:
221
Minimize
1
2
‖𝐰‖2 (10)
Subject to: 𝑦𝑖(𝐰𝐱 + 𝑏) ≥ 1 𝑖 = 1, … , 𝑛. (11)
When the data are not completely separable, the constraint is relaxed and a greater margin
222
can be achieved by [18, 19]:
223
Minimize
1
2
‖𝐰‖2
+ 𝐶 ∑ 𝜉𝑖
𝑛
𝑖=1
(12)
Subject to: 𝑦𝑖(𝐰𝑥 + 𝑏) ≥ 1 − 𝜉𝑖 (13)
where 𝜉𝑖 (0≤ 𝜉𝑖 ≤ 1) is the margin error that allows an example to be in the margin and C
224
is the penalty that lowers the misclassification rate. When C is increased, a smaller margin
225
error is achieved. C needs to be adjusted to ensure the maximum margin with a minimum
226
margin error [18, 19].
227
3. Results and Discussion
228
3.1. Hyperspectral information
229
Figure 4 shows the raw hyperspectral image of one mortar specimen, the image after
230
subtraction of dark reference, and the image after dark and white reference deductions. The
231
sensor current measured from the dark reference can induce perturbation and generate a noisy
232
and drifted spectrum. The white reference can rectify illumination non-uniformity and non-
233
flatness of a spectrum. Removal of the dark reference and the white reference can correct
234
the image and produce right reflectance spectra. For each type of mortar, 9 cuboid samples
235
were prepared, 4 side faces of each sample were scanned, and 30 spectra were extracted over
236
a 50 mm × 50 mm side surface area, totaling 1080 spectra for each scanning day. The spectra
237
10. were collected from the flat surface area only to avoid any non-uniform illumination from
238
uneven spots.
239
(a) (b) (c)
Figure 4. Hyperspectral image of a mortar specimen: (a) raw, (b) after subtraction of dark
240
reference, and (c) after dark and white reference deduction.
241
Figure 5 shows the average reflectance spectra of samples with three different W/C ratios
242
over a period of 14 days. Each line represents the average reflectance spectrum of 1080
243
spectra in the wavelength range of 1200 nm to 2400 nm. The average spectra can reduce
244
potential biases and are more representative of the scanning surface. As seen in Figure 5, the
245
reflectance value on the average spectra rapidly ascends from Day 1 to Day 3 and then
246
gradually increased till Day 14 of test. After 1 day of testing, the samples were scanned after
247
they were demolded and put in air at the room temperature for 1 hour. The higher moisture
248
content on the sample surface resulted in the lower reflectance value due to water absorption.
249
The rapid increase of reflectance from Day 1 to Day 3 is because the hydration process during
250
this period rapidly consumes more water compared with that at a later stage.
251
252
0.04
0.05
0.06
0.07
0.08
0.09
0.1
0.11
0.12
1200 1400 1600 1800 2000 2200 2400
Reflectance
(R)
Wavelength (nm)
Day 1 Day 3 Day 5 Day 7
Day 9 Day 11 Day 13 Day 14
(a)
11. 253
254
Figure 5. The average reflectance spectra over a wavelength of 1200 nm to 2400 nm for
255
samples with a W/C ratio of: (a) 0.6, (b) 0.5, and (c) 0.4.
256
When shot on the surface of materials, some of the incident light leads to vibration of
257
molecules and is absorbed by the chemical bond between atoms in the molecules. In the Near
258
Infrared Region (NIR) (from 780 nm to 2500 nm), higher vibrational energy is acquired to
259
absorb the light, which stimulates the overtones and combinations of fundamental vibrations
260
[20, 21]. Basically, overtones and combinations of the vibrations of C-H, O-H, N-H, and S-
261
H chemical bonds dominate NIR spectroscopy with each chemical bond corresponding to a
262
wavelength region for light absorbance [21]. The combination of OH and H2O corresponds
263
to the region of 1900 nm to 2000 nm [21-24], which is of particular interest in this study. The
264
reflectance change in this wavelength range can be used to track the change of H2O molecules
265
due to hydration consumption in the process of mortar curing. The reflectance values over
266
0.04
0.05
0.06
0.07
0.08
0.09
0.1
0.11
0.12
1200 1400 1600 1800 2000 2200 2400
Reflectance
(R)
Wavelength (nm)
Day 1 Day 3 Day 5 Day 7
Day 9 Day 11 Day 13 Day 14
0.06
0.07
0.08
0.09
0.1
0.11
0.12
1200 1400 1600 1800 2000 2200 2400
Reflectance
(R)
Wavelength (nm)
Day 1 Day 3 Day 5 Day 7
Day 9 Day 11 Day 13 Day 14
(b)
(c)
12. the wavelength range of 1920 nm to 1980 nm as shown in the marked red circles in Figure 5
267
are averaged and defined as dR (1920-1980). The average reflectance dR (1920-1980) with
268
an error bar of ± one standard deviation for three types of mortar over the curing period of
269
14 days are plotted in Figure 6. Logarithmic regression was conducted to fit into the
270
experimental data for the three types of mortar. R2
value for each type of mortar is higher
271
than 90%, indicating good fitting of the regression curve. The overall reflectance during the
272
14 days shows an increasing trend when the W/C ratio is reduced from 0.6 to 0.4 since the
273
mortar with lower W/C ratio has less water content. For each type of mortar, the average
274
reflectance increases because water is reacted and reduced during the hydration process. As
275
less water is left on the sample surface, less light is absorbed by water molecules and
276
reflectance of the light is increased. Therefore, the regression curve can be used to predict
277
the curing process for mortar samples.
278
Figure 6. The average reflectance dR (1920-1980) over a curing period of 14 days for
279
samples with W/C ratios of: (a) 0.6, (b) 0.5, and (c) 0.4.
280
3.2 Classification results with KNN and SVM
281
Both KNN and SVM algorithms were used to first establish classification models with
282
the training data, and then predict classification labels for the test data. In the KNN algorithm,
283
the parameter K varied from 1 to 40 in model training. Its corresponding prediction
284
y = 0.007ln(x) + 0.07
R² = 0.92
W/C=0.6
0
0.02
0.04
0.06
0.08
0.1
0.12
0 2 4 6 8 10 12 14
Average
reflectance
dA
(1920-1980)
Curing Time (Day)
y = 0.011ln(x) + 0.07
R² = 0.93
W/C=0.5
0
0.02
0.04
0.06
0.08
0.1
0.12
0 2 4 6 8 10 12 14
Average
reflectance
dA
(1920-1980)
Curing Time (Day)
y = 0.007ln(x) + 0.09
R² = 0.91
0
0.02
0.04
0.06
0.08
0.1
0.12
0 2 4 6 8 10 12 14
Average
reflectance
dA
(1920-1980)
Curing Time (Day)
(b)
(a)
(c)
13. accuracies after 1, 4, 7, and 13 days of testing are presented in Figure 7. In this study, accuracy
285
is defined by the relative difference in percentage between the number of correct predictions
286
and the number of actual test data. It is observed that K = 5 yields the highest prediction
287
accuracy for Day 1, Day 7, and Day 13, and K = 6 provides the highest accuracy for Day 4.
288
Overall, K = 5 is chosen for the KNN classification model. Figure 8 shows the predicted
289
classifications after 1, 7, and 13 day of testing with K = 5 and after 4 day of testing with K =
290
6. In Figure 8, ‘True’ represents the test data and ‘Pred’ symbolizes the prediction data with
291
the trained KNN classification model. The predicted classification (yellow triangles) and the
292
actual classification (blue squares) are in general agreement.
293
294
Figure 7. Prediction accuracies as a function of K after 1, 4, 7, and 13 day of testing.
295
296
45
50
55
60
65
70
75
80
0 5 10 15 20 25 30 35 40
Accuracy
(%)
K value
Day 1
Day 4
Day 7
Day 13
(b)
(a)
14. Figure 8. Classification predictions after (a) 1, (b) 4, (c) 7, and (d) 13 day of testing.
297
To test the applicability of parameter K, K=5 is applied to predict the classifications of
298
the test data after 2, 5, 8, and 14 day of testing, and the predicted classifications are compared
299
in Figure 9 with their actual classifications. It can be seen from Figure 9 that the predictions
300
are in good agreement with the actual classifications. Specifically, the prediction accuracies
301
during the 4 days of testing range from 70% to 75% as shown in Figure 10, which falls into
302
the same range of accuracies achieved after 1, 4, 7, and 13 day of testing. This comparison
303
indicates that K = 5 is the best fit for the KNN model. Due to low prediction accuracies (<
304
75%) for the KNN model, the SVM algorithm is attempted to improve the prediction
305
accuracy for mortar classification.
306
(d)
(c)
(a) (b)
15. Figure 9. Classification predictions with K = 5 after (a) 2, (b) 5, (c) 8, and (d) 14 day of
307
testing.
308
309
Figure 10. Prediction accuracies with K = 5 after 2, 5, 8, and 14 day of testing.
310
In this study, the average reflectance dR(1920-1980 nm) and its corresponding Min-Max
311
normalized reflectance calculated from Equation (14) forms two features in x1-x2 plane. The
312
representative training data in the feature plane after 13 day of testing are presented in Figure
313
11. It can be seen that the three classes of training data are mainly distributed along three
314
straight lines due to the correlation of two features selected. The relatively low reflectance
315
for a w/c ratio of 0.6 can be easily separated from the other classes using a linear separator.
316
However, the two straight lines representing the feature data in other two classes are crossed
317
each other, making it impossible to separate them directly with a linear classifier. Therefore,
318
a nonlinear classifier with the Gaussian kernel and a relaxed constraint were used to obtain a
319
high prediction accuracy of the classification model.
320
0
20
40
60
80
100
Accuracy
(%)
Day 2 Day 5 Day 8 Day 14
(c) (d)
16. Min-Max normalized reflectance=
dR (1920−1980) −Min.
Max.−Min.
× 100% (14)
321
where Min. and Max. represent the minimum reflectance and the maximum reflectance of
322
the whole data set during each test day, respectively. dR (1920 − 1980) represents the
323
average reflectance over a wavelength range of 1920 nm to 1980 nm, corresponding to each
324
datum used in the dataset.
325
326
Figure 11. Training data in the feature plane after 13 day of testing.
327
To improve the accuracy of classification predication, the hyperparameters C and σ in
328
the SVM model were first tuned. Figure 12 shows the prediction accuracies after 1, 4, 7, and
329
13 day of testing when C is examined from 10-2
to 103
and σ is tested from 10-3
to 10. It can
330
be observed from Figure 12 that C=103
and σ=10 yield the highest prediction accuracies of
331
94.5%, 89.9%, 95%, and 87.4% after 1, 4, 7, and 13 day of testing, respectively. The
332
predicted classifications from the SVM model are compared in Figure 13 with the actual
333
classifications displayed when C = 103
and σ = 10. Compared with the classification results
334
in Figures 8 and 9 predicted from the KNN model, the overlapped range between the
335
predicted data and test data in Figure 12 is enlarged with a higher accuracy. Therefore, C =
336
103
and σ = 10 are chosen for the SVM classification model.
337
17. Figure 12. Prediction accuracies as a function of C and σ in the logarithmic format after (a)
338
1, (b) 4, (c) 7, and (d) 13 day of testing.
339
340
(a) (b)
(c) (d)
(a) (b)
18. Figure 13. Classification predictions of the test data after (a) 1 , (b) 4 , (c) 7 , and (d) 13 day
341
of testing with C = 103
and σ = 10.
342
343
To evaluate their applicability, the two hyperparameters C = 103
and σ = 10 are applied
344
to the test data collected after 2, 5, 8, and 14 day of testing. The predicted results are compared
345
in Figure 14 with their actual classifications. Figure 15 shows 93.4%, 92.9%, 93.0%, and
346
92.5% prediction accuracies after 2, 5, 8, and 14 day of testing, respectively. Therefore, C =
347
103
and σ = 10 in the SVM model are appropriate for the classification of mortar.
348
(c)
(d)
(a) (b)
19. Figure 14. Classification predictions with C = 103
and σ = 10 after (a) 2, (b) 5, (c) 8, and (d)
349
14 day of testing.
350
351
Figure 15. Prediction accuracies with C = 103
and σ = 10 after 2, 5, 8, and 14 day of testing.
352
3.3 Relation between average reflectance and compressive strength of mortar samples
353
For each W/C ratio, five mortar samples were tested in compression after 1, 3, 5, 7, 9,
354
11, 13, and 14 day of curing, following the ASTM C39 Guideline. The average and standard
355
deviation of the five compressive strengths of mortar for each testing day were determined.
356
The average strength with an error bar of ± one standard deviation is displayed in Figure 16
357
as a function of the curing day. Overall, the compressive strength of mortar decreases with
358
the increase of W/C ratio. For each W/C ratio, the compressive strength increases
359
logarithmically with the curing time. The short error bars in Figure 16 indicate consistent
360
data obtained from the compressive tests.
361
0
20
40
60
80
100
Accuracy
(%)
Day 2 Day 5 Day 8 Day 14
(c) (d)
20. 362
Figure 16. Compressive strengths of mortar samples with W/C ratios of 0.6, 0.5, and 0.4.
363
After 1, 3, 5, 7, 9, 11, 13, and 14 day of curing, both the compressive strength and the
364
average reflectance dA (1920-1980) were obtained. Their correlations for W/C ratios of 0.6,
365
0.5, and 0.4 are drawn in Figure 17. Exponential regression of the test data was conducted
366
for each W/C ratio with R2
larger than 90%. These relations indicate that the average
367
reflectance can be used to predict the compressive strength of mortar. Such relationships can
368
potentially be applied to predict the strength of early-age concrete pavement in practice.
369
y = 10.1ln(x) + 4.7
R² = 0.98
y = 11.2ln(x) + 6.6
R² = 0.99
y = 13.4ln(x) + 10.1
R² = 0.99
0
10
20
30
40
50
0 2 4 6 8 10 12 14
Compression
strength
(MPa)
Curing time (Day)
0.6
0.5
0.4
y = 0.041e79x
R² = 0.99
W/C=0.6
0
10
20
30
40
0.04 0.08 0.12
Compression
Stregnth
(MPa)
Average Reflenctance
dA (1920-1980)
y = 0.214e51x
R² = 0.98
W/C=0.5
0
10
20
30
40
0.04 0.08 0.12
Compression
Stregnth
(MPa)
Average Reflenctance
dA (1920-1980)
(a) (b)
21. Figure 17. Average reflectance dA (1920-1980) versus the compressive strength of mortar
370
with a W/C ratio of: (a) 0.6, (b) 0.5, and (c) 0.4 over 14 days.
371
3.4 Further work
372
Concrete samples with a wide range of mixture designs will be scanned with
373
hyperspectral camera. The SVM classification model will be kept updated and improved with
374
the reflectance data extracted from newly scanned concrete samples. The reflectance dataset
375
needs to be enlarged to include as many types of concrete as possible. Field test will be
376
conducted with hyperspectral camera installed on UAV to establish a similar correlation
377
between reflectance and compressive strength under actual in-situ conditions.
378
4. Conclusions
379
This study utilized a hyperspectral camera to scan the surface of mortar samples and
380
extract the reflectance data for the classification of different types of mortar and the
381
prediction of their compressive strength. Based on experimental data, regression analysis,
382
and classification results, the following conclusions can be drawn:
383
• The reflectance spectra over a wavelength range of 1200 nm to 2400 nm are
384
consistently shifted upward as mortar samples are cured and hardened over time. In
385
particular, the average reflectance in 1920-1980 nm wavelength increases
386
logarithmically with time because water molecules on each sample surface are
387
gradually reacted during the hydration process. As the water content of mortar is
388
reduced, less light is absorbed and more light is reflected. The average reflectance
389
can be accurately predicted from the curing time with their correlation coefficient
390
of over 0.9.
391
• The KNN classification model with K=5 represents the best fit to the test data taken
392
from mortar samples with a W/C ratio of 0.4 to 0.6. The classification (W/C ratio)
393
y = 0.045e66x
R² = 0.99
W/C=0.4
0
10
20
30
40
50
0.04 0.08 0.12
Compression
Stregnth
(MPa)
Average Reflenctance
dA (1920-1980)
(c)
22. prediction accuracies are in the range of 70% to 75%. The SVM classification model
394
with C=103
and σ=10 are the best fit to the test data with a prediction accuracy of
395
approximately 90%. Thus, the SVM classification model is recommended to
396
classify various types of mortar.
397
• The compressive strength of mortar can be exponentially related to the average
398
reflectance in 1920-1980 nm wavelength with a correlation coefficient of over 0.95.
399
With this strong exponential relation, hyperspectral imaging can be used as a rapid
400
and nondestructive evaluation tool to predict the compressive strength of mortar.
401
With further studies on concrete specimens, hyperspectral imaging is promising for
402
the prediction of early-age strength of concrete pavement in practical applications.
403
Author Contributions: Conceptualization, L.F., G. C. and H. M.; methodology, L.F. and
404
M.F.; validation, L.F. M.F. and G. C.; formal analysis, L.F. and M.F.; investigation, L.F.,
405
M.F. and A.A.; resources, G. C.; data curation, L.F. and M.F.; writing—original draft
406
preparation, L.F. and M.F.; writing—review and editing, L.F., M.F. and G.C.; visualization,
407
L.F., M.F. and G.C.; supervision, G.C.; project administration, G.C.; funding acquisition,
408
G.C. In general, the first two authors contributed equally to the preparation of this paper.
409
Funding: Financial support to complete this study was provided by the U.S. Department of
410
Transportation, Office of the Assistant Secretary for Research and Technology (OST-R)
411
under the Auspices of the INSPIRE University Transportation Center under Grant No.
412
69A3551747126 at Missouri University of Science and Technology. The findings and
413
opinions expressed in this paper are solely those of the authors and do not represent the
414
official policy or position of the USDOT/OST-R, or any State or other entity.
415
Conflicts of Interest: The authors declare no conflict of interest.
416
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417
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