Compressive stregnth of concerte using image processing ANM & ML
1. Department Of Civil Engineering
Seminar Review 3
Topic Name :- Predecting Compressive Strenght Of Concrete Using
Image Processing & ML.
Student Name :- Mundhe Sujit S. (TECV140)
Guide Name :- Prof. G.W. Rathod
2. 2
1) To Find Different Method Based On Dataset.
2) To Learn Working Of Each Method.
3) To Get The Final Conclusion.
To Check The Applicability Of IPT & ML For
Determination Of Compressive Strength Of Concrete.
OBJECTIVES
AIM
3. METHDOLOGY
3
Selection of Topic
Study Through
Research papers
Finding different
Methods
Study the
Outcomes of
different method
Literature Review
& Conclusion
Preparation of
Report
4. Months January Feb March April May JUNE
Weeks 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2
Selection Of Topic
Collection Of
Research Paper
Studying Different
method
Literature Review
& Conclusion
Prediction Of
Results
TIME SCALE CHART
4
5. Department Of Civil Engineering, PCCOE
7
SR.
NO.
NAME OF RESEARCH PAPER NAME OF
JOURNAL
NAME OF RESEARCHER CONCLUSIONS
1. Machine learning-based
compressive strength prediction
for concrete: An adaptive
boosting approach
Construction and
Building Materials
D.Cheng Feng ,Zhen-Tao Liu &
X.Wang
The AdaBoost algorithm is adopted to predict the compressive strength of concrete.
Large Dataset is collected to train the model and reaches an accuracy of 98%.
Different algorithms are compared to show the superior of the proposed model.
Key factors and in the AdaBoost and influence of input variables are investigated
2. Assessment of concrete
compressive strength by image
processing technique
CONSTRUCTION
AND BUILDING
MATERIALS
Celalettin Basyig, Bekir Çomak
& Ismail Serkan
Seven different concrete specimens were produced.
Concrete compressive strength values were predicted based on the Grey Scale value
of image.
ImageJ Software is used for proccesing on images
3. Modeling Of Strength Of High-
performance Concrete Using
Artificial Neural Networks
CEMENT AND
CONCRETE
RESEARCH
I.C. Yeh This paper is aimed at demonstrating the possibilities of adapting artificial neural
networks (ANN) to predict the compressive strength of high-performance concrete.
A set of trial batches of HPC was produced in the laboratory and demonstrated
satisfactory experimental results
4 Metaheuristic-Based Machine
Learning System for Prediction
of Compressive Strength based
on Concrete Mixture Properties
and Early-Age Strength Test
Results
CIVIL ENGINEERING
DIMENSION, VOL.
20, NO. 1
Prayogo D. This research develops an advanced machine learning method to forecast the
concrete.
hybrid system is proposed so called the symbiotic organisms search-least squares
support vector regression (SOS–LSSVR)
5 Fuzzy logic model for the
prediction of cement
compressive strength
CEMENT AND
CONCRETE
RESEARCH
Sedat Akkurta, Gokmen
Tayfurb, Sever Canc These rules are expressed in the If –Then format.No mathematical equations and
parameters
The input variables of alkali, Blaine, SO3, and C3S and the output variable of 28-day
cement strength were fuzzified by the use of artificial neural networks
The prediction of 50 sets of the 28-day cement strength data by the developed fuzzy
model was quite satisfactory
6. Department Of Civil Engineering, PCCOE 8
SR.
NO.
NAME OF
RESEARCH PAPER
NAME OF
JOURNAL
NAME OF
RESEARCHER
CONCLUSIONS
6 CONSTRUCTION AND
BUILDING MATERIALS
Machine learning in
concrete strength
simulations: Multi-
nation data analytics
J.S. Chou ,C.F.Tsai &
A.d. Pham
This comprehensive study used advanced machine learning techniques to predict concrete compressive
strength.
Model performance is evaluated through multi-nation data simulation experiments.
The prediction accuracy of ensemble technique is superior to that of single learning models.
This study developed advanced learning approaches for solving civil engineering problems
7 Machine learning
techniques to predict
the compressive
strength
of concrete
International Journal
Of Methods
Numerical For
Calculation And
Design In
Engineering
P. F. S. Silva ,G. F.
Moita & V. F.
Arruda
Three computational methods of machine learning and artificial intelligence were used, namely Random
Forest, Support Vector Machines and Artificial Neural Networks
The obtained results show that the Random Forest gave the best performance.
Computational intelligence models can be used to predict the compressive strength of concrete
specimens, is shown in this study.
suggests an approach to evaluate compressive strength against destructive testing methods
8 Machine learning in
concrete strength
simulations: Multi-
nation data analytics
CONSTRUCTION
AND BUILDING
MATERIALS
J.S. Chou ,C.F.Tsai
& A.d. Pham
This comprehensive study used advanced machine learning techniques to predict concrete compressive
strength.
Model performance is evaluated through multi-nation data simulation experiments.
The prediction accuracy of ensemble technique is superior to that of single learning models.
This study developed advanced learning approaches for solving civil engineering problems.
9 Concrete compressive
strength detection
using image processing
based new test method
Measurement G.Dogan,M.H.
Arslan & M.
Ceylan
This study uses ANN and IP together to determine the mechanical properties of concrete, such as the
compressive strength, modulus of elasticity and maximum deformation.
The primary objective of study is to predict the mechanical properties of concrete without causing
destruction, using a new alternative method
96 cylindrical concrete samples were produced; images of the samples were taken before they were
examined at the compression testing, and the training and testing procedures for ANN and IP were
realized using the obtained pressure readings at the laboratory
7. FINDINGS
7
1030 sets of data is collected to train the model
Different algorithms are compared to show the superior of the proposed
model.
Familiar with Softwares Such As ImageJ
Adaboost & Random forest algorithm are suitable for achieving
maximum accurancy than other models.
The releation for training the data & Testing the data is generally 4:1 is
suitable.
8. 8
Compressive strength of the concrete
is very important factor in any
concrete work.
Equipment's required for the testing
are very expensive.
Require more time for the prediction
of results.
Image Processing Technology (IPT).
Introduction
COMPRESSIVE
STRENGTH
Destructive
Testing
CTM/UTM
Non-destructive
Testing
Rebound Hammer
Test /Ultrasonic
Pulse Velocity etc.
9. 9
IMAGE PROCESSING TECHNOLOGY
A picture can be converted into digital form
Adaptation of the human eye’s vision physiology to computer
systems.
Image processing methods include many processes such as image
provision, image digitalization, segmentation, image enhancement ,
classification, recording and recalling.
Image processing is used in medicine , radar images assessment.
weather forecasting and estimation of agricultural products
10. 10
Self Driving Cars
Image Tagging & Recognozation
Optical Character Recognization OCR
Why to Choose Image Processing ?
11. The Overall Work Can be
Done With Different Ways
Some Of them As Follows:-
Using Grey Scale Value
With Machine Learning
(ML)
With Artificial Neural
Network (ANN)
11
12. In this study, seven different concrete classes were obtained by using different
water/cement ratios, which are 0.79, 0.69, 0.61, 0.54, 0.47, 0.42 and 0.37. As known,
with increasing cement amounts in the mixture, concrete compressive strength
values also increase. Therefore, an increase is also expected in the gray color
values in concrete images.
12
Using Grey Scale Value
13. Sample Preparation
Concrete specimens, which are 100x100x100 mm in size, were
marked as shown in Fig.
Marked specimens were cut into pieces with water in a coring
header, so that four pieces were obtained from each specimen.
In total, six surfaces, one of which is from the cut surface of
pieces no 1 and 4, and two of which are from pieces no 2 and 3
were obtained.
Cutting was made in perpendicular direction to concrete
pouring
13
14. 14
Setup For For Production Of Images
Photographed in 4272 x 2848 pixel size and in RGB color mode, with jpg extension.
15. 15
Processing
Sizes were turned into 2350 x 2350 pixels by means of the open source code ImageJ
software
The images were first transferred from RGB color mode to grayscale of 8 bytes
Grayscale images are composed of different gray values (G = {0, 1, 2,..., 255}), which
means that there are 256 different values in an image. The 256 gray value is defined as
a byte (1 byte = 8 bit and 28 = 256). As a rule, 0 value corresponds to black, while 255
value corresponds to white. Gray tones are formed in between these value
The image histogram identifies the pixels at each point in an image and shows the
number of these pixels; so a great deal of information regarding an image can be
provided by histograms
16. 16
• In the next step, histograms, which are the graphical expressions of pixel values
on an image, are formed for each image turned into grayscale.
18. 18
What Is Machine Learning?
“Machine Learning is the science of getting computers to learn and act like
humans do, and improve their learning over time in autonomous fashion, by
feeding them data and information in the form of observations and real-world
interactions.”
How We Get Machines to Learn
There are four basic steps for building a machine learning application (or model).
These are typically performed by data scientists working closely with the
business professionals for whom the model is being developed.
19. 19
Step 1: Select
and prepare a
training data set
Step 2: Choose
an algorithm to
run on the
training data set
Step 3: Training
the algorithm
to create the
model
Step 4: Using
and improving
the model
Data Collection and Analysis
The output variable is the compressive concrete strength (MPa) which depends on eight input
variables:
1. Cement (kg/m3 ),
2. Fly ash (kg/m3 ),
3. Blast furnace slag (kg/m3 ),
4. Water (kg/m3 ),
5. Superplasticizer (kg/m3 ),
6. Coarse aggregate (kg/m3 ),
7. Fine aggregate (kg/m3 )
8. Age at the time of testing (days).
20. 20
List of Common Machine Learning Algorithms
Here is the list of commonly used machine learning algorithms. These algorithms can be applied
to almost any data problem:
1.Linear Regression
2.Logistic Regression
3.Decision Tree
4.Support Vector Machine(SVM)
5.Random Forest
6.Dimensionality Reduction Algorithms
7.Gradient Boosting algorithms
8. Adaptive boosting
Out Of Which the Adaptive boosting (Adaboost) is most used for the concrete & it Founded that
it is More Accurate Than Others,
21. 21
Adaptive boosting ( Adaboost))
How AdaBoost Works?
It makes n number of decision trees during the training period of data.
As the first decision tree/model is made, the record which is incorrectly classified during the first
model is given more priority. Only these records are sent as input for the second model.
The process will go on until we specify a number of base learners we want to create.
The repetition of records is allowed with all boosting techniques.
The characteristic of AdaBoost is to use the initial training data to generate a weak learner, then adjust
the distribution of the training data according to the predicting performance for the next round weak
learner training.
The training samples with low predicting accuracy in the previous step will get more attention in the
next step. Finally the weak learners are integrated together with different weights to a strong learner
22. 22
This figure shows that when the first model is made and the errors from the first model are noted by the algorithm,
the record which is incorrectly classified is given as the input for the next model. This process is repeated until the
specified condition is met. As you can see in the figure, there are n number of models made by taking the errors
from the previous model. This is how boosting works. The models 1,2, 3,…, N are individual models that can be
known as decision trees. All types of boosting models work on the same principle
23. 23
Implementation process
The implementation of AdaBoost can be performed easily. In general it has four
stages:
(1) collection of the experimental data;
(2) generation of the strong learner;
(3) test or validation of the learner;
(4) application of the learner to the engineering problems.
24. 24
There are many different algorithms used to
train neural networks with too many variants.
there are three layers in the neural network.
•The input layer
•Hidden Layer
•The output layer
Artificial Neural Network (ANN)
Artificial neural networks (ANN) is a Biologically
inspired computing code with the number of simpl
highly interconnected processing elements for
simulating human brain working & to process
information model
What Is Meant by ANN ?
26. 26
Steps Involved In ANN
In ANN model the data set was divided into three
subsets
1.Training which contains 70% of total data
2.Validation which contains 15% of total data
3.Test data which contains 15% of total data
27. 27
Training stage
The training state for the artificial neural network model. As it is illustrated in the Figure, the errors are repeated 6 times after
epoch 9 and the test is stopped at epoch 15. This error repeats starting at epoch 10 demonstrated over-fitting of the data.
Therefore, the epoch 9 is selected as the base and its weights are chosen as the final weights. Moreover, the validation check is
equal to 6, due to the fact that the errors are repeated 6 times before stopping the process.
28. 28
Figure presents the validation performance and mean squared error of the network starting at a large value and reducing to
a small value. The plot consists of three lines for three different steps of training, validation, and test. Training process on the
training vectors continues until the model gets to the point that the training reduces the error of network on the validation
vectors which would lead to avoiding the over-fitting of the data sets. As it is shown in the Figure, the best validation
performance is happened at epoch 9, and after 6 error repetitions, the process is stopped at epoch 15.
Validation stage
29. 29
Figure shows the error histogram for the three steps of training, validation, and test in artificial
neural network modeling. As it is shown in the Figure, the zero error is illustrated with a yellow line
in the middle with 9 instances in the training set.
Validation stage
31. 31
CONCLUSION
Image Processing & ML Helps to getting Easily the Compressive Strength of
Concrete
Data must be well trained to get the maximum Accurancy
With IPT & ANN the cost required can be reduced upto a great extend
The Results Obtained From these techniques are very close to the actual values
At Initial Stages For the data preparation & data training requires much
algorithms knowledge .
With very intelligently using these technique we can by-pass the costly traditional
instruments Such As CTM/UTM.
This technique also helps to nullified the 28 days wait for the results . Ultimately
Saves Time & hence Speed of work increase.
32. 32
D.Feng , & Zhen-tao Liu (2019), “Machine Learning-based Compressive Strength
Prediction For Concrete: An Adaptive Boosting Approach”, Construction And
Building Materials.
Celalettin, Bekir Çomak & Ismail Serkan (2012), “Assessment Of Concrete
Compressive Strength By Image Processing Technique” , Construction And
Building Materials.
Prayogo, D (2018) “Metaheuristic-based Machine Learning System For
Prediction Of Compressive Strength”, Civil Engineering Dimension, Vol. 20.
I.C. Yeh (1998), “Modeling Of Strength Of High-performance Concrete” Cement
And Concrete Research, vol. 28.
G.Dogan &Musa H. Arslan,(2017) “Concrete Compressive Strength Detection
Using Image Processing Based New Test Method”, Measurement.
References
33. J.Chou, C.F. Tsai & A.D. Pham ,(2014), “Machine Learning In Concrete Strength
Simulations: Multi-nation Data Analytics”, Construction And Building Materials.
Sedat Akkurta, Gokmen Tayfurb, & Sever Canc,(2004), “Fuzzy Logic Model For
The Prediction Of Cement Compressive Strength”, Cement And Concrete
Research.
Priscila F. S. Silva G. F.Moita & V.F. Arruda (2020), “Machine learning
techniques to predict the compressive strength of concrete” Revista International.
Shivaraj. M ,Ravi Kumar H , Prema Kumar W & Preetham. S (2015),
“Prediction of Compressive, Flexural and Splitting Tensile Strengths of Concrete
using Machine Learning Tools”, International Journal Of Engineering Research &
Technology (IJERT) ISSN: 2278-0181 Vol.4
Susom Dutta, P. Samuiand & D. Kim,(2018), “Comparison of machine learning
techniques to predict compressive strength of concrete”, Computers and Concrete.
33
References
