Applications of Computational Intelligence in Concrete Technology

Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Preface
Editors
Contributors
Chapter 1 Usage of Computational Intelligence Techniques in Concrete Technology
1.1 Introduction
1.2 Computational Intelligence Models for Concrete Technology
1.2.1 Artificial Neural Networks (ANN)
1.2.2 Adaptive Neuro-Fuzzy Inference System (ANFIS)
1.2.3 Genetic Algorithm (GA)
1.2.4 Random Forest (RF)
1.2.5 Random Tree (RT)
1.2.6 Linear Regression (LR)
1.2.7 M5P Model
1.2.8 Support Vector Machine (SVM)
1.3 Predictive Computational Intelligence in Concrete Technology
1.3.1 Prediction of Compressive Strength of the Concrete
1.3.2 Prediction of Ultrasonic Pulse Velocity of the Concrete
1.4 Conclusions
References
Chapter 2 Developing Random Forest, Random Tree, and Linear Regression Models to Predict Compressive Strength of Concrete Using Glass Fiber
2.1 Introduction
2.2 Modeling Techniques
2.2.1 Random Forest (RF)
2.2.2 Random Tree (RT)
2.2.3 Linear Regression (LR)
2.2.3.1 Methodology and Data Description
2.2.3.2 Performance Evaluation Criteria
2.3 Result Analysis and Discussion
2.3.1 Result of RF and RT Model
2.3.2 Result of LR Model
2.4 Intercomparison among Computing Models
2.5 Sensitivity Analysis
2.6 Conclusion
References
Chapter 3 Prediction of Compressive Strength at Elevated Temperatures Using Machine Learning Methods
3.1 Introduction
3.2 Dataset and Methodology
3.3 Predictive Modelling
3.4 Analysis of Results and Discussion
3.4.1 Linear Regression Method
3.4.2 Regression Tree Method
3.4.3 Boosting Method
3.4.4 Neural Network Method
3.5 Conclusion
References
Chapter 4 Implementation of Machine Learning Approaches to Evaluate Flexural Strength of Concrete with Glass Fiber
4.1 Introduction
4.2 Machine Learning Models
4.2.1 Random Forest (RF)
4.2.2 Bagging
4.2.3 Stochastic
4.2.4 M5P Tree
4.3 Methodology and Dataset
4.4 Model Evaluation
4.5 Result and Discussion
4.5.1 Random Forest (RF) Model Evaluation
4.5.2 Bagging Random Forest Model Evaluation (BRF)
4.5.3 Stochastic Random Forest Model Evaluation (SRF)
4.5.4 M5P Tree Model Evaluation
4.6 Results Comparison
4.7 Sensitivity Analysis
4.8 Conclusion
References
Chapter 5 A Comparative Study Using ANFIS and ANN for Determining the Compressive Strength of Concrete
5.1 Introduction
5.2 Soft Computing Techniques
5.3 ANN
5.4 ANFIS
5.5 Performance Assessment Indices
5.6 Results and Discussion
5.6.1 Results of ANFIS Training-Based
Model
5.6.2 Results of ANFIS Testing-Based
Model
5.6.3 Results of ANN-Based
Model
5.7 Comparison of Models
5.8 Conclusion
References
Chapter 6 Prediction of Concrete Mix Compressive Strength Using Waste
Marble Powder: A Comparison of ANN, RF, RT, and LR Models
6.1 Introduction
6.2 Conventional Models
6.3 Soft Computing Techniques
6.3.1 Artificial Neural Network (ANN)
6.3.2 Random Forest (RF)
6.3.3 Random Tree (RT)
6.3.4 Linear Regression (LR)
6.4 Methodology and Dataset
6.4.1 Dataset
6.4.2 Model Evaluation
6.5 Result Analysis
6.5.1 Assessment of Empirical Formula
6.5.2 Assessment of ANN Based Model
6.5.3 Assessment of RF Based Model
6.5.4 Assessment of RT Based Model
6.5.5 Assessment of LR Based Model
6.5.6 Comparison among Best Developed Models
6.6 Conclusion
References
Chapter 7 Using GA to Predict the Compressive Strength of Concrete Containing Nano-Silica
7.1 Introduction
7.2 Genetic Algorithm (GA)
7.3 Database
7.4 Function Approximation
7.5 Optimization Using Genetic Algorithm Technique
7.6 Availability Optimization Using Genetic Algorithm
7.7 Conclusions
References
Chapter 8 Evaluation of Models by Soft Computing Techniques for the Prediction of Compressive Strength of Concrete Using Steel Fibre
8.1 Introduction
8.1.1 Objectives of the Study
8.2 Soft Computing Techniques
8.2.1 Artificial Neural Network (ANN)
8.2.2 Artificial Neural Network–Cross-Validation
8.2.3 Linear Regression (LR)
8.3 Methodology and Dataset
8.3.1 Dataset
8.3.2 Model Evaluation
8.4 Result Analysis
8.4.1 Assessment of ANN-Based Model
8.4.2 Assessment of ANN–Cross-Validation (Ten-Fold)-Based Model
8.4.3 Assessment of LR-Based Model
8.4.4 Comparison among Best Developed Models
8.5 Conclusion
8.6 The Interest of Conflict Statement
References
Chapter 9 Using Regression Model to Estimate the Splitting Tensile Strength for the Concrete with Basalt Fiber Reinforced Concrete
9.1 Introduction
9.2 Soft Computing Technique Review
9.2.1 Gaussian Process Regression
9.2.2 Support Vector Machines (SVM)
9.2.3 Multiple Linear Regression (MLR)
9.2.4 Performance Evaluation Indices
9.2.4.1 Correlation Coefficient (CC)
9.2.4.2 Root Mean Square Error (RMSE)
9.2.4.3 Mean Absolute Error
9.3 Data Set
9.4 Material Methodology
9.5 Results and Discussion
9.5.1 Sensitivity Analysis
9.6 Conclusion
References
Chapter 10 Prediction of Compressive Strength of Self-Compacting Concrete Containing Silica’s Using Soft Computing Techniques
10.1 Introduction
10.2 Soft Computing Techniques
10.2.1 Artificial Neural Network (ANN)
10.2.2 Linear Regression (LR)
10.2.3 Support Vector Machine (SVM)
10.2.4 Random Forest (RF)
10.2.5 Bagging
10.3 Data and Analysis
10.3.1 Data Set
10.3.2 Evaluation Parameters
10.4 Results and Discussion
10.4.1 ANN and Ensemble ANN Model
10.4.2 LR and Ensemble LR Model
10.4.3 SVM and Ensemble SVM Model
10.4.4 RF and Ensemble RF Model
10.4.5 Inter-Comparison between Applied Models
10.4.6 Sensitivity Analysis
10.4.7 Experimental Work
10.5 Conclusions
References
Chapter 11 Using Soft Computing Techniques to Predict the Values of Compressive Strength of Concrete with Basalt Fiber Reinforced Concrete
11.1 Introduction
11.2 Review of Regression and Soft Computing Techniques
11.2.1 Artificial Neural Networks
11.2.2 Random Forest
11.2.3 M5P Model
11.2.4 Stochastic
11.2.5 Random Tree Model
11.2.6 Performance Evaluation Indices
11.2.6.1 Correlation Coefficient (CC)
11.2.6.2 Root Mean Square Error (RMSE)
11.2.6.3 Mean Absolute Error
11.2.6.4 Nash Sutcliffe Model Efficiency
11.3 Materials and Methodology
11.3.1 Data Set
11.4 Results and Discussion
11.4.1 Results of ANN Technique
11.4.2 Results of the Tree and Forest-Based
Models
11.4.3 Comparison among ANN and Soft Computing-Based
Models
11.4.4 Sensitivity Analysis
11.5 Conclusion
References
Chapter 12 Soft Computing-Based
Prediction of Compressive Strength
of High Strength Concrete
12.1 Introduction
12.2 Soft Computing Techniques Theory
12.2.1 GP
12.2.2 SVM
12.3 Data Representation Superplasticizer and Description
12.4 Results and Discussion
12.4.1 Prediction of CS by GP
12.4.2 Prediction of CS by SVM
12.4.3 Prediction of CS by LR
12.4.4 Comparison of Results
12.5 Conclusion
References
Chapter 13 Forecasting Compressive Strength of Concrete Containing Nano-Silica Using Particle Swarm Optimization Algorithm and Genetic Algorithm
13.1 Introduction
13.2 Problem Formulation
13.2.1 Database
13.2.2 Function Approximation
13.2.2.1 Function Approximation: Algorithm
13.2.2.2 Function Approximation: Model
13.2.2.3 Function Optimization: Model
13.3 Methodology
13.3.1 Genetic Algorithm (GA)
13.3.2 Particle Swarm Optimization
13.4 Results
13.4.1 GA Technique
13.4.2 Optimization Using PSO Technique
13.4.3 Comparison of the Results from GA and PSO Techniques
13.5 Conclusions
13.6 Conflict of Interest
References
Chapter 14 Prediction of Ultrasonic Pulse Velocity of Concrete
14.1 Introduction
14.2 Details of Modeling Approaches Used
14.2.1 M5P Tree (M5PT)
14.2.2 M5 Rule (M5PR)
14.2.3 Random Forest Regression (Ran-For)
14.3 Experiments Performed and Data Set
14.3.1 Ultrasonic Pulse Velocity Test (UPV)
14.3.2 Core Tests
14.3.3 Half-Cell Potential Test (HCP)
14.3.4 Carbonation Test
14.3.5 Chloride Test
14.3.6 Data Set and Analysis
14.4 Results and Discussion
14.4.1 Results from M5PR
14.4.2 M5PT
14.4.3 Result from Ran-Forest
14.4.4 Comparison of Results
14.5 Conclusion
References
Chapter 15 Evaluation of ANN and Tree-Based Techniques for Predicting the Compressive Strength of Granite Powder Reinforced Concrete
15.1 Introduction
15.2 Soft Computing Techniques
15.2.1 Artificial Neural Network (ANN)
15.2.2 Random Forest (RF)
15.2.3 Random Tree (RT)
15.2.4 Reduced Error Pruning (REP) Tree
15.2.5 Performance Assessment Parameters
15.2.6 Data Set
15.3 Result and Discussion
15.3.1 Results of ANN-Based Models
15.3.2 Results of RF-Based Models
15.3.3 Results of RT-Based Models
15.3.4 Results of REP Tree-Based Models
15.4 Assessment or Comparison among Soft Computing-Based Applied Models
15.5 Sensitivity Analysis
15.6 Conclusion
References
Chapter 16 Predicting Recycled Aggregates Compressive Strength in
High-Performance Concrete Using Artificial Neural Networks
16.1 Introduction
16.2 Artificial Neural Networks (ANN) (Yadollahi 2016)
16.3 Design of Neural Networks Models
16.4 Experimental Program
16.5 Data Sets
16.6 Analytic Study Using
16.7 Result and Discussion
16.8 Conclusion
Acknowledgment
References
Chapter 17 Compressive Strength Prediction and Analysis of Concrete
Using Hybrid Artificial Neural Networks
17.1 Introduction
17.2 Material
17.2.1 Training and Testing Neural Networks with Exemplar Data
17.3 Methods
17.3.1 Data Pre-processing
17.3.2 Training Parameters and Neural Networks Structure
17.3.3 ANN training and Determination Using the BP Algorithm
17.3.4 Using GA to Evolve Neural Networks’ Initial Weights and Biases and Then Training Them Using the BP Technique
17.4 Statistical Analysis
17.5 Results
17.6 Discussion
17.7 Conclusion
References
Index