Recent studies highlight the application of artificial intelligence, machine learning, and simulation techniques in engineering. This book covers the successful implementation of different intelligent techniques in various areas of engineering focusing on common areas between mechatronics and civil engineering. The power of artificial intelligence and machine learning techniques in solving some examples of real-life problems in engineering is highlighted in this book. The implementation process to design the optimum intelligent models is discussed in this book.
Author(s): Ehsan Momeni, Danial Jahed Armaghani, Aydin Azizi
Series: Emerging Trends in Mechatronics
Publisher: Springer
Year: 2023
Language: English
Pages: 253
City: Singapore
About This Book
Contents
Optical Resistance Switch for Optical Sensing
1 Introduction
2 Graphene Optical Switch
3 Nanomaterial Heterostructures-Based Switch
4 Modulation Characteristics
5 Summary
References
Empirical, Statistical, and Machine Learning Techniques for Predicting Surface Settlement Induced by Tunnelling
1 Introduction
2 Settlements and Volume Losses Due to Tunnelling
2.1 Surface Settlement Induced by Single Tunnels
2.2 Surface Settlement Induced by Twin Tunnels
3 Methods for Predicting Surface Settlement Induced by Tunnelling
3.1 Empirical Formula
3.2 Numerical Analysis Modeling
3.3 Laboratory Works
3.4 Statistical Models
3.5 Machine Learning (ML)
4 ML Application in Settlement Induced by Tunnelling
5 Discussion
6 Future Perspective
7 Conclusion
References
A Review on the Feasibility of Artificial Intelligence in Mechatronics
1 Introduction
2 Smart Control Methods
2.1 Adaptive Control Methods
2.2 Optimization Techniques
2.3 ANNs
2.4 Fuzzy Logic Method
2.5 Reinforcement Learning
3 Application of Intelligent Approaches in Engineering Control Problems
3.1 Stabilization and Program Control Problems
3.2 Controller Tuning
3.3 Identification Problems
3.4 Optimization Problems
3.5 Problems of Iterative Learning
4 Conclusions
References
Feasibility of Artificial Intelligence Techniques in Rock Characterization
1 Introduction
2 Artificial Intelligence Methods
2.1 ANFIS Algorithm
2.2 Artificial Neural Networks
2.3 Gene Expression Programming (GEP)
2.4 Random Forest Regression (RFR)
3 Application of Artificial Intelligence in Rock Characterization
3.1 ANN-Based Models for UCS Prediction
3.2 Tree-Based Models for UCS Prediction
4 Summary and Conclusion
References
A Review on the Application of Soft Computing Techniques in Foundation Engineering
1 Introduction
2 Fundamental Methods
2.1 ANFIS Algorithm
2.2 Artificial Neural Networks
3 Application of Artificial Intelligence in Foundation Engineering
3.1 ANN-based Predictive Models of Bearing Capacity for Shallow Foundations
3.2 Tree-Based Predictive Models of Bearing Capacity for Shallow Foundations
3.3 AI-Model for Skirted Foundations
3.4 ANN-Based Predictive Models of Bearing Capacity for Piles
3.5 Tree-Based Predictive Models of Bearing Capacity for Piles
3.6 AI-Based Predictive Models of Settlement for Piles
4 Summary and Conclusion
References
Application of a Data Augmentation Technique on Blast-Induced Fly-Rock Distance Prediction
1 Introduction
2 Factors Influencing Fly-Rock
3 Data Source and Pre-Processing
3.1 Study Area
3.2 Overview of Data
3.3 Data Classification
4 Methodology
4.1 Study Step
4.2 Tabular Variational Autoencoder (TVAE)
4.3 Prediction Models
5 Results and Analysis
5.1 Data Synthesis
5.2 Validation and Analysis
6 Future Direction
7 Conclusion
References
Forecast of Modern Concrete Properties Using Machine Learning Methods
1 Introduction
2 Machine Learning Concept
2.1 Supervised Learning Methods
2.2 Support Vector Machine
2.3 Hybrid SVM-Based Models
2.4 Decision Tree Model
2.5 Gene-Expression Programming (GEP)
2.6 Artificial Neural Network Methods
2.7 Hybrid ANN-Based Models
2.8 Fuzzy Logic (FL)
2.9 Ensemble Learning Methods
3 Using ML Methods in Concrete Science
3.1 Ordinary Concrete
3.2 Self-Consolidation Concrete
3.3 Ultra-High-Performance Concrete
3.4 Alkali-Activated Concert
3.5 Recycled Aggregate Concrete
4 Conclusion
References
Reliability-Based Design Optimization of Detention Rockfill Dams and Investigation of the Effect of Uncertainty on Their Performance Using Meta-Heuristic Algorithm
1 Introduction
2 Material and Methods
2.1 Governing Equations in the First Design Step
2.2 The Structural Stability of the Dam
2.3 Reliability-Based Design Optimization of Detention Rockfill Dams
2.4 Optimization of Detention Rockfill Dam
2.5 Reliability Determination
3 Results
3.1 Case Study
3.2 Preliminary Design
3.3 Uncertainties of Design Parameters
3.4 The Impact of Uncertainty on the Structural and Hydraulic Performance of Preliminary Design
3.5 Optimal Design and the Impact of the Preliminary Design on the Increasing of Optimization Algorithm Efficiency
3.6 The Impact of Uncertainty on the Structural Performance of Optimal Design
3.7 Reliability-Based Design Optimization of the Detention Rockfill Dam
4 Discussion and Conclusion
References
Machine Learning in Mechatronics and Robotics and Its Application in Face-Related Projects
1 Introduction: Machine Learning
2 Face Related Tasks
2.1 Face Detection
2.2 Face Recognition
2.3 Facial Expression Recognition (FER)
References
