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Summary
Summary
In today's developing world, industries are constantly required to improve and advance. New approaches are being implemented to determine optimum values and solutions for models such as artificial intelligence and machine learning. Research is a necessity for determining how these recent methods are being applied within the engineering field and what effective solutions they are providing. Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering is a collection of innovative research on the methods and implementation of machine learning and AI in multiple facets of engineering. While highlighting topics including control devices, geotechnology, and artificial neural networks, this book is ideally designed for engineers, academicians, researchers, practitioners, and students seeking current research on solving engineering problems using smart technology.
Table of Contents
| 1 Review and Applications of Machine Learning and Artificial Intelligence in Engineering: Overview for Machine Learning and AI | p. 1 |
| 2 Artificial Neural Networks (ANNs) and Solution of Civil Engineering Problems: ANNs and Prediction Applications | p. 13 |
| 3 A Novel Prediction Perspective to the Bending Over Sheave Fatigue Lifetime of Steel Wire Ropes by Means of Artificial Neural Networks | p. 39 |
| 4 Introduction and Application Aspects of Machine Learning for Model Reference Adaptive Control With Polynomial Neurons | p. 59 |
| 5 Optimum Design of Carbon Fiber-Reinforced Polymer (CFRP) Beams for Shear Capacity via Machine Learning Methods: Optimum Prediction Methods on Advance Ensemble Algorithms - Bagging Combinations | p. 85 |
| 6 A Scientometric Analysis and a Review on Current Literature of Computer Vision Applications | p. 104 |
| 7 High Performance Concrete (HPC) Compressive Strength Prediction With Advanced Machine Learning Methods: Combinations of Machine Learning Algorithms With Bagging, Rotation Forest, and Additive Regression | p. 118 |
| 8 Artificial Intelligence Towards Water Conservation: Approaches, Challenges, and Opportunities | p. 141 |
| 9 Analysis of Ground Water Quality Using Statistical Techniques: A Case Study of Madurai City | p. 152 |
| 10 Probe People and Vehicle-Based Data Sources Application in Smart Transportation | p. 162 |
| 11 Application of Machine Learning Methods for Passenger Demand Prediction in Transfer Stations of Istanbul's Public Transportation System | p. 196 |
| 12 Metaheuristics Approaches to Solve the Employee Bus Routing Problem With Clustering-Based Bus Stop Selection | p. 217 |
| 13 An Assessment of Imbalanced Control Chart Pattern Recognition by Artificial Neural Networks | p. 240 |
| 14 An Exploration of Machine Learning Methods for Biometric Identification Based on Keystroke Dynamics | p. 259 |
