Artificial Intelligence and Machine Learning Techniques for Civil Engineering

✍ Scribed by Vagelis Plevris, Afaq Ahmad, Nikos D. Lagaros

• Publisher: Engineering Science Reference
• Year: 2023
• Tongue: English
• Leaves: 404
• Category: Library

✦ Synopsis

In recent years, artificial intelligence (AI) has drawn significant attention with respect to its applications in several scientific fields, varying from big data handling to medical diagnosis. A tremendous transformation has taken place with the emerging application of AI. AI can provide a wide range of solutions to address many challenges in civil engineering.
Artificial Intelligence and Machine Learning Techniques for Civil Engineering highlights the latest technologies and applications of AI in structural engineering, transportation engineering, geotechnical engineering, and more. It features a collection of innovative research on the methods and implementation of AI and machine learning in multiple facets of civil engineering. Covering topics such as damage inspection, safety risk management, and information modeling, this premier reference source is an essential resource for engineers, government officials, business leaders and executives, construction managers, students and faculty of higher education, librarians, researchers, and academicians.

✦ Table of Contents

Title Page
Copyright Page
Book Series
Editorial Advisory Board
Table of Contents
Detailed Table of Contents
Preface
Acknowledgment
Chapter 1: Artificial Intelligence-Assisted Building Information Modelling
Chapter 2: Deep Learning-Based Damage Inspection for Concrete Structures
Chapter 3: Machine Learning Applications for Vibration-Based Structural Health Monitoring
Chapter 4: Use of AI and ML Algorithms in Developing Closed-Form Formulae for Structural Engineering Design
Chapter 5: A Predictive Regression Model for the Shear Strength of RC Knee Joint Subjected to Cyclic Load
Chapter 6: Predicting the Fundamental Period of Light-Frame Wooden Buildings by Employing Bat Algorithm-Based Artificial Neural Network
Chapter 7: Shear Capacity of RC Elements With Transverse Reinforcement Through a Variable-Angle Truss Model With Machine-Learning-Calibrated Coefficients
Chapter 8: Groundwater Modelling of the Saq Aquifer Using Artificial Intelligence and Hydraulic Simulations
Chapter 9: Reliability Analysis of RC Code for Predicting Load-Carrying Capacity of RCC Walls Through ANN
Chapter 10: The Value Proposition of Machine Learning in Construction Management
Chapter 11: Explainable Safety Risk Management in Construction With Unsupervised Learning
Chapter 12: Enhanced Stochastic Paint Optimizer for Nonlinear Design of Fuzzy Logic Controllers in Steel Building Structures for the Near-Fault Earthquakes
Compilation of References
About the Contributors
Index