Book: "Predicting Bored Pile Foundation Behavior Using Random Forest Machine Learning Algorithm"

Synopsis

The development of Artificial Intelligence (AI) in the last decade has opened up opportunities to improve the quality of planning, design, analysis, monitoring, and project management. In civil engineering, challenges such as rapid infrastructure needs, limited resources, disaster risks, and sustainability demands encourage the use of methods that can learn from data—from Machine Learning (ML), Deep Learning (DL), to Generative AI (GAI). This book is structured with a focus on practice: each chapter is equipped with core concepts, simple calculation examples, and Python code snippets that can be run immediately. Topics covered include geotechnics (bearing capacity, settlement, slope stability), structures (concrete strength, crack detection), transportation (traffic prediction, signal optimization), construction management (cost estimation, delays, heavy equipment), and water resources & the environment (flooding, air quality, water pollution). In addition to the examples above, this book also discusses the prediction of the bearing capacity of bored pile foundations using the Random Forest Machine Learning algorithm deployed to Streamlit and GitHub using the foundation design example of the Wori Bridge. We hope that this book will be a bridge between theory and practice in the field.