The construction industry worldwide has been a late bloomer to adopting digital technology, where construction projects are predominantly managed with a heavy reliance on the knowledge and experience of construction professionals. Artificial intelligence (AI) works by combining large amounts of data with fast, iterative processing and intelligent algorithms (eg, neural networks, process mining, and deep learning), allowing the computer to learn automatically from patterns or features in the data. It provides a wide range of solutions to address many challenging construction problems, such as tunnel-induced damages, risk estimates, and defect detection. A tremendous transformation has taken place in the past years with the emerging applications of AI in the construction industry. This enables industrial participants to operate projects more efficiently and safely, not only increasing the automation and productivity in construction but also enhancing the competitiveness globally.

This book highlights the latest technologies and applications of AI in the domain of construction engineering and management (CEM). Various AI techniques have been developed to make machines mimic human cognitive processes in terms of learning, reasoning, and self-correcting. Relying on the important AI approaches, we put emphasis on nine hot research topics of AI in CEM, including “ Knowledge Representation and Discovery”(second chapter),“ Fuzzy Modeling and Reasoning”(third chapter),“Time Series Prediction”(fourth chapter),“Information Fusion”(fifth chapter),“Dynamic Bayesian Networks”(sixth chapter),“Process Mining”(seventh chapter),“Agent-Based Simulation”(eighth chapter),“Expert Systems”(ninth chapter), and “Computer Vision”(tenth chapter). Different AI approaches have been proposed to achieve various kinds of functions which are beneficial to CEM in terms of automation, safety, reliability, and digitalization. These applications serve as evidence to verify that AI techniques have created tremendous value in revolutionizing the construction industry, leading to a more reliable, automated, self-modifying, time-saving, and cost-effective process of CEM. This book expects to (1) reveal complexities in conducting CEM activities and unlock the potential of incorporating AI in supporting and improving CEM;(2) display procedures and algorithms in a detailed manner on how to use AI to discover knowledge and address CEM challenges;(3) present evidence-based practices to