1st Edition

Leveraging Artificial Intelligence in Engineering, Management, and Safety of Infrastructure

Edited By M.Z. Naser Copyright 2023
    458 Pages 8 Color & 186 B/W Illustrations
    by CRC Press

    458 Pages 8 Color & 186 B/W Illustrations
    by CRC Press

    The design, construction, and upkeep of infrastructure is comprised of a multitude of dimensions spanning a highly complex paradigm of interconnected opportunities and challenges. While traditional methods fall short of adequately accounting for such complexity, artificial intelligence (AI) presents novel and out-of-the-box solutions that effectively tackle the growing demands of our infrastructure. The convergence between AI and civil engineering is an emerging frontier with tremendous potential.

    The book is likely to provide a boost to the state of infrastructure engineering by fostering a new look at civil engineering that capitalizes on AI as its main driver. It highlights the ongoing push to adopt and leverage AI to realize contemporary, intelligent, safe, and resilient infrastructure. The book comprises interdisciplinary and novel works from across the globe. It presents findings from innovative efforts supplemented with physical tests, numerical simulations, and case studies – all of which can be used as benchmarks to carry out future experiments and/or facilitate the development of future AI models in structural engineering, traffic engineering, construction engineering, and construction materials.

    The book will serve as a guide for a wide range of audiences, including senior undergraduate and graduate students, professionals, and government officials of civil, traffic, and computer engineering backgrounds, as well as for those engaged in urban planning and human sciences.

    Chapter 1: Convolutional Neural Networks and Applications on Civil Infrastructure

    Onur Avci, Osama Abdeljaber, Serkan Kiranyaz, Turker Ince, and Daniel J. Inman

    Chapter 2: Identifying Non-linearity in Construction Workers' Personality: Safety Behaviour Predictive Relationship Using Neural Network and Linear Regression Modelling

    Yifan Gao, Vicente A. González, Tak Wing Yiu, and Guillermo Cabrera-Guerrero

    Chapter 3: Machine Learning Framework for Predicting Failure Mode and Flexural capacity of Frp-Reinforced Beams

    Ahmad N. Tarawneh, and Eman F. Saleh

    Chapter 4: A Novel Formulation for Estimating Compressive Strength of High Performance Concrete Using Gene Expression Programming

    Iman Mansouri, Jale Tezcan, and Paul O. Awoyera

    Chapter 5: Implementation of Data-Driven Approaches for Condition Assessment of Structures and Analyzing Complex Data

    Vafa Soltangharaei, Li Ai, and Paul Ziehl

    Chapter 6: Automatic Detection of Surface Thermal Cracks in Structural Concrete with Numerical Correlation Analysis

    Diana Andrushia, Anand N, Richard Walls, Daniel Paul T, and Prince Arulraj

    Chapter 7: State-of-the-Art Research in the Area of Artificial Intelligence with Specific Consideration to Civil Infrastructure, Construction Engineering and Management, and Safety

    Islam H. El-adaway and Rayan H. Assaad

    Chapter 8: Artificial Intelligence in Concrete Materials: A Scientometric View

    Zhanzhao Li and Aleksandra Radlińska

    Chapter 9: Active Learning Kriging-Based Reliability for Assessing the Safety of Structures: Theory and Application

    Koosha Khorramian, and Fadi Oudah

    Chapter 10: A Bayesian Estimation Technique for Multilevel Damage Classification in DBHM

    William Lockea, Stefani Mokalledb, Omar Abuodehc, Laura Redmondd, and Christopher McMahane

    Chapter 11: Machine Learning and IoT Data for Concrete Performance Testing and Analysis

    Andrew Fahim, Tahmid Mehdi, Ali Taheri, Pouria Ghods, Aali Alizadeh and Sarah De Carufel

    Chapter 12: Knowledge-enhanced Deep Learning for Efficient Response Estimation of Nonlinear Structures

    Haifeng Wang and Teng Wu

    Chapter 13: Damage Detection in Reinforced Concrete Girders by Finite Element and Artificial Intelligence Synergy

    Hayder A. Rasheed, Ahmed Al-Rahmani, and AlaaEldin Abouelleil

    Chapter 14: Deep Learning in Transportation Cyber-Physical Systems

    Zadid Khan, Sakib Mahmud Khan, Mizanur Rahman, Mhafuzul Islam, and Mashrur Chowdhury

    Chapter 15: Artificial Intelligence in the Construction Industry: Theory and Emerging Applications for the Future of Work

    Amir H. Behzadan, Nipun D. Nath, and Reza Akhavian

    Chapter 16: The Use of Machine Learning in Heat Transfer Analysis for Structural Fire Engineering Applications

    Yavor Panev, Tom Parker and Panagiotis Kotsovinos

    Chapter 17: Using Artificial Intelligence to Derive Temperature-Dependent Mechanical Properties of Ultra-High Performance Concrete

    Srishti Banerji

    Chapter 18: Smart Tunnel Fire Safety Management by Sensor Network and Artificial Intelligence

    Xinyan Huang, Xiqiang Wu, Xiaoning Zhang and Asif Usmani


    M.Z. Naser is a tenure-track faculty member at the School of Civil and Environmental Engineering & Earth Sciences, a member of the AI Research Institute for Science and Engineering (AIRISE) at Clemson University, USA. Dr. Naser has co-authored over 100 publications and has 10 years of experience in structural engineering and AI. His research interest spans causal & explainable AI methodologies to discover new knowledge hidden within the domains of structural & fire engineering and materials science to realize functional, sustainable, and resilient infrastructure. He is a registered professional engineer and a member of various international editorial boards and building committees.