1st Edition
Leveraging Artificial Intelligence in Engineering, Management, and Safety of Infrastructure
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
Biography
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.
