M. Z. Naser – författare

Interpretable Machine Learning for the Analysis, Design, Assessment, and Informed Decision Making for Civil Infrastructure highlights the growing trend of fostering machine learning to realize contemporary, smart, and safe infrastructure. This volume delves into the latest advancements in machine learning and artificial intelligence, providing readers with practical insights into their applications in the analysis, design, and assessment of civil infrastructure. From the innovative use of Generative Adversarial Networks in the design of shear wall structures to the application of deep learning for damage inspection of concrete structures, each chapter offers a unique perspective on the integration of cutting-edge technology in the field. Explore the potential of AI-driven fire safety design for smart buildings, the challenges and promises of large-scale evacuation modeling, and the use of machine learning classifiers for evaluating liquefaction potential. The book also features an in-depth discussion on explainable machine learning models for predicting the axial capacity of strengthened CFST columns and the development of spalling detection techniques using deep learning. Whether you are a civil engineer, researcher, or industry professional, this book is an invaluable resource that will equip you with the knowledge and tools to revolutionize civil infrastructure design and management. This book presents innovative research results supplemented with case studies from leading researchers in this dynamic and emerging field to be used as benchmarks to carry out future experiments and/or facilitate the development of future experiments and advanced numerical models. The book is delivered as a guide for a wide audience, including senior postgraduate students, academic and industrial researchers, materials scientists, and practicing engineers working in civil, environmental, and mechanical engineering.

Presents the fundamentals of AI/ML and how they can be applied in civil and environmental engineeringShares the latest advances in explainable and interpretable methods for AI/ML in the context of civil and environmental engineeringFocuses on civil and environmental engineering applications (day-to-day and extreme events) and features case studies and examples covering various aspects of applications

Machine learning (ML) is in constant transformation and various engineering disciplines are now heavily investing in it too. Currently, the majority of civil- and environmental-based works on ML are utilizing pure data-driven (i.e., black box) models built on correlations and associations. These models, however, do not truly identify the cause-effect relationship needed to answer questions such as: what caused a given structure to fail? Why does a particular construction material behave the way it does under specific conditions?

Causal Machine Learning in Civil and Environmental Engineering: Case Studies and Datasets aims to introduce causal ML approaches to civil and environmental engineering, covering theories, applications, as well as providing datasets, code, and examples of solutions to key problems in the sector. Students, academics, and engineering professionals both in the private and public sectors will find this book to be an invaluable reference source.Introduces causal ML from a civil and environmental engineering perspective, comprehensively covering both theory and step-by-step application proceduresIncludes flowcharts and examples for the successful adoption of causal ML to solve various engineering problemsProvides insight into not only the latest research developments, but also future implications of predictive science for engineeringIs accompanied by a website where all relevant datasets, algorithms, and code are hosted

Construction Materials and Their Properties for Fire Resistance and Insulation covers the properties of novel types of concrete and other more conventional building materials in fire scenarios. The volume also stands out as an invaluable reference resource for its relevance to varied audiences both in academia and industry, spanning materials science, civil and structural engineering, and fire safety engineering. Ensuring buildings are fire safe starts at the very beginning of planning new builds or renovations. Hence, it's essential, in fact, that the right materials are chosen not only according to their load-bearing capabilities but also their susceptibility to decay and fire resistance.Provides appropriate inputs for material behavior at elevated temperatures generated by fire, which is key to enable the assessment of various construction materials’ fire performanceIllustrates the effects of fire on materials’ performance to support decisions related to a building’s safe structural rehabilitationIncludes probabilistic models, numerical studies, and application cases

Accessible and practical framework for machine learning applications and solutions for civil and environmental engineers This textbook introduces engineers and engineering students to the applications of artificial intelligence (AI), machine learning (ML), and machine intelligence (MI) in relation to civil and environmental engineering projects and problems, presenting state-of-the-art methodologies and techniques to develop and implement algorithms in the engineering domain. Through real-world projects like analysis and design of structural members, optimizing concrete mixtures for site applications, examining concrete cracking via computer vision, evaluating the response of bridges to hazards, and predicating water quality and energy expenditure in buildings, this textbook offers readers in-depth case studies with solved problems that are commonly faced by civil and environmental engineers. The approaches presented range from simplified to advanced methods, incorporating coding-based and coding-free techniques. Professional engineers and engineering students will find value in the step-by-step examples that are accompanied by sample databases and codes for readers to practice with. Written by a highly qualified professional with significant experience in the field, Machine Learning includes valuable information on: The current state of machine learning and causality in civil and environmental engineering as viewed through a scientometrics analysis, plus a historical perspectiveSupervised vs. unsupervised learning for regression, classification, and clustering problemsExplainable and causal methods for practical engineering problemsDatabase development, outlining how an engineer can effectively collect and verify appropriate data to be used in machine intelligence analysisA framework for machine learning adoption and application, covering key questions commonly faced by practitionersThis textbook is a must-have reference for undergraduate/graduate students to learn concepts on the use of machine learning, for scientists/researchers to learn how to integrate machine learning into civil and environmental engineering, and for design/engineering professionals as a reference guide for undertaking MI design, simulation, and optimization for infrastructure.