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Author: M. Z. Naser
Publisher: John Wiley & Sons
ISBN: 1119897610
Category : Technology & Engineering
Languages : en
Pages : 610
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Book Description
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 perspective Supervised vs. unsupervised learning for regression, classification, and clustering problems Explainable and causal methods for practical engineering problems Database development, outlining how an engineer can effectively collect and verify appropriate data to be used in machine intelligence analysis A framework for machine learning adoption and application, covering key questions commonly faced by practitioners This 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.
Author: M. Z. Naser
Publisher: John Wiley & Sons
ISBN: 1119897610
Category : Technology & Engineering
Languages : en
Pages : 610
Get Book Here
Book Description
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 perspective Supervised vs. unsupervised learning for regression, classification, and clustering problems Explainable and causal methods for practical engineering problems Database development, outlining how an engineer can effectively collect and verify appropriate data to be used in machine intelligence analysis A framework for machine learning adoption and application, covering key questions commonly faced by practitioners This 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.
Author: James-A. Goulet
Publisher: MIT Press
ISBN: 0262538709
Category : Computers
Languages : en
Pages : 298
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Book Description
An introduction to key concepts and techniques in probabilistic machine learning for civil engineering students and professionals; with many step-by-step examples, illustrations, and exercises. This book introduces probabilistic machine learning concepts to civil engineering students and professionals, presenting key approaches and techniques in a way that is accessible to readers without a specialized background in statistics or computer science. It presents different methods clearly and directly, through step-by-step examples, illustrations, and exercises. Having mastered the material, readers will be able to understand the more advanced machine learning literature from which this book draws. The book presents key approaches in the three subfields of probabilistic machine learning: supervised learning, unsupervised learning, and reinforcement learning. It first covers the background knowledge required to understand machine learning, including linear algebra and probability theory. It goes on to present Bayesian estimation, which is behind the formulation of both supervised and unsupervised learning methods, and Markov chain Monte Carlo methods, which enable Bayesian estimation in certain complex cases. The book then covers approaches associated with supervised learning, including regression methods and classification methods, and notions associated with unsupervised learning, including clustering, dimensionality reduction, Bayesian networks, state-space models, and model calibration. Finally, the book introduces fundamental concepts of rational decisions in uncertain contexts and rational decision-making in uncertain and sequential contexts. Building on this, the book describes the basics of reinforcement learning, whereby a virtual agent learns how to make optimal decisions through trial and error while interacting with its environment.
Author: Taesam Lee
Publisher: Springer Nature
ISBN: 3030647773
Category : Science
Languages : en
Pages : 215
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Book Description
This book provides a step-by-step methodology and derivation of deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN), especially for estimating parameters, with back-propagation as well as examples with real datasets of hydrometeorology (e.g. streamflow and temperature) and environmental science (e.g. water quality). Deep learning is known as part of machine learning methodology based on the artificial neural network. Increasing data availability and computing power enhance applications of deep learning to hydrometeorological and environmental fields. However, books that specifically focus on applications to these fields are limited. Most of deep learning books demonstrate theoretical backgrounds and mathematics. However, examples with real data and step-by-step explanations to understand the algorithms in hydrometeorology and environmental science are very rare. This book focuses on the explanation of deep learning techniques and their applications to hydrometeorological and environmental studies with real hydrological and environmental data. This book covers the major deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN) as well as the conventional artificial neural network model.
Author: Thendiyath Roshni
Publisher: Elsevier
ISBN: 0323856993
Category : Technology & Engineering
Languages : en
Pages : 554
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Book Description
Risk, Reliability and Sustainable Remediation in the Field of Civil and Environmental Engineering illustrates the concepts of risk, reliability analysis, its estimation, and the decisions leading to sustainable development in the field of civil and environmental engineering. The book provides key ideas on risks in performance failure and structural failures of all processes involved in civil and environmental systems, evaluates reliability, and discusses the implications of measurable indicators of sustainability in important aspects of multitude of civil engineering projects. It will help practitioners become familiar with tolerances in design parameters, uncertainties in the environment, and applications in civil and environmental systems. Furthermore, the book emphasizes the importance of risks involved in design and planning stages and covers reliability techniques to discover and remove the potential failures to achieve a sustainable development. - Contains relevant theory and practice related to risk, reliability and sustainability in the field of civil and environment engineering - Gives firsthand experience of new tools to integrate existing artificial intelligence models with large information obtained from different sources - Provides engineering solutions that have a positive impact on sustainability
Author: Sybil Derrible
Publisher: MIT Press
ISBN: 0262356759
Category : Political Science
Languages : en
Pages : 657
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Book Description
A textbook that introduces integrated, sustainable design of urban infrastructures, drawing on civil engineering, environmental engineering, urban planning, electrical engineering, mechanical engineering, and computer science. This textbook introduces urban infrastructure from an engineering perspective, with an emphasis on sustainability. Bringing together both fundamental principles and practical knowledge from civil engineering, environmental engineering, urban planning, electrical engineering, mechanical engineering, and computer science, the book transcends disciplinary boundaries by viewing urban infrastructures as integrated networks. The text devotes a chapter to each of five engineering systems—electricity, water, transportation, buildings, and solid waste—covering such topics as fundamentals, demand, management, technology, and analytical models. Other chapters present a formal definition of sustainability; discuss population forecasting techniques; offer a history of urban planning, from the Neolithic era to Kevin Lynch and Jane Jacobs; define and discuss urban metabolism and infrastructure integration, reviewing system interdependencies; and describe approaches to urban design that draw on complexity theory, algorithmic models, and machine learning. Throughout, a hypothetical city state, Civitas, is used to explain and illustrate the concepts covered. Each chapter includes working examples and problem sets. An appendix offers tables, diagrams, and conversion factors. The book can be used in advanced undergraduate and graduate courses in civil engineering and as a reference for practitioners. It can also be helpful in preparation for the Fundamentals of Engineering (FE) and Principles and Practice of Engineering (PE) exams.
Author: Gebrail Bekdas
Publisher: Engineering Science Reference
ISBN: 9781799803027
Category : Artificial intelligence
Languages : en
Pages : 312
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Book Description
"This book examines the application of artificial intelligence and machine learning civil, mechanical, and industrial engineering"--
Author: M. Z. Naser
Publisher: Elsevier
ISBN: 0128240741
Category : Technology & Engineering
Languages : en
Pages : 300
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Book Description
The past few years have demonstrated how civil infrastructure continues to experience an unprecedented scale of extreme loading conditions (i.e. hurricanes, wildfires and earthquakes). Despite recent advancements in various civil engineering disciplines, specific to the analysis, design and assessment of structures, it is unfortunate that it is common nowadays to witness large scale damage in buildings, bridges and other infrastructure. The analysis, design and assessment of infrastructure comprises of a multitude of dimensions spanning a highly complex paradigm across material sciences, structural engineering, construction and planning among others. While traditional methods fall short of adequately accounting for such complexity, fortunately, computational intelligence presents novel solutions that can effectively tackle growing demands of intense extreme events and modern designs of infrastructure – especially in this era where infrastructure is reaching new heights and serving larger populations with high social awareness and expectations. Computational Intelligence for Analysis, Design and Assessment of Civil Infrastructure highlights the growing trend of fostering the use of CI to realize contemporary, smart and safe infrastructure. This is an emerging area that has not fully matured yet and hence the book will draw considerable interest and attention. In a sense, the book presents results of innovative efforts supplemented with case studies from leading researchers that can be used as benchmarks to carryout future experiments and/or facilitate development of future experiments and advanced numerical models. The book is written with the intention to serve as a guide for a wide audience including senior postgraduate students, academic and industrial researchers, materials scientists and practicing engineers working in civil, structural and mechanical engineering. - Presents the fundamentals of AI/ML and how they can be applied in civil and environmental engineering - Shares the latest advances in explainable and interpretable methods for AI/ML in the context of civil and environmental engineering - Focuses on civil and environmental engineering applications (day-to-day and extreme events) and features case studies and examples covering various aspects of applications
Author: Ni-Bin Chang
Publisher: CRC Press
ISBN: 1351650637
Category : Technology & Engineering
Languages : en
Pages : 627
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Book Description
In the last few years the scientific community has realized that obtaining a better understanding of interactions between natural systems and the man-made environment across different scales demands more research efforts in remote sensing. An integrated Earth system observatory that merges surface-based, air-borne, space-borne, and even underground sensors with comprehensive and predictive capabilities indicates promise for revolutionizing the study of global water, energy, and carbon cycles as well as land use and land cover changes. The aim of this book is to present a suite of relevant concepts, tools, and methods of integrated multisensor data fusion and machine learning technologies to promote environmental sustainability. The process of machine learning for intelligent feature extraction consists of regular, deep, and fast learning algorithms. The niche for integrating data fusion and machine learning for remote sensing rests upon the creation of a new scientific architecture in remote sensing science that is designed to support numerical as well as symbolic feature extraction managed by several cognitively oriented machine learning tasks at finer scales. By grouping a suite of satellites with similar nature in platform design, data merging may come to help for cloudy pixel reconstruction over the space domain or concatenation of time series images over the time domain, or even both simultaneously. Organized in 5 parts, from Fundamental Principles of Remote Sensing; Feature Extraction for Remote Sensing; Image and Data Fusion for Remote Sensing; Integrated Data Merging, Data Reconstruction, Data Fusion, and Machine Learning; to Remote Sensing for Environmental Decision Analysis, the book will be a useful reference for graduate students, academic scholars, and working professionals who are involved in the study of Earth systems and the environment for a sustainable future. The new knowledge in this book can be applied successfully in many areas of environmental science and engineering.
Author: Venkatesh Kodur
Publisher: McGraw Hill Professional
ISBN: 1260128598
Category : Technology & Engineering
Languages : en
Pages : 481
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Book Description
Actionable strategies for the design and construction of fire-resistant structures This hands-on guide clearly explains the complex building codes and standards that relate to fire design and presents hands-on techniques engineers can apply to prevent or mitigate the effects of fire in structures. Dedicated chapters discuss specific procedures for steel, concrete, and timber buildings. You will get step-by-step guidance on how to evaluate fire resistance using both testing and calculation methods. Structural Fire Engineering begins with an introduction to the behavioral aspects of fire and explains how structural materials react when exposed to elevated temperatures. From there, the book discusses the fire design aspects of key codes and standards, such as the International Building Code, the International Fire Code, and the NFPA Fire Code. Advanced topics are covered in complete detail, including residual capacity evaluation of fire damaged structures and fire design for bridges and tunnels. Explains the fire design requirements of the IBC, IFC, the NFPA Fire Code, and National Building Code of Canada Presents design strategies for steel, concrete, and timber structures as well as for bridges and tunnels Contains downloadable spreadsheets and problems along with solutions for instructors
Author: Paresh Chandra Deka
Publisher: CRC Press
ISBN: 042983666X
Category : Computers
Languages : en
Pages : 258
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Book Description
Machine learning has undergone rapid growth in diversification and practicality, and the repertoire of techniques has evolved and expanded. The aim of this book is to provide a broad overview of the available machine-learning techniques that can be utilized for solving civil engineering problems. The fundamentals of both theoretical and practical aspects are discussed in the domains of water resources/hydrological modeling, geotechnical engineering, construction engineering and management, and coastal/marine engineering. Complex civil engineering problems such as drought forecasting, river flow forecasting, modeling evaporation, estimation of dew point temperature, modeling compressive strength of concrete, ground water level forecasting, and significant wave height forecasting are also included. Features Exclusive information on machine learning and data analytics applications with respect to civil engineering Includes many machine learning techniques in numerous civil engineering disciplines Provides ideas on how and where to apply machine learning techniques for problem solving Covers water resources and hydrological modeling, geotechnical engineering, construction engineering and management, coastal and marine engineering, and geographical information systems Includes MATLAB® exercises
