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Author: M. Z. Naser Publisher: John Wiley & Sons ISBN: 1119897610 Category : Technology & Engineering Languages : en Pages : 610
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
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
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
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
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
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: Cristiane Q. Surbeck Publisher: CRC Press ISBN: 0429762984 Category : Science Languages : en Pages : 323
Book Description
This book serves as a primary textbook for environmental site investigation and remediation of subsurface soil and groundwater. It introduces concepts and principles of field investigative techniques to adequately determine the extent of contamination in the subsurface for the selection of cleanup alternatives. It then focuses on practical calculations and skills needed to design and operate remediation systems that will both educate students and be useful for entry-level professionals in the field. Features: • Examines the practical aspects of investigating and cleaning up contaminated soil and groundwater • Contains scenarios, illustrations, equations, and example problems with discussions that illustrate various practical situations and interpret the results • Includes end-of-chapter problems to reinforce student learning • Provides a regulatory and risk analysis context, as well as public and community involvement aspects • Discusses sustainability and performance assessment of the remediation methods presented Site Assessment and Remediation for Environmental Engineers provides upper-level undergraduate and graduate students with practical, project-oriented knowledge of how to investigate and clean up a site contaminated with chemicals and hazardous waste.
Author: M. Z. Naser Publisher: Elsevier ISBN: 0128240741 Category : Technology & Engineering Languages : en Pages : 300
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: Limao Zhang Publisher: Springer Nature ISBN: 9811628424 Category : Technology & Engineering Languages : en Pages : 271
Book Description
This book highlights the latest technologies and applications of Artificial Intelligence (AI) in the domain of construction engineering and management. 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. AI works by combining large amounts of data with fast, iterative processing, and intelligent algorithms (e.g., 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 knowledge discovery, risk estimates, root cause analysis, damage assessment and prediction, and defect detection. A tremendous transformation has taken place in the past years with the emerging applications of AI. 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.
Author: Ni-Bin Chang Publisher: CRC Press ISBN: 1351650637 Category : Technology & Engineering Languages : en Pages : 627
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: M. Z. Naser
Author: M. Z. Naser
Author: James-A. Goulet
Author: Taesam Lee
Author: Thendiyath Roshni
Author: Sybil Derrible
Author: Cristiane Q. Surbeck
Author: M. Z. Naser
Author: Gebrail Bekdas
Author: Limao Zhang
Author: Ni-Bin Chang