Machine Learning For Civil And Environmental Engineers

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Machine Learning for Civil and Environmental Engineers

Author : M. Z. Naser
Publisher : John Wiley & Sons
ISBN 13 : 1119897602
Total Pages : 610 pages
Book Rating : 4.1/5 (198 download)

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Book Synopsis Machine Learning for Civil and Environmental Engineers by : M. Z. Naser

Download or read book Machine Learning for Civil and Environmental Engineers written by M. Z. Naser and published by John Wiley & Sons. This book was released on 2021-08-10 with total page 610 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 Details 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.

Probabilistic Machine Learning for Civil Engineers

Author : James-A. Goulet
Publisher : MIT Press
ISBN 13 : 0262358018
Total Pages : 298 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Probabilistic Machine Learning for Civil Engineers by : James-A. Goulet

Download or read book Probabilistic Machine Learning for Civil Engineers written by James-A. Goulet and published by MIT Press. This book was released on 2020-03-16 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Interpretable Machine Learning for the Analysis, Design, Assessment, and Informed Decision Making for Civil Infrastructure

Author : M. Z. Naser
Publisher : Elsevier
ISBN 13 : 0128240741
Total Pages : 300 pages
Book Rating : 4.1/5 (282 download)

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Book Synopsis Interpretable Machine Learning for the Analysis, Design, Assessment, and Informed Decision Making for Civil Infrastructure by : M. Z. Naser

Download or read book Interpretable Machine Learning for the Analysis, Design, Assessment, and Informed Decision Making for Civil Infrastructure written by M. Z. Naser and published by Elsevier. This book was released on 2023-11-01 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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. Focuses on civil engineering applications for extreme events Explains the fundamentals of AI/ML and how they can be applied in civil engineering Features case study examples, design codes, and problems and solutions that would work for extreme events

Deep Learning for Hydrometeorology and Environmental Science

Author : Taesam Lee
Publisher : Springer Nature
ISBN 13 : 3030647773
Total Pages : 215 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis Deep Learning for Hydrometeorology and Environmental Science by : Taesam Lee

Download or read book Deep Learning for Hydrometeorology and Environmental Science written by Taesam Lee and published by Springer Nature. This book was released on 2021-01-27 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Probabilistic Machine Learning for Civil Engineers

Author : James-A. Goulet
Publisher : MIT Press
ISBN 13 : 0262538709
Total Pages : 298 pages
Book Rating : 4.2/5 (625 download)

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Book Synopsis Probabilistic Machine Learning for Civil Engineers by : James-A. Goulet

Download or read book Probabilistic Machine Learning for Civil Engineers written by James-A. Goulet and published by MIT Press. This book was released on 2020-04-14 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering

Author : Gebrail Bekdas
Publisher : Engineering Science Reference
ISBN 13 : 9781799803027
Total Pages : 312 pages
Book Rating : 4.8/5 (3 download)

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Book Synopsis Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering by : Gebrail Bekdas

Download or read book Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering written by Gebrail Bekdas and published by Engineering Science Reference. This book was released on 2019 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book examines the application of artificial intelligence and machine learning civil, mechanical, and industrial engineering"--

Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing

Author : Ni-Bin Chang
Publisher : CRC Press
ISBN 13 : 1498774342
Total Pages : 508 pages
Book Rating : 4.4/5 (987 download)

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Book Synopsis Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing by : Ni-Bin Chang

Download or read book Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing written by Ni-Bin Chang and published by CRC Press. This book was released on 2018-02-21 with total page 508 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Artificial Intelligence and Machine Learning Techniques for Civil Engineering

Author : Plevris, Vagelis
Publisher : IGI Global
ISBN 13 : 1668456443
Total Pages : 404 pages
Book Rating : 4.6/5 (684 download)

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Book Synopsis Artificial Intelligence and Machine Learning Techniques for Civil Engineering by : Plevris, Vagelis

Download or read book Artificial Intelligence and Machine Learning Techniques for Civil Engineering written by Plevris, Vagelis and published by IGI Global. This book was released on 2023-06-05 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, artificial intelligence (AI) has drawn significant attention with respect to its applications in several scientific fields, varying from big data handling to medical diagnosis. A tremendous transformation has taken place with the emerging application of AI. AI can provide a wide range of solutions to address many challenges in civil engineering. Artificial Intelligence and Machine Learning Techniques for Civil Engineering highlights the latest technologies and applications of AI in structural engineering, transportation engineering, geotechnical engineering, and more. It features a collection of innovative research on the methods and implementation of AI and machine learning in multiple facets of civil engineering. Covering topics such as damage inspection, safety risk management, and information modeling, this premier reference source is an essential resource for engineers, government officials, business leaders and executives, construction managers, students and faculty of higher education, librarians, researchers, and academicians.

Risk, Reliability and Sustainable Remediation in the Field of Civil and Environmental Engineering

Author : Thendiyath Roshni
Publisher : Elsevier
ISBN 13 : 0323856993
Total Pages : 554 pages
Book Rating : 4.3/5 (238 download)

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Book Synopsis Risk, Reliability and Sustainable Remediation in the Field of Civil and Environmental Engineering by : Thendiyath Roshni

Download or read book Risk, Reliability and Sustainable Remediation in the Field of Civil and Environmental Engineering written by Thendiyath Roshni and published by Elsevier. This book was released on 2022-03-22 with total page 554 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Site Assessment and Remediation for Environmental Engineers

Author : Cristiane Q. Surbeck
Publisher : CRC Press
ISBN 13 : 0429762976
Total Pages : 243 pages
Book Rating : 4.4/5 (297 download)

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Book Synopsis Site Assessment and Remediation for Environmental Engineers by : Cristiane Q. Surbeck

Download or read book Site Assessment and Remediation for Environmental Engineers written by Cristiane Q. Surbeck and published by CRC Press. This book was released on 2021-02-25 with total page 243 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Machine Learning in Earth, Environmental and Planetary Sciences

Author : Hossein Bonakdari
Publisher : Elsevier
ISBN 13 : 0443152853
Total Pages : 390 pages
Book Rating : 4.4/5 (431 download)

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Book Synopsis Machine Learning in Earth, Environmental and Planetary Sciences by : Hossein Bonakdari

Download or read book Machine Learning in Earth, Environmental and Planetary Sciences written by Hossein Bonakdari and published by Elsevier. This book was released on 2023-07-03 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning in Earth, Environmental and Planetary Sciences: Theoretical and Practical Applications is a practical guide on implementing different variety of extreme learning machine algorithms to Earth and environmental data. The book provides guided examples using real-world data for numerous novel and mathematically detailed machine learning techniques that can be applied in Earth, environmental, and planetary sciences, including detailed MATLAB coding coupled with line-by-line descriptions of the advantages and limitations of each method. The book also presents common postprocessing techniques required for correct data interpretation. This book provides students, academics, and researchers with detailed understanding of how machine learning algorithms can be applied to solve real case problems, how to prepare data, and how to interpret the results. Describes how to develop different schemes of machine learning techniques and apply to Earth, environmental and planetary data Provides detailed, guided line-by-line examples using real-world data, including the appropriate MATLAB codes Includes numerous figures, illustrations and tables to help readers better understand the concepts covered

Urban Engineering for Sustainability

Author : Sybil Derrible
Publisher : MIT Press
ISBN 13 : 0262356759
Total Pages : 657 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Urban Engineering for Sustainability by : Sybil Derrible

Download or read book Urban Engineering for Sustainability written by Sybil Derrible and published by MIT Press. This book was released on 2019-12-03 with total page 657 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Artificial Intelligence Applications for Sustainable Construction

Author : Moncef L. Nehdi
Publisher : Elsevier
ISBN 13 : 0443131929
Total Pages : 440 pages
Book Rating : 4.4/5 (431 download)

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Book Synopsis Artificial Intelligence Applications for Sustainable Construction by : Moncef L. Nehdi

Download or read book Artificial Intelligence Applications for Sustainable Construction written by Moncef L. Nehdi and published by Elsevier. This book was released on 2024-02-13 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence Applications for Sustainable Construction presents the latest developments in AI and ML technologies applied to real-world civil engineering concerns. With an increasing amount of attention on the environmental impact of every industry, more construction projects are going to require sustainable construction practices. This volume offers research evidence, simulation results, and case studies to support this change. Sustainable construction, in fact, not only uses renewable and recyclable materials when building new structures or repairing deteriorating ones, but also adopts all possible methods to reduce energy consumption and waste. The concisely written but comprehensive, practical knowledge put forward by this international group of highly specialized editors and contributors will prove to be beneficial to engineering students and professionals alike. Presents convincing “success stories that encourage application of AI-powered tools to civil engineering Provides a wealth of valuable technical information to address and resolve many challenging construction problems Illustrates the most recent shifts in thinking and practice for sustainable construction

A Primer on Machine Learning Applications in Civil Engineering

Author : Paresh Chandra Deka
Publisher : CRC Press
ISBN 13 : 0429836651
Total Pages : 201 pages
Book Rating : 4.4/5 (298 download)

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Book Synopsis A Primer on Machine Learning Applications in Civil Engineering by : Paresh Chandra Deka

Download or read book A Primer on Machine Learning Applications in Civil Engineering written by Paresh Chandra Deka and published by CRC Press. This book was released on 2019-10-28 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Machine Learning Applications in Civil Engineering

Author : Kundan Meshram
Publisher : Elsevier
ISBN 13 : 0443153639
Total Pages : 220 pages
Book Rating : 4.4/5 (431 download)

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Book Synopsis Machine Learning Applications in Civil Engineering by : Kundan Meshram

Download or read book Machine Learning Applications in Civil Engineering written by Kundan Meshram and published by Elsevier. This book was released on 2023-10-01 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning Applications in Civil Engineering discusses machine learning and deep learning models for different civil engineering applications. These models work for stochastic methods wherein internal processing is done using randomized prototypes. The book explains various machine learning model designs that will assist researchers to design multi domain systems with maximum efficiency. It introduces Machine Learning and its applications to different Civil Engineering tasks, including Basic Machine Learning Models for data pre-processing, models for data representation, classification models for Civil Engineering Applications, Bioinspired Computing models for Civil Engineering, and their case studies. Using this book, civil engineering students and researchers can deep dive into Machine Learning, and identify various solutions to practical Civil Engineering tasks. Introduces various ML models for Civil Engineering Applications that will assist readers in their analysis of design and development interfaces for building these applications Reviews different lacunas and challenges in current models used for Civil Engineering scenarios Explores designs for customized components for optimum system deployment Explains various machine learning model designs that will assist researchers to design multi domain systems with maximum efficiency

Computing in Civil Engineering

Author : Ioannis Brilakis
Publisher : ASCE Publications
ISBN 13 : 9780784413029
Total Pages : 904 pages
Book Rating : 4.4/5 (13 download)

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Book Synopsis Computing in Civil Engineering by : Ioannis Brilakis

Download or read book Computing in Civil Engineering written by Ioannis Brilakis and published by ASCE Publications. This book was released on 2013 with total page 904 pages. Available in PDF, EPUB and Kindle. Book excerpt: Proceedings of the 2013 ASCE International Workshop on Computing in Civil Engineering.

Civil and Environmental Engineering

Author : Information Resources Management Association
Publisher :
ISBN 13 : 9781466696198
Total Pages : 0 pages
Book Rating : 4.6/5 (961 download)

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Book Synopsis Civil and Environmental Engineering by : Information Resources Management Association

Download or read book Civil and Environmental Engineering written by Information Resources Management Association and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a comprehensive multi-volume publication showcasing the best research on topics pertaining to road design, building maintenance and construction, transportation, earthquake engineering, waste and pollution management, and water resources management and engineering. It offers extensive coverage of crucial concepts in the field of civil engineering and environmental engineering.