A Primer on Machine Learning Applications in Civil Engineering
A Primer on Machine Learning Applications in Civil Engineering

Author: Paresh Chandra Deka

Publisher: CRC Press

Published: 2019-10-28

Total Pages: 211

ISBN-13: 0429836651

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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

Foundations of Data Science for Engineering Problem Solving
Foundations of Data Science for Engineering Problem Solving

Author: Parikshit Narendra Mahalle

Publisher: Springer Nature

Published: 2021-08-21

Total Pages: 125

ISBN-13: 9811651604

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This book is one-stop shop which offers essential information one must know and can implement in real-time business expansions to solve engineering problems in various disciplines. It will also help us to make future predictions and decisions using AI algorithms for engineering problems. Machine learning and optimizing techniques provide strong insights into novice users. In the era of big data, there is a need to deal with data science problems in multidisciplinary perspective. In the real world, data comes from various use cases, and there is a need of source specific data science models. Information is drawn from various platforms, channels, and sectors including web-based media, online business locales, medical services studies, and Internet. To understand the trends in the market, data science can take us through various scenarios. It takes help of artificial intelligence and machine learning techniques to design and optimize the algorithms. Big data modelling and visualization techniques of collected data play a vital role in the field of data science. This book targets the researchers from areas of artificial intelligence, machine learning, data science and big data analytics to look for new techniques in business analytics and applications of artificial intelligence in recent businesses.

Metaheuristic and Machine Learning Optimization Strategies for Complex Systems
Metaheuristic and Machine Learning Optimization Strategies for Complex Systems

Author: R., Thanigaivelan

Publisher: IGI Global

Published: 2024-07-17

Total Pages: 423

ISBN-13:

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In contemporary engineering domains, optimization and decision-making issues are crucial. Given the vast amounts of available data, processing times and memory usage can be substantial. Developing and implementing novel heuristic algorithms is time-consuming, yet even minor improvements in solutions can significantly reduce computational costs. In such scenarios, the creation of heuristics and metaheuristic algorithms has proven advantageous. The convergence of machine learning and metaheuristic algorithms offers a promising approach to address these challenges. Metaheuristic and Machine Learning Optimization Strategies for Complex Systems covers all areas of comprehensive information about hyper-heuristic models, hybrid meta-heuristic models, nature-inspired computing models, and meta-heuristic models. The key contribution of this book is the construction of a hyper-heuristic approach for any general problem domain from a meta-heuristic algorithm. Covering topics such as cloud computing, internet of things, and performance evaluation, this book is an essential resource for researchers, postgraduate students, educators, data scientists, machine learning engineers, software developers and engineers, policy makers, and more.

Climate Change and Water Security
Climate Change and Water Security

Author: Sreevalsa Kolathayar

Publisher: Springer Nature

Published: 2021-11-18

Total Pages: 516

ISBN-13: 9811655014

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This book presents the select proceedings of the Virtual Conference on Disaster Risk Reduction (VCDRR 2021). It emphasizes on the role of civil engineering for a disaster resilient society. It presents latest research on climate change and water security focusing on disaster risk reduction. Various topics covered in this book are climate change, stormwater management, flood risk analysis, drought management, water treatment, etc. This book is a comprehensive volume on disaster risk reduction (DRR) and its management for a sustainable built environment. This book is useful for the students, researchers, policy makers and professionals working in the area of civil engineering, climate change and disaster management.

Machine Learning for Civil and Environmental Engineers
Machine Learning for Civil and Environmental Engineers

Author: M. Z. Naser

Publisher: John Wiley & Sons

Published: 2023-07-17

Total Pages: 610

ISBN-13: 1119897610

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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.

Practical Knowledge-Based Systems in Conceptual Design
Practical Knowledge-Based Systems in Conceptual Design

Author: John C. Miles

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 254

ISBN-13: 1447120426

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Conceptual Design is one of the few areas of Engineering Design where computers have yet to make an impact. With the development of Knowledge Based Systems it is now possible to rectify this situation. This publication deals with the use of Knowledge Based Systems (KBS) as tools for conceptual design. Included are neglected aspects such as evaluation and user needs. Practical Knowledge Based Systems in Conceptual Design is based on the authors' experience of developing KBS for use in civil engineering, an area of industrial application which is recognised as being one of great potential. The methodology has been tried and tested by designers. Examples of systems which have been developed to solve specific design problems are included.

Machine Learning Applications in Civil Engineering
Machine Learning Applications in Civil Engineering

Author: Kundan Meshram

Publisher: Elsevier

Published: 2023-09-29

Total Pages: 220

ISBN-13: 0443153639

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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

Advances in Machine Learning Research and Application: 2011 Edition
Advances in Machine Learning Research and Application: 2011 Edition

Author:

Publisher: ScholarlyEditions

Published: 2012-01-09

Total Pages: 935

ISBN-13: 1464920877

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Advances in Machine Learning Research and Application: 2011 Edition is a ScholarlyEditions™ eBook that delivers timely, authoritative, and comprehensive information about Machine Learning. The editors have built Advances in Machine Learning Research and Application: 2011 Edition on the vast information databases of ScholarlyNews.™ You can expect the information about Machine Learning in this eBook to be deeper than what you can access anywhere else, as well as consistently reliable, authoritative, informed, and relevant. The content of Advances in Machine Learning Research and Application: 2011 Edition has been produced by the world’s leading scientists, engineers, analysts, research institutions, and companies. All of the content is from peer-reviewed sources, and all of it is written, assembled, and edited by the editors at ScholarlyEditions™ and available exclusively from us. You now have a source you can cite with authority, confidence, and credibility. More information is available at http://www.ScholarlyEditions.com/.

Proceedings of the Canadian Society of Civil Engineering Annual Conference 2021
Proceedings of the Canadian Society of Civil Engineering Annual Conference 2021

Author: Scott Walbridge

Publisher: Springer Nature

Published: 2022-04-13

Total Pages: 583

ISBN-13: 9811906564

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This book comprises the proceedings of the Annual Conference of the Canadian Society of Civil Engineering 2021. The contents of this volume focus on specialty conferences in construction, environmental, hydrotechnical, materials, structures, transportation engineering, etc. This volume will prove a valuable resource for those in academia and industry.