Handbook of Neural Computation explores neural computation applications, ranging from conventional fields of mechanical and civil engineering, to electronics, electrical engineering and computer science. This book covers the numerous applications of artificial and deep neural networks and their uses in learning machines, including image and speech recognition, natural language processing and risk analysis. Edited by renowned authorities in this field, this work is comprised of articles from reputable industry and academic scholars and experts from around the world.

Each contributor presents a specific research issue with its recent and future trends. As the demand rises in the engineering and medical industries for neural networks and other machine learning methods to solve different types of operations, such as data prediction, classification of images, analysis of big data, and intelligent decision-making, this book provides readers with the latest, cutting-edge research in one comprehensive text.

Features high-quality research articles on multivariate adaptive regression splines, the minimax probability machine, and more Discusses machine learning techniques, including classification, clustering, regression, web mining, information retrieval and natural language processing Covers supervised, unsupervised, reinforced, ensemble, and nature-inspired learning methods

Handbook of Neural Computation explores neural computation applications, ranging from conventional fields of mechanical and civil engineering, to electronics, electrical engineering and computer science. This book covers the numerous applications of artificial and deep neural networks and their uses in learning machines, including image and speech recognition, natural language processing and risk analysis. Edited by renowned authorities in this field, this work is comprised of articles from reputable industry and academic scholars and experts from around the world.Each contributor presents a specific research issue with its recent and future trends. As the demand rises in the engineering and medical industries for neural networks and other machine learning methods to solve different types of operations, such as data prediction, classification of images, analysis of big data, and intelligent decision-making, this book provides readers with the latest, cutting-edge research in one comprehensive text.- Features high-quality research articles on multivariate adaptive regression splines, the minimax probability machine, and more- Discusses machine learning techniques, including classification, clustering, regression, web mining, information retrieval and natural language processing- Covers supervised, unsupervised, reinforced, ensemble, and nature-inspired learning methods

Integrated Disaster Science and Management: Global Case Studies in Mitigation and Recovery bridges the gap between scientific research on natural disasters and the practice of disaster management. It examines natural hazards, including earthquakes, landslides and tsunamis, and uses integrated disaster management techniques, quantitative methods and big data analytics to create early warning models to mitigate impacts of these hazards and reduce the risk of disaster. It also looks at mitigation as part of the recovery process after a disaster, as in the case of the Nepal earthquake.

Edited by global experts in disaster management and engineering, the book offers case studies that focus on the critical phases of disaster management.

Identifies advanced techniques and models based on natural disaster science for forecasting disasters and analyzing risk Offers a holistic approach to the problem of disaster management, including preparation, recovery, and resilience Includes coverage of social, economic, and environmental impacts on disasters

Integrated Disaster Science and Management: Global Case Studies in Mitigation and Recovery bridges the gap between scientific research on natural disasters and the practice of disaster management. It examines natural hazards, including earthquakes, landslides and tsunamis, and uses integrated disaster management techniques, quantitative methods and big data analytics to create early warning models to mitigate impacts of these hazards and reduce the risk of disaster. It also looks at mitigation as part of the recovery process after a disaster, as in the case of the Nepal earthquake.Edited by global experts in disaster management and engineering, the book offers case studies that focus on the critical phases of disaster management.- Identifies advanced techniques and models based on natural disaster science for forecasting disasters and analyzing risk- Offers a holistic approach to the problem of disaster management, including preparation, recovery, and resilience- Includes coverage of social, economic, and environmental impacts on disasters

Handbook of Probabilistic Models carefully examines the application of advanced probabilistic models in conventional engineering fields. In this comprehensive handbook, practitioners, researchers and scientists will find detailed explanations of technical concepts, applications of the proposed methods, and the respective scientific approaches needed to solve the problem. This book provides an interdisciplinary approach that creates advanced probabilistic models for engineering fields, ranging from conventional fields of mechanical engineering and civil engineering, to electronics, electrical, earth sciences, climate, agriculture, water resource, mathematical sciences and computer sciences.

Specific topics covered include minimax probability machine regression, stochastic finite element method, relevance vector machine, logistic regression, Monte Carlo simulations, random matrix, Gaussian process regression, Kalman filter, stochastic optimization, maximum likelihood, Bayesian inference, Bayesian update, kriging, copula-statistical models, and more.

Explains the application of advanced probabilistic models encompassing multidisciplinary research Applies probabilistic modeling to emerging areas in engineering Provides an interdisciplinary approach to probabilistic models and their applications, thus solving a wide range of practical problems

Handbook of Probabilistic Models carefully examines the application of advanced probabilistic models in conventional engineering fields. In this comprehensive handbook, practitioners, researchers and scientists will find detailed explanations of technical concepts, applications of the proposed methods, and the respective scientific approaches needed to solve the problem. This book provides an interdisciplinary approach that creates advanced probabilistic models for engineering fields, ranging from conventional fields of mechanical engineering and civil engineering, to electronics, electrical, earth sciences, climate, agriculture, water resource, mathematical sciences and computer sciences.Specific topics covered include minimax probability machine regression, stochastic finite element method, relevance vector machine, logistic regression, Monte Carlo simulations, random matrix, Gaussian process regression, Kalman filter, stochastic optimization, maximum likelihood, Bayesian inference, Bayesian update, kriging, copula-statistical models, and more.- Explains the application of advanced probabilistic models encompassing multidisciplinary research- Applies probabilistic modeling to emerging areas in engineering- Provides an interdisciplinary approach to probabilistic models and their applications, thus solving a wide range of practical problems

Predictive Modeling for Energy Management and Power Systems Engineering introduces readers to the cutting-edge use of big data and large computational infrastructures in energy demand estimation and power management systems. The book supports engineers and scientists who seek to become familiar with advanced optimization techniques for power systems designs, optimization techniques and algorithms for consumer power management, and potential applications of machine learning and artificial intelligence in this field. The book provides modeling theory in an easy-to-read format, verified with on-site models and case studies for specific geographic regions and complex consumer markets. Presents advanced optimization techniques to improve existing energy demand system Provides data-analytic models and their practical relevance in proven case studies Explores novel developments in machine-learning and artificial intelligence applied in energy management Provides modeling theory in an easy-to-read format

Predictive Modeling for Energy Management and Power Systems Engineering introduces readers to the cutting-edge use of big data and large computational infrastructures in energy demand estimation and power management systems. The book supports engineers and scientists who seek to become familiar with advanced optimization techniques for power systems designs, optimization techniques and algorithms for consumer power management, and potential applications of machine learning and artificial intelligence in this field. The book provides modeling theory in an easy-to-read format, verified with on-site models and case studies for specific geographic regions and complex consumer markets.- Presents advanced optimization techniques to improve existing energy demand system- Provides data-analytic models and their practical relevance in proven case studies- Explores novel developments in machine-learning and artificial intelligence applied in energy management- Provides modeling theory in an easy-to-read format

New Materials in Civil Engineering provides engineers and scientists with the tools and methods needed to meet the challenge of designing and constructing more resilient and sustainable infrastructures. This book is a valuable guide to the properties, selection criteria, products, applications, lifecycle and recyclability of advanced materials. It presents an A-to-Z approach to all types of materials, highlighting their key performance properties, principal characteristics and applications. Traditional materials covered include concrete, soil, steel, timber, fly ash, geosynthetic, fiber-reinforced concrete, smart materials, carbon fiber and reinforced polymers. In addition, the book covers nanotechnology and biotechnology in the development of new materials.

Covers a variety of materials, including fly ash, geosynthetic, fiber-reinforced concrete, smart materials, carbon fiber reinforced polymer and waste materials Provides a "one-stop� resource of information for the latest materials and practical applications Includes a variety of different use case studies

New Materials in Civil Engineering provides engineers and scientists with the tools and methods needed to meet the challenge of designing and constructing more resilient and sustainable infrastructures. This book is a valuable guide to the properties, selection criteria, products, applications, lifecycle and recyclability of advanced materials. It presents an A-to-Z approach to all types of materials, highlighting their key performance properties, principal characteristics and applications. Traditional materials covered include concrete, soil, steel, timber, fly ash, geosynthetic, fiber-reinforced concrete, smart materials, carbon fiber and reinforced polymers. In addition, the book covers nanotechnology and biotechnology in the development of new materials.- Covers a variety of materials, including fly ash, geosynthetic, fiber-reinforced concrete, smart materials, carbon fiber reinforced polymer and waste materials- Provides a "one-stop resource of information for the latest materials and practical applications- Includes a variety of different use case studies

Data Analytics in Biomedical Engineering and Healthcare explores key applications using data analytics, machine learning, and deep learning in health sciences and biomedical data. The book is useful for those working with big data analytics in biomedical research, medical industries, and medical research scientists. The book covers health analytics, data science, and machine and deep learning applications for biomedical data, covering areas such as predictive health analysis, electronic health records, medical image analysis, computational drug discovery, and genome structure prediction using predictive modeling. Case studies demonstrate big data applications in healthcare using the MapReduce and Hadoop frameworks. Examines the development and application of data analytics applications in biomedical data Presents innovative classification and regression models for predicting various diseases Discusses genome structure prediction using predictive modeling Shows readers how to develop clinical decision support systems Shows researchers and specialists how to use hybrid learning for better medical diagnosis, including case studies of healthcare applications using the MapReduce and Hadoop frameworks

Data Analytics in Biomedical Engineering and Healthcare explores key applications using data analytics, machine learning, and deep learning in health sciences and biomedical data. The book is useful for those working with big data analytics in biomedical research, medical industries, and medical research scientists. The book covers health analytics, data science, and machine and deep learning applications for biomedical data, covering areas such as predictive health analysis, electronic health records, medical image analysis, computational drug discovery, and genome structure prediction using predictive modeling. Case studies demonstrate big data applications in healthcare using the MapReduce and Hadoop frameworks.- Examines the development and application of data analytics applications in biomedical data- Presents innovative classification and regression models for predicting various diseases- Discusses genome structure prediction using predictive modeling- Shows readers how to develop clinical decision support systems- Shows researchers and specialists how to use hybrid learning for better medical diagnosis, including case studies of healthcare applications using the MapReduce and Hadoop frameworks

Basics of Computational Geophysics provides a one-stop, collective resource for practitioners on the different techniques and models in geoscience, their practical applications, and case studies. The reference provides the modeling theory in an easy-to-read format that is verified with onsite models for specific regions and scenarios, including the use of big data and artificial intelligence. This book offers a platform whereby readers will learn theory, practical applications, and the comparison of real-world problems surrounding geomechanics, modeling and optimizations. Covers various advanced computational techniques for solving different problems in geophysics, including the use of Big Data and artificial intelligence Includes case studies that provide examples surrounding practical applications Provides an assessment of the capabilities of commercial software

Water Engineering Modeling and Mathematic Tools provides an informative resource for practitioners who want to learn more about different techniques and models in water engineering and their practical applications and case studies. The book provides modelling theories in an easy-to-read format verified with on-site models for specific regions and scenarios. Users will find this to be a significant contribution to the development of mathematical tools, experimental techniques, and data-driven models that support modern-day water engineering applications. Civil engineers, industrialists, and water management experts should be familiar with advanced techniques that can be used to improve existing systems in water engineering.

This book provides key ideas on recently developed machine learning methods and AI modelling. It will serve as a common platform for practitioners who need to become familiar with the latest developments of computational techniques in water engineering.

Includes firsthand experience about artificial intelligence models, utilizing case studies Describes biological, physical and chemical techniques for the treatment of surface water, groundwater, sea water and rain/snow Presents the application of new instruments in water engineering

Water Engineering Modeling and Mathematic Tools provides an informative resource for practitioners who want to learn more about different techniques and models in water engineering and their practical applications and case studies. The book provides modelling theories in an easy-to-read format verified with on-site models for specific regions and scenarios. Users will find this to be a significant contribution to the development of mathematical tools, experimental techniques, and data-driven models that support modern-day water engineering applications. Civil engineers, industrialists, and water management experts should be familiar with advanced techniques that can be used to improve existing systems in water engineering.This book provides key ideas on recently developed machine learning methods and AI modelling. It will serve as a common platform for practitioners who need to become familiar with the latest developments of computational techniques in water engineering.- Includes firsthand experience about artificial intelligence models, utilizing case studies- Describes biological, physical and chemical techniques for the treatment of surface water, groundwater, sea water and rain/snow- Presents the application of new instruments in water engineering

Basics of Computational Geophysics provides a one-stop, collective resource for practitioners on the different techniques and models in geoscience, their practical applications, and case studies. The reference provides the modeling theory in an easy-to-read format that is verified with onsite models for specific regions and scenarios, including the use of big data and artificial intelligence. This book offers a platform whereby readers will learn theory, practical applications, and the comparison of real-world problems surrounding geomechanics, modeling and optimizations.- Covers various advanced computational techniques for solving different problems in geophysics, including the use of Big Data and artificial intelligence- Includes case studies that provide examples surrounding practical applications- Provides an assessment of the capabilities of commercial software

Modeling in Geotechnical Engineering is a one stop reference for a range of computational models, the theory explaining how they work, and case studies describing how to apply them. Drawing on the expertise of contributors from a range of disciplines including geomechanics, optimization, and computational engineering, this book provides an interdisciplinary guide to this subject which is suitable for readers from a range of backgrounds.

Before tackling the computational approaches, a theoretical understanding of the physical systems is provided that helps readers to fully grasp the significance of the numerical methods. The various models are presented in detail, and advice is provided on how to select the correct model for your application.

Provides detailed descriptions of different computational modelling methods for geotechnical applications, including the finite element method, the finite difference method, and the boundary element method Gives readers the latest advice on the use of big data analytics and artificial intelligence in geotechnical engineering Includes case studies to help readers apply the methods described in their own work

Modeling in Geotechnical Engineering is a one stop reference for a range of computational models, the theory explaining how they work, and case studies describing how to apply them. Drawing on the expertise of contributors from a range of disciplines including geomechanics, optimization, and computational engineering, this book provides an interdisciplinary guide to this subject which is suitable for readers from a range of backgrounds.Before tackling the computational approaches, a theoretical understanding of the physical systems is provided that helps readers to fully grasp the significance of the numerical methods. The various models are presented in detail, and advice is provided on how to select the correct model for your application.- Provides detailed descriptions of different computational modelling methods for geotechnical applications, including the finite element method, the finite difference method, and the boundary element method- Gives readers the latest advice on the use of big data analytics and artificial intelligence in geotechnical engineering- Includes case studies to help readers apply the methods described in their own work

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

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

Green Materials in Civil Engineering provides a comprehensive resource for practitioners to learn more about the utilization of these materials in civil engineering, as well as their practical applications. Novel green materials such as fly ash, slag, fiber-reinforced concrete and soil, smart materials, biological materials, and waste materials such as mineral wastes, building and demolition waste, recycled asphalt, coal gangue and industrial by-products are discussed in detail. Emphasis is placed on understanding the qualities, selection criteria, products and applications, durability, life cycle, and recyclability of these materials.The book will be a valuable reference resource for academic and industrial researchers, materials scientists, and civil engineers who are working in the development of construction materials, utilization of waste, and other fine by-products in the production of concrete and other construction materials.Provides an up-to-date and comprehensive resource on the use of green materials in civil engineeringCovers green concrete, agricultural waste, industrial by-products, biological and waste materials such as smart materials, microbially generated calcium precipitation, recycled asphalt and natural fibersDiscusses selection criteria, durability, lifecycle, recyclability, and regulatory measures

Green Materials in Civil Engineering provides a comprehensive resource for practitioners to learn more about the utilization of these materials in civil engineering, as well as their practical applications. Novel green materials such as fly ash, slag, fiber-reinforced concrete and soil, smart materials, carbon fibre reinforced polymers, waste materials, biological materials, and waste materials such as building and demolition waste, recycled asphalt, and industrial by-products are discussed in detail. Emphasis is placed on understanding the qualities, selection criteria, products and applications, durability, life cycle, and recyclability of these materials.The book will be a valuable reference resource for academic and industrial researchers, materials scientists and civil engineers who are working in the development of construction materials and utilization of waste and other fine by-products in the production of concrete and other construction materials.- Provides an up-to-date and comprehensive resource on the use of green materials in civil engineering- Covers green concrete, agricultural waste, industrial by-products, biological and waste materials such as smart materials, microbially generated calcium precipitation, recycled asphalt and natural fibers- Discusses selection criteria, durability, lifecycle, recyclability, and regulatory measures

The design of structures and infrastructure facilities has typically assumed a future climate that is much the same as today’s. However, a changing climate and the resulting more extreme weather events mean those climate bands are becoming outdated, leaving infrastructure operating outside or at the limit of its tolerance levels.With its evaluation of a host of assets in the built environment, Climate Change Effects on Infrastructure: Advanced Tools for Monitoring, Mitigation, and Lifecycle Optimization instructs readers how to assess and predict structural vulnerabilities resulting from climate change as well as ensuing hazard patterns using mathematical tools and data-driven models. The carefully compiled contributions also comprehensively survey the latest technological developments and the most useful approaches to counteract and/or prevent damage and connected socio-economic risks.The volume proves to be an even more invaluable resource thanks to its real-world application examples that spotlight the urgency of further investigations into the implementation of cost-effective and sustainable adaptive measures that can enhance or extend the reliable performance, durability, and efficiency of structures and infrastructure systems throughout their entire lifecycle.Reviews advanced computational models, simulations, and data analytics techniques for determining the effects of climate change on infrastructure.Describes advanced methods to enhance infrastructure resilience and offset or prevent the adverse impacts of climate change.Features case studies and applied examples for different structure types and infrastructure systems from a variety of regions and assorted scenarios.

The design of structures and infrastructure facilities has typically assumed a future climate that is much the same as today's. However, a changing climate and the resulting more extreme weather events mean those climate bands are becoming outdated, leaving infrastructure operating outside or at the limit of its tolerance levels.With its evaluation of a host of assets in the built environment, Climate Change Effects on Infrastructure: Advanced Tools for Monitoring, Mitigation, and Lifecycle Optimization instructs readers how to assess and predict structural vulnerabilities resulting from climate change as well as ensuing hazard patterns using mathematical tools and data-driven models. The carefully compiled contributions also comprehensively survey the latest technological developments and the most useful approaches to counteract and/or prevent damage and connected socio-economic risks.The volume proves to be an even more invaluable resource thanks to its real-world application examples that spotlight the urgency of further investigations into the implementation of cost-effective and sustainable adaptive measures that can enhance or extend the reliable performance, durability, and efficiency of structures and infrastructure systems throughout their entire lifecycle. - Reviews advanced computational models, simulations, and data analytics techniques for determining the effects of climate change on infrastructure. - Describes advanced methods to enhance infrastructure resilience and offset or prevent the adverse impacts of climate change. - Features case studies and applied examples for different structure types and infrastructure systems from a variety of regions and assorted scenarios.

Machine Learning Applications in Structural Engineering is a practical guide to machine learning in structural engineering. With first-hand examples of machine learning applications, this book is a vital reference for both entry-level readers and advanced professionals. For experts, the book offers insights into emerging applications that are shaping the future of the discipline, making it a compelling choice for engineers looking to leverage machine learning for smarter, more resilient structural solutions. This accessible style makes complex concepts manageable, and the book offers clear explanations while showcasing the potential of machine learning as a versatile tool for advancing structural engineering practices.It is aimed at engineers, researchers, and students with an interest in integrating new, machine learning technologies into daily practice. Readers will find a balance of foundational theory with hands-on, data-driven solutions tailored to meet real-world demands.Provides a domain-specific resource that combines machine learning with structural engineeringDescribes advanced machine learning techniques for a wide range of structural engineering applicationsDemonstrates how data-driven approaches are reshaping decision-making, enhancing resilience, and providing valuable predictive insights that are of particular importance in structural health monitoring and disaster resilienceIncludes theoretical approaches that are illustrated with extensive use of practical case studies and real-world examples

Reshaping Geotechnical Engineering with Machine Learning: Theory, Applications, and Innovations explores the transformative impact of machine learning (ML) on the field of geotechnical engineering. The book begins by examining the broad applications of ML in key areas such as foundation engineering and slope stability, demonstrating how advanced algorithms can enhance predictive accuracy and decision-making. It emphasizes the importance of robust data acquisition and preprocessing techniques, which are critical for the successful implementation of ML models in geotechnical contexts. The text examines the use of machine learning for predicting soil behavior, a complex challenge in geotechnical engineering, and highlights its role in risk assessment and management.In addition, it also addresses the integration of ML with finite element modeling to improve the analysis of tunnel and underground stability. The applications of machine learning in understanding geotechnical materials further showcase the versatility of these techniques. It also covers experimental investigations, including laboratory and field studies, which provide essential data for model training and validation. Through detailed case studies, the book illustrates practical implementations of machine learning, bridging theory and real-world problem-solving.Examines advanced machine learning techniques for a variety of geotechnical engineering applicationsDetails practical, real-world case studies that highlight machine learning in geotechnical engineeringProvides cross-disciplinary insights, bridging geotechnical engineering and data science

Advancements in Underground Infrastructures presents the advanced modelling tools and experimental techniques applied in underground infrastructure development. It examines the usage of mathematical tools, experimental techniques, and data-driven models, as well as the latest technological advancements in underground engineering used to enhance the safety and stability of underground structures. It also addresses the application of the circular economy model in underground engineering.Provides modelling theories in an easy-to-read format verified by on-site models for various regions and scenariosPresents applications of soft computing tools and techniques in underground engineeringIncludes practical examples and case studiesColour versions of the figures in this book can be found at www.routledge.com/9781032373379.

Advancements in Underground Infrastructures presents the advanced modelling tools and experimental techniques applied in underground infrastructure development. It examines the usage of mathematical tools, experimental techniques, and data-driven models, as well as the latest technological advancements in underground engineering used to enhance the safety and stability of underground structures. It also addresses the application of the circular economy model in underground engineering.Provides modelling theories in an easy-to-read format verified by on-site models for various regions and scenariosPresents applications of soft computing tools and techniques in underground engineeringIncludes practical examples and case studiesColour versions of the figures in this book can be found at www.routledge.com/9781032373379.

This book brings together leading experts from around the world to explore the transformative potential of Machine Learning (ML) and the Internet of Things (IoT) in healthcare. It provides a platform for studying a future where healthcare becomes more precise, personalized, and accessible for all. The book covers recent advancements that will shape the future of healthcare and how artificial intelligence is revolutionizing disease detection, from analyzing chest X-rays for pneumonia to solving the secrets of our genes. It investigates the transformative potential of smart devices, real-time analysis of heart data, and personalized treatment plan creation. It shows how ML and IoT work and presents real-world examples of how they are leading to earlier and more accurate diagnoses and personalized treatments.Therefore, this edited book will be an invaluable resource for researchers, healthcare professionals, data scientists, or simply someone passionate about the future of healthcare. Readers will discover the exciting possibilities that lie ahead at the crossroads of ML, IoT, and health informatics.

Handbook of Neural Computation explores neural computation applications, ranging from conventional fields of mechanical and civil engineering, to electronics, electrical engineering and computer science. This book covers the numerous applications of artificial and deep neural networks and their uses in learning machines, including image and speech recognition, natural language processing and risk analysis. Edited by renowned authorities in this field, this work is comprised of articles from reputable industry and academic scholars and experts from around the world.

Each contributor presents a specific research issue with its recent and future trends. As the demand rises in the engineering and medical industries for neural networks and other machine learning methods to solve different types of operations, such as data prediction, classification of images, analysis of big data, and intelligent decision-making, this book provides readers with the latest, cutting-edge research in one comprehensive text.

Features high-quality research articles on multivariate adaptive regression splines, the minimax probability machine, and more Discusses machine learning techniques, including classification, clustering, regression, web mining, information retrieval and natural language processing Covers supervised, unsupervised, reinforced, ensemble, and nature-inspired learning methods

Handbook of Neural Computation explores neural computation applications, ranging from conventional fields of mechanical and civil engineering, to electronics, electrical engineering and computer science. This book covers the numerous applications of artificial and deep neural networks and their uses in learning machines, including image and speech recognition, natural language processing and risk analysis. Edited by renowned authorities in this field, this work is comprised of articles from reputable industry and academic scholars and experts from around the world.Each contributor presents a specific research issue with its recent and future trends. As the demand rises in the engineering and medical industries for neural networks and other machine learning methods to solve different types of operations, such as data prediction, classification of images, analysis of big data, and intelligent decision-making, this book provides readers with the latest, cutting-edge research in one comprehensive text.- Features high-quality research articles on multivariate adaptive regression splines, the minimax probability machine, and more- Discusses machine learning techniques, including classification, clustering, regression, web mining, information retrieval and natural language processing- Covers supervised, unsupervised, reinforced, ensemble, and nature-inspired learning methods

Integrated Disaster Science and Management: Global Case Studies in Mitigation and Recovery bridges the gap between scientific research on natural disasters and the practice of disaster management. It examines natural hazards, including earthquakes, landslides and tsunamis, and uses integrated disaster management techniques, quantitative methods and big data analytics to create early warning models to mitigate impacts of these hazards and reduce the risk of disaster. It also looks at mitigation as part of the recovery process after a disaster, as in the case of the Nepal earthquake.

Edited by global experts in disaster management and engineering, the book offers case studies that focus on the critical phases of disaster management.

Identifies advanced techniques and models based on natural disaster science for forecasting disasters and analyzing risk Offers a holistic approach to the problem of disaster management, including preparation, recovery, and resilience Includes coverage of social, economic, and environmental impacts on disasters

Integrated Disaster Science and Management: Global Case Studies in Mitigation and Recovery bridges the gap between scientific research on natural disasters and the practice of disaster management. It examines natural hazards, including earthquakes, landslides and tsunamis, and uses integrated disaster management techniques, quantitative methods and big data analytics to create early warning models to mitigate impacts of these hazards and reduce the risk of disaster. It also looks at mitigation as part of the recovery process after a disaster, as in the case of the Nepal earthquake.Edited by global experts in disaster management and engineering, the book offers case studies that focus on the critical phases of disaster management.- Identifies advanced techniques and models based on natural disaster science for forecasting disasters and analyzing risk- Offers a holistic approach to the problem of disaster management, including preparation, recovery, and resilience- Includes coverage of social, economic, and environmental impacts on disasters

Handbook of Probabilistic Models carefully examines the application of advanced probabilistic models in conventional engineering fields. In this comprehensive handbook, practitioners, researchers and scientists will find detailed explanations of technical concepts, applications of the proposed methods, and the respective scientific approaches needed to solve the problem. This book provides an interdisciplinary approach that creates advanced probabilistic models for engineering fields, ranging from conventional fields of mechanical engineering and civil engineering, to electronics, electrical, earth sciences, climate, agriculture, water resource, mathematical sciences and computer sciences.

Specific topics covered include minimax probability machine regression, stochastic finite element method, relevance vector machine, logistic regression, Monte Carlo simulations, random matrix, Gaussian process regression, Kalman filter, stochastic optimization, maximum likelihood, Bayesian inference, Bayesian update, kriging, copula-statistical models, and more.

Explains the application of advanced probabilistic models encompassing multidisciplinary research Applies probabilistic modeling to emerging areas in engineering Provides an interdisciplinary approach to probabilistic models and their applications, thus solving a wide range of practical problems

Handbook of Probabilistic Models carefully examines the application of advanced probabilistic models in conventional engineering fields. In this comprehensive handbook, practitioners, researchers and scientists will find detailed explanations of technical concepts, applications of the proposed methods, and the respective scientific approaches needed to solve the problem. This book provides an interdisciplinary approach that creates advanced probabilistic models for engineering fields, ranging from conventional fields of mechanical engineering and civil engineering, to electronics, electrical, earth sciences, climate, agriculture, water resource, mathematical sciences and computer sciences.Specific topics covered include minimax probability machine regression, stochastic finite element method, relevance vector machine, logistic regression, Monte Carlo simulations, random matrix, Gaussian process regression, Kalman filter, stochastic optimization, maximum likelihood, Bayesian inference, Bayesian update, kriging, copula-statistical models, and more.- Explains the application of advanced probabilistic models encompassing multidisciplinary research- Applies probabilistic modeling to emerging areas in engineering- Provides an interdisciplinary approach to probabilistic models and their applications, thus solving a wide range of practical problems

Predictive Modeling for Energy Management and Power Systems Engineering introduces readers to the cutting-edge use of big data and large computational infrastructures in energy demand estimation and power management systems. The book supports engineers and scientists who seek to become familiar with advanced optimization techniques for power systems designs, optimization techniques and algorithms for consumer power management, and potential applications of machine learning and artificial intelligence in this field. The book provides modeling theory in an easy-to-read format, verified with on-site models and case studies for specific geographic regions and complex consumer markets. Presents advanced optimization techniques to improve existing energy demand system Provides data-analytic models and their practical relevance in proven case studies Explores novel developments in machine-learning and artificial intelligence applied in energy management Provides modeling theory in an easy-to-read format

Predictive Modeling for Energy Management and Power Systems Engineering introduces readers to the cutting-edge use of big data and large computational infrastructures in energy demand estimation and power management systems. The book supports engineers and scientists who seek to become familiar with advanced optimization techniques for power systems designs, optimization techniques and algorithms for consumer power management, and potential applications of machine learning and artificial intelligence in this field. The book provides modeling theory in an easy-to-read format, verified with on-site models and case studies for specific geographic regions and complex consumer markets.- Presents advanced optimization techniques to improve existing energy demand system- Provides data-analytic models and their practical relevance in proven case studies- Explores novel developments in machine-learning and artificial intelligence applied in energy management- Provides modeling theory in an easy-to-read format

New Materials in Civil Engineering provides engineers and scientists with the tools and methods needed to meet the challenge of designing and constructing more resilient and sustainable infrastructures. This book is a valuable guide to the properties, selection criteria, products, applications, lifecycle and recyclability of advanced materials. It presents an A-to-Z approach to all types of materials, highlighting their key performance properties, principal characteristics and applications. Traditional materials covered include concrete, soil, steel, timber, fly ash, geosynthetic, fiber-reinforced concrete, smart materials, carbon fiber and reinforced polymers. In addition, the book covers nanotechnology and biotechnology in the development of new materials.

Covers a variety of materials, including fly ash, geosynthetic, fiber-reinforced concrete, smart materials, carbon fiber reinforced polymer and waste materials Provides a "one-stop� resource of information for the latest materials and practical applications Includes a variety of different use case studies

New Materials in Civil Engineering provides engineers and scientists with the tools and methods needed to meet the challenge of designing and constructing more resilient and sustainable infrastructures. This book is a valuable guide to the properties, selection criteria, products, applications, lifecycle and recyclability of advanced materials. It presents an A-to-Z approach to all types of materials, highlighting their key performance properties, principal characteristics and applications. Traditional materials covered include concrete, soil, steel, timber, fly ash, geosynthetic, fiber-reinforced concrete, smart materials, carbon fiber and reinforced polymers. In addition, the book covers nanotechnology and biotechnology in the development of new materials.- Covers a variety of materials, including fly ash, geosynthetic, fiber-reinforced concrete, smart materials, carbon fiber reinforced polymer and waste materials- Provides a "one-stop resource of information for the latest materials and practical applications- Includes a variety of different use case studies

Data Analytics in Biomedical Engineering and Healthcare explores key applications using data analytics, machine learning, and deep learning in health sciences and biomedical data. The book is useful for those working with big data analytics in biomedical research, medical industries, and medical research scientists. The book covers health analytics, data science, and machine and deep learning applications for biomedical data, covering areas such as predictive health analysis, electronic health records, medical image analysis, computational drug discovery, and genome structure prediction using predictive modeling. Case studies demonstrate big data applications in healthcare using the MapReduce and Hadoop frameworks. Examines the development and application of data analytics applications in biomedical data Presents innovative classification and regression models for predicting various diseases Discusses genome structure prediction using predictive modeling Shows readers how to develop clinical decision support systems Shows researchers and specialists how to use hybrid learning for better medical diagnosis, including case studies of healthcare applications using the MapReduce and Hadoop frameworks

Data Analytics in Biomedical Engineering and Healthcare explores key applications using data analytics, machine learning, and deep learning in health sciences and biomedical data. The book is useful for those working with big data analytics in biomedical research, medical industries, and medical research scientists. The book covers health analytics, data science, and machine and deep learning applications for biomedical data, covering areas such as predictive health analysis, electronic health records, medical image analysis, computational drug discovery, and genome structure prediction using predictive modeling. Case studies demonstrate big data applications in healthcare using the MapReduce and Hadoop frameworks.- Examines the development and application of data analytics applications in biomedical data- Presents innovative classification and regression models for predicting various diseases- Discusses genome structure prediction using predictive modeling- Shows readers how to develop clinical decision support systems- Shows researchers and specialists how to use hybrid learning for better medical diagnosis, including case studies of healthcare applications using the MapReduce and Hadoop frameworks

Basics of Computational Geophysics provides a one-stop, collective resource for practitioners on the different techniques and models in geoscience, their practical applications, and case studies. The reference provides the modeling theory in an easy-to-read format that is verified with onsite models for specific regions and scenarios, including the use of big data and artificial intelligence. This book offers a platform whereby readers will learn theory, practical applications, and the comparison of real-world problems surrounding geomechanics, modeling and optimizations. Covers various advanced computational techniques for solving different problems in geophysics, including the use of Big Data and artificial intelligence Includes case studies that provide examples surrounding practical applications Provides an assessment of the capabilities of commercial software

Water Engineering Modeling and Mathematic Tools provides an informative resource for practitioners who want to learn more about different techniques and models in water engineering and their practical applications and case studies. The book provides modelling theories in an easy-to-read format verified with on-site models for specific regions and scenarios. Users will find this to be a significant contribution to the development of mathematical tools, experimental techniques, and data-driven models that support modern-day water engineering applications. Civil engineers, industrialists, and water management experts should be familiar with advanced techniques that can be used to improve existing systems in water engineering.

This book provides key ideas on recently developed machine learning methods and AI modelling. It will serve as a common platform for practitioners who need to become familiar with the latest developments of computational techniques in water engineering.

Includes firsthand experience about artificial intelligence models, utilizing case studies Describes biological, physical and chemical techniques for the treatment of surface water, groundwater, sea water and rain/snow Presents the application of new instruments in water engineering

Water Engineering Modeling and Mathematic Tools provides an informative resource for practitioners who want to learn more about different techniques and models in water engineering and their practical applications and case studies. The book provides modelling theories in an easy-to-read format verified with on-site models for specific regions and scenarios. Users will find this to be a significant contribution to the development of mathematical tools, experimental techniques, and data-driven models that support modern-day water engineering applications. Civil engineers, industrialists, and water management experts should be familiar with advanced techniques that can be used to improve existing systems in water engineering.This book provides key ideas on recently developed machine learning methods and AI modelling. It will serve as a common platform for practitioners who need to become familiar with the latest developments of computational techniques in water engineering.- Includes firsthand experience about artificial intelligence models, utilizing case studies- Describes biological, physical and chemical techniques for the treatment of surface water, groundwater, sea water and rain/snow- Presents the application of new instruments in water engineering

Basics of Computational Geophysics provides a one-stop, collective resource for practitioners on the different techniques and models in geoscience, their practical applications, and case studies. The reference provides the modeling theory in an easy-to-read format that is verified with onsite models for specific regions and scenarios, including the use of big data and artificial intelligence. This book offers a platform whereby readers will learn theory, practical applications, and the comparison of real-world problems surrounding geomechanics, modeling and optimizations.- Covers various advanced computational techniques for solving different problems in geophysics, including the use of Big Data and artificial intelligence- Includes case studies that provide examples surrounding practical applications- Provides an assessment of the capabilities of commercial software

Modeling in Geotechnical Engineering is a one stop reference for a range of computational models, the theory explaining how they work, and case studies describing how to apply them. Drawing on the expertise of contributors from a range of disciplines including geomechanics, optimization, and computational engineering, this book provides an interdisciplinary guide to this subject which is suitable for readers from a range of backgrounds.

Before tackling the computational approaches, a theoretical understanding of the physical systems is provided that helps readers to fully grasp the significance of the numerical methods. The various models are presented in detail, and advice is provided on how to select the correct model for your application.

Provides detailed descriptions of different computational modelling methods for geotechnical applications, including the finite element method, the finite difference method, and the boundary element method Gives readers the latest advice on the use of big data analytics and artificial intelligence in geotechnical engineering Includes case studies to help readers apply the methods described in their own work

Modeling in Geotechnical Engineering is a one stop reference for a range of computational models, the theory explaining how they work, and case studies describing how to apply them. Drawing on the expertise of contributors from a range of disciplines including geomechanics, optimization, and computational engineering, this book provides an interdisciplinary guide to this subject which is suitable for readers from a range of backgrounds.Before tackling the computational approaches, a theoretical understanding of the physical systems is provided that helps readers to fully grasp the significance of the numerical methods. The various models are presented in detail, and advice is provided on how to select the correct model for your application.- Provides detailed descriptions of different computational modelling methods for geotechnical applications, including the finite element method, the finite difference method, and the boundary element method- Gives readers the latest advice on the use of big data analytics and artificial intelligence in geotechnical engineering- Includes case studies to help readers apply the methods described in their own work

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

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

Green Materials in Civil Engineering provides a comprehensive resource for practitioners to learn more about the utilization of these materials in civil engineering, as well as their practical applications. Novel green materials such as fly ash, slag, fiber-reinforced concrete and soil, smart materials, biological materials, and waste materials such as mineral wastes, building and demolition waste, recycled asphalt, coal gangue and industrial by-products are discussed in detail. Emphasis is placed on understanding the qualities, selection criteria, products and applications, durability, life cycle, and recyclability of these materials.The book will be a valuable reference resource for academic and industrial researchers, materials scientists, and civil engineers who are working in the development of construction materials, utilization of waste, and other fine by-products in the production of concrete and other construction materials.Provides an up-to-date and comprehensive resource on the use of green materials in civil engineeringCovers green concrete, agricultural waste, industrial by-products, biological and waste materials such as smart materials, microbially generated calcium precipitation, recycled asphalt and natural fibersDiscusses selection criteria, durability, lifecycle, recyclability, and regulatory measures

Green Materials in Civil Engineering provides a comprehensive resource for practitioners to learn more about the utilization of these materials in civil engineering, as well as their practical applications. Novel green materials such as fly ash, slag, fiber-reinforced concrete and soil, smart materials, carbon fibre reinforced polymers, waste materials, biological materials, and waste materials such as building and demolition waste, recycled asphalt, and industrial by-products are discussed in detail. Emphasis is placed on understanding the qualities, selection criteria, products and applications, durability, life cycle, and recyclability of these materials.The book will be a valuable reference resource for academic and industrial researchers, materials scientists and civil engineers who are working in the development of construction materials and utilization of waste and other fine by-products in the production of concrete and other construction materials.- Provides an up-to-date and comprehensive resource on the use of green materials in civil engineering- Covers green concrete, agricultural waste, industrial by-products, biological and waste materials such as smart materials, microbially generated calcium precipitation, recycled asphalt and natural fibers- Discusses selection criteria, durability, lifecycle, recyclability, and regulatory measures

The design of structures and infrastructure facilities has typically assumed a future climate that is much the same as today’s. However, a changing climate and the resulting more extreme weather events mean those climate bands are becoming outdated, leaving infrastructure operating outside or at the limit of its tolerance levels.With its evaluation of a host of assets in the built environment, Climate Change Effects on Infrastructure: Advanced Tools for Monitoring, Mitigation, and Lifecycle Optimization instructs readers how to assess and predict structural vulnerabilities resulting from climate change as well as ensuing hazard patterns using mathematical tools and data-driven models. The carefully compiled contributions also comprehensively survey the latest technological developments and the most useful approaches to counteract and/or prevent damage and connected socio-economic risks.The volume proves to be an even more invaluable resource thanks to its real-world application examples that spotlight the urgency of further investigations into the implementation of cost-effective and sustainable adaptive measures that can enhance or extend the reliable performance, durability, and efficiency of structures and infrastructure systems throughout their entire lifecycle.Reviews advanced computational models, simulations, and data analytics techniques for determining the effects of climate change on infrastructure.Describes advanced methods to enhance infrastructure resilience and offset or prevent the adverse impacts of climate change.Features case studies and applied examples for different structure types and infrastructure systems from a variety of regions and assorted scenarios.

The design of structures and infrastructure facilities has typically assumed a future climate that is much the same as today's. However, a changing climate and the resulting more extreme weather events mean those climate bands are becoming outdated, leaving infrastructure operating outside or at the limit of its tolerance levels.With its evaluation of a host of assets in the built environment, Climate Change Effects on Infrastructure: Advanced Tools for Monitoring, Mitigation, and Lifecycle Optimization instructs readers how to assess and predict structural vulnerabilities resulting from climate change as well as ensuing hazard patterns using mathematical tools and data-driven models. The carefully compiled contributions also comprehensively survey the latest technological developments and the most useful approaches to counteract and/or prevent damage and connected socio-economic risks.The volume proves to be an even more invaluable resource thanks to its real-world application examples that spotlight the urgency of further investigations into the implementation of cost-effective and sustainable adaptive measures that can enhance or extend the reliable performance, durability, and efficiency of structures and infrastructure systems throughout their entire lifecycle. - Reviews advanced computational models, simulations, and data analytics techniques for determining the effects of climate change on infrastructure. - Describes advanced methods to enhance infrastructure resilience and offset or prevent the adverse impacts of climate change. - Features case studies and applied examples for different structure types and infrastructure systems from a variety of regions and assorted scenarios.

Machine Learning Applications in Structural Engineering is a practical guide to machine learning in structural engineering. With first-hand examples of machine learning applications, this book is a vital reference for both entry-level readers and advanced professionals. For experts, the book offers insights into emerging applications that are shaping the future of the discipline, making it a compelling choice for engineers looking to leverage machine learning for smarter, more resilient structural solutions. This accessible style makes complex concepts manageable, and the book offers clear explanations while showcasing the potential of machine learning as a versatile tool for advancing structural engineering practices.It is aimed at engineers, researchers, and students with an interest in integrating new, machine learning technologies into daily practice. Readers will find a balance of foundational theory with hands-on, data-driven solutions tailored to meet real-world demands.Provides a domain-specific resource that combines machine learning with structural engineeringDescribes advanced machine learning techniques for a wide range of structural engineering applicationsDemonstrates how data-driven approaches are reshaping decision-making, enhancing resilience, and providing valuable predictive insights that are of particular importance in structural health monitoring and disaster resilienceIncludes theoretical approaches that are illustrated with extensive use of practical case studies and real-world examples

Reshaping Geotechnical Engineering with Machine Learning: Theory, Applications, and Innovations explores the transformative impact of machine learning (ML) on the field of geotechnical engineering. The book begins by examining the broad applications of ML in key areas such as foundation engineering and slope stability, demonstrating how advanced algorithms can enhance predictive accuracy and decision-making. It emphasizes the importance of robust data acquisition and preprocessing techniques, which are critical for the successful implementation of ML models in geotechnical contexts. The text examines the use of machine learning for predicting soil behavior, a complex challenge in geotechnical engineering, and highlights its role in risk assessment and management.In addition, it also addresses the integration of ML with finite element modeling to improve the analysis of tunnel and underground stability. The applications of machine learning in understanding geotechnical materials further showcase the versatility of these techniques. It also covers experimental investigations, including laboratory and field studies, which provide essential data for model training and validation. Through detailed case studies, the book illustrates practical implementations of machine learning, bridging theory and real-world problem-solving.Examines advanced machine learning techniques for a variety of geotechnical engineering applicationsDetails practical, real-world case studies that highlight machine learning in geotechnical engineeringProvides cross-disciplinary insights, bridging geotechnical engineering and data science

Advancements in Underground Infrastructures presents the advanced modelling tools and experimental techniques applied in underground infrastructure development. It examines the usage of mathematical tools, experimental techniques, and data-driven models, as well as the latest technological advancements in underground engineering used to enhance the safety and stability of underground structures. It also addresses the application of the circular economy model in underground engineering.Provides modelling theories in an easy-to-read format verified by on-site models for various regions and scenariosPresents applications of soft computing tools and techniques in underground engineeringIncludes practical examples and case studiesColour versions of the figures in this book can be found at www.routledge.com/9781032373379.

Advancements in Underground Infrastructures presents the advanced modelling tools and experimental techniques applied in underground infrastructure development. It examines the usage of mathematical tools, experimental techniques, and data-driven models, as well as the latest technological advancements in underground engineering used to enhance the safety and stability of underground structures. It also addresses the application of the circular economy model in underground engineering.Provides modelling theories in an easy-to-read format verified by on-site models for various regions and scenariosPresents applications of soft computing tools and techniques in underground engineeringIncludes practical examples and case studiesColour versions of the figures in this book can be found at www.routledge.com/9781032373379.

This book brings together leading experts from around the world to explore the transformative potential of Machine Learning (ML) and the Internet of Things (IoT) in healthcare. It provides a platform for studying a future where healthcare becomes more precise, personalized, and accessible for all. The book covers recent advancements that will shape the future of healthcare and how artificial intelligence is revolutionizing disease detection, from analyzing chest X-rays for pneumonia to solving the secrets of our genes. It investigates the transformative potential of smart devices, real-time analysis of heart data, and personalized treatment plan creation. It shows how ML and IoT work and presents real-world examples of how they are leading to earlier and more accurate diagnoses and personalized treatments.Therefore, this edited book will be an invaluable resource for researchers, healthcare professionals, data scientists, or simply someone passionate about the future of healthcare. Readers will discover the exciting possibilities that lie ahead at the crossroads of ML, IoT, and health informatics.