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

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

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.

This new volume aims to find real-world solutions to present-day problems by using IoT and related technologies. It explores the myriad applications of the Internet of Things in diverse areas—in healthcare, the construction industry, in wildlife monitoring, in home security systems, in agriculture, in cryptology, in hospitality employment, in data security, and more. The chapters illustrate the defining aspects of architecture, product design, modules, interfaces, and data for building systems that satisfy specified requirements of the IoT applications discussed. The authors show the novel results that present solutions to meet the ever-increasing demand of industries.

This book gathers the latest advances, innovations, and applications in the field of information technology in civil and building engineering, presented at the 19th International Conference on Computing in Civil and Building Engineering (ICCCBE), held in Cape Town, South Africa on October 26-28, 2022. It covers highly diverse topics such as BIM, construction information modeling, knowledge management, GIS, GPS, laser scanning, sensors, monitoring, VR/AR, computer-aided construction, product and process modeling, big data and IoT, cooperative design, mobile computing, simulation, structural health monitoring, computer-aided structural control and analysis, ICT in geotechnical engineering, computational mechanics, asset management, maintenance, urban planning, facility management, and smart cities. Written by leading researchers and engineers, and selected by means of a rigorous international peer-review process, the contributions highlight numerous exciting ideas that will spur novel research directions and foster multidisciplinary collaborations.

Machine learning continues to have myriad applications across industries and fields. To ensure this technology is utilized appropriately and to its full potential, organizations must better understand exactly how and where it can be adapted. Further study on the applications of machine learning is required to discover its best practices, challenges, and strategies. The Research Anthology on Machine Learning Techniques, Methods, and Applications provides a thorough consideration of the innovative and emerging research within the area of machine learning. The book discusses how the technology has been used in the past as well as potential ways it can be used in the future to ensure industries continue to develop and grow. Covering a range of topics such as artificial intelligence, deep learning, cybersecurity, and robotics, this major reference work is ideal for computer scientists, managers, researchers, scholars, practitioners, academicians, instructors, and students.

While the word "automation" may conjure images of robots taking over jobs, the reality is much more nuanced. In construction, for instance, automation is less likely to diminish employment opportunities than it is to increase productivity. Indeed, automation alongside the global need for new and updated infrastructure and better and more affordable housing can help shape the direction of the construction industry. The key will be anticipating and preparing for the shift, in part by developing new skills in the current and future workforce. This book presents all aspects of automation in construction pertaining to the use of information technologies in design, engineering, construction technologies, and maintenance and management of constructed facilities. The broad scope encompasses all stages of the construction life cycle from initial planning and design, through the construction of the facility, its operation, and maintenance, to the eventual dismantling and recycling of buildings and engineering structures. Features: Examines Building Information Management systems, allowing on-site execution of construction more efficient, and for project teams to eliminate mistakes and better coordinate the workforce Presents the latest information on the automation of modular construction, production in factories, including 3-D printing of components such as facades, or even load-bearing and essential components