Gebrail Bekdas - Böcker

Introduction and Applications of Machine Learning in Geotechnics offers a comprehensive exploration of machine learning methodologies and their diverse applications in geotechnical engineering. The book begins with a detailed review of machine learning methods tailored for geotechnical applications, setting the foundation for subsequent chapters. Regression models are utilized to predict shear wave velocities while optimization-based approaches are employed to determine the optimal dimensions of reinforced concrete (RC) retaining walls. The book further explores the identification of gravelly soil through optimized machine learning models and predicts stress-strain responses using data from simple shear tests.Additionally, it outlines the forecasting of liquefaction events triggered by seismic activities and estimates the uniaxial compressive strength of soil using machine learning techniques. The prediction of vertical effective stress and specific penetration resistance is examined to enhance soil characterization and geotechnical analyses. The authors' provide valuable insights for geotechnical engineers and researchers seeking to leverage advanced computational tools for enhanced geotechnical assessments and design processes.Provides a systematic overview of machine learning and optimization applications in geotechnical engineeringDemonstrates real-world implementations for soil classification, seismic response, and structural-geotechnical interactionIntroduces interpretable and explainable AI methods for transparent engineering decision-makingEmphasizes sustainable and data-driven solutions through hybrid modeling and metaheuristic optimization

Metaheuristics for Structural Design and Analysis discusses general properties and types of metaheuristic techniques, basic principles of topology, shape and size optimization of structures, and applications of metaheuristic algorithms in solving structural design problems. Analysis of structures using metaheuristic algorithms is also discussed. Comparisons are made with classical methods and modern computational methods through metaheuristic algorithms. The book is designed for senior structural engineering students, graduate students, academicians and practitioners.

In today's developing world, industries are constantly required to improve and advance. New approaches are being implemented to determine optimum values and solutions for models such as artificial intelligence and machine learning. Research is a necessity for determining how these recent methods are being applied within the engineering field and what effective solutions they are providing. Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering is a collection of innovative research on the methods and implementation of machine learning and AI in multiple facets of engineering. While highlighting topics including control devices, geotechnology, and artificial neural networks, this book is ideally designed for engineers, academicians, researchers, practitioners, and students seeking current research on solving engineering problems using smart technology.

This book is an up-to-date source for computation applications of optimization, prediction via artificial intelligence methods, and evaluation of metaheuristic algorithm with different structural applications.

This book is an up-to-date source for computation applications of optimization, prediction via artificial intelligence methods, and evaluation of metaheuristic algorithm with different structural applications.

Another difference and advantage of the book are to provide chapters about several types of control types including passive tuned mass dampers, active tuned mass dampers, tuned liquid dampers, tuned liquid column dampers and inerter dampers.

Another difference and advantage of the book are to provide chapters about several types of control types including passive tuned mass dampers, active tuned mass dampers, tuned liquid dampers, tuned liquid column dampers and inerter dampers.

These algorithms have unique features, but the usage of different features of different algorithms may give more effective optimum results in means of precision in optimum results, computational effort, and convergence.

These algorithms have unique features, but the usage of different features of different algorithms may give more effective optimum results in means of precision in optimum results, computational effort, and convergence.

Soft computing applications plays a crucial role in civil engineering applications, with engineers striving to create outstanding designs that prioritize safety, aesthetics, cost-efficiency, and environmental considerations. Advanced optimization techniques are especially valuable for complex systems including multi-constraint problems, multi-objective problems and control problems needing iterative processes in solving differential equations.Throughout history, people have used their creativity to enhance designs in everyday tasks, and this is where metaheuristics come into play, drawing inspiration from nature to develop novel algorithms. These artificial intelligence-based algorithms possess distinctive attributes, and leveraging various features from different algorithms can enhance the effectiveness of optimization, improving precision, computational efficiency, and convergence.This book serves as a timely resource, summarizing the latest advancements in civil engineering optimization, encompassing both metaheuristic approaches and emerging trends that integrates artificial intelligence and machine learning techniques to predict optimal solutions, streamlining lengthy optimization processes.The book's chapters cover a wide range of civil engineering applications, with the primary goal being to introduce fundamental concepts and advanced adaptations. This comprehensive resource is designed to provide undergraduates and graduate engineering students with a solid understanding of materials and content, making it a valuable reference for university courses in various civil engineering disciplines.The book will be edited, and the editors will contribute to most of the chapters. Depending on the availability of high-quality papers, the editors may increase their contributions by sharing recent research projects and postgraduate students' theses.

Artificial Intelligence in Civil Engineering: Recent Advances provides a comprehensive overview of the latest developments in applying artificial intelligence (AI) techniques to solve complex civil engineering problems. Civil engineers today face challenges that demand not only safety and cost-effectiveness, but also sustainability and resilience against natural hazards. This volume demonstrates how AI offers powerful tools to meet these demands by transforming traditional engineering methodologies.The chapters present a broad spectrum of applications, including predicting shear wave velocity, slope stability, estimating uniaxial compressive strength of soils, optimization-based design of reinforced concrete retaining walls, structural control strategies for seismic resilience, and the integration of Building Information Modeling (BIM) with AI-driven solutions. Machine learning, deep learning, and metaheuristic algorithms are explored in detail, supported by real-world case studies and experimental data.By bridging theory with practice, the book highlights how AI enhances predictive accuracy, optimizes design processes, and reduces computational effort in engineering tasks. Researchers, graduate students, and practicing engineers will find this volume a timely reference, offering both methodological foundations and practical insights into AI-powered approaches that are reshaping the future of civil engineering.

This timely book deals with a current topic, i.e. the applications of metaheuristic algorithms, with a primary focus on optimization problems in civil engineering. The first chapter offers a concise overview of different kinds of metaheuristic algorithms, explaining their advantages in solving complex engineering problems that cannot be effectively tackled by traditional methods, and citing the most important works for further reading. The remaining chapters report on advanced studies on the applications of certain metaheuristic algorithms to specific engineering problems. Genetic algorithm, bat algorithm, cuckoo search, harmony search and simulated annealing are just some of the methods presented and discussed step by step in real-application contexts, in which they are often used in combination with each other. Thanks to its synthetic yet meticulous and practice-oriented approach, the book is a perfect guide for graduate students, researchers and professionals willing to applying metaheuristic algorithms in civil engineering and other related engineering fields, such as mechanical, transport and geotechnical engineering. It is also a valuable aid for both lectures and advanced engineering students.

Thanks to its synthetic yet meticulous and practice-oriented approach, the book is a perfect guide for graduate students, researchers and professionals willing to applying metaheuristic algorithms in civil engineering and other related engineering fields, such as mechanical, transport and geotechnical engineering.