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Computational Intelligence for Water and Environmental Sciences
This book provides a comprehensive yet fresh perspective for the cutting-edge CI-oriented approaches in water resources planning and management. The book takes a deep dive into topics like meta-heuristic evolutionary optimization algorithms (e.g., GA, PSA, etc.), data mining techniques (e.g., SVM, ANN, etc.), probabilistic and Bayesian-oriented frameworks, fuzzy logic, AI, deep learning, and expert systems. These approaches provide a practical approach to understand and resolve complicated and intertwined real-world problems that often imposed serious challenges to traditional deterministic precise frameworks. The topic caters to postgraduate students and senior researchers who are interested in computational intelligence approach to issues stemming from water and environmental sciences.
Uncertainty, Modeling, and Decision Making in Geotechnics
Uncertainty, Modeling, and Decision Making in Geotechnics shows how uncertainty quantification and numerical modeling can complement each other to enhance decision-making in geotechnical practice, filling a critical gap in guiding practitioners to address uncertainties directly. The book helps practitioners acquire a working knowledge of geotechnical risk and reliability methods and guides them to use these methods wisely in conjunction with data and numerical modeling. In particular, it provides guidance on the selection of realistic statistics and a cost-effective, accessible method to address different design objectives, and for different problem settings, and illustrates the value of this to decision-making using realistic examples. Bringing together statistical characterization, reliability analysis, reliability-based design, probabilistic inverse analysis, and physical insights drawn from case studies, this reference guide from an international team of experts offers an excellent resource for state-of-the-practice uncertainty-informed geotechnical design for specialist practitioners and the research community.
Civil Engineering and Energy-Environment Vol 1
Civil Engineering and Energy-Environment focuses on the research of civil engineering, environment resources and energy materials. This proceedings gathers the most cutting-edge research and achievements, aiming to provide scholars and engineers with preferable research direction and engineering solution as reference. Subjects in this proceedings include: - Engineering Structure - Environmental Protection Materials - Architectural Environment ·Environment Resources - Energy Storage - Building Electrical Engineering The works of this proceedings will promote development of civil engineering and environment engineering. Thereby, promote scientific information interchange between scholars from top universities, research centers and high-tech enterprises working all around the world.
Proceedings of the 2024 8th International Conference on Civil Architecture and Structural Engineering (ICCASE 2024)
Zusammenfassung: This is an open access book. 2024 8th International Conference on Civil Architecture and Structural Engineering (ICCASE 2024) will be held in Guangzhou during April 19-21, 2024. The conference mainly discussed research areas such as "Engineering Structures", "Smart Buildings", "Building Materials", "Structural Seismic Resistance", "Monitoring and Testing", "Geotechnical Engineering", and "Underground Space Engineering". Intended to provide an exchange platform for experts, scholars, engineering technicians, and technical research and development personnel in related fields. By discussing scientific research achievements and cutting-edge technologies, understanding academic development trends, broadening research ideas, strengthening academic research and exploration, and promoting the industrialization of academic achievements
Bayesian Machine Learning in Geotechnical Site Characterization
Bayesian data analysis and modelling linked with machine learning offers a new tool for handling geotechnical data. This book presents recent advancements made by the author in the area of probabilistic geotechnical site characterization. Two types of correlation play central roles in geotechnical site characterization: cross-correlation among soil properties and spatial-correlation in the underground space. The book starts with the introduction of Bayesian notion of probability “degree of belief”, showing that well-known probability axioms can be obtained by Boolean logic and the definition of plausibility function without the use of the notion “relative frequency”. It then reviews probability theories and useful probability models for cross-correlation and spatial correlation. Methods for Bayesian parameter estimation and prediction are also presented, and the use of these methods demonstrated with geotechnical site characterization examples. Bayesian Machine Learning in Geotechnical Site Characterization suits consulting engineers and graduate students in the area.
Frontiers in Civil and Hydraulic Engineering, Volume 2
Frontiers in Civil and Hydraulic Engineering focuses on the research of architecture and hydraulic engineering in civil engineering. The proceedings feature the most cutting-edge research directions and achievements related to civil and hydraulic engineering. Subjects in the proceedings including: Engineering Structure Intelligent Building Structural Seismic Resistance Monitoring and Testing Hydraulic Engineering Engineering Facility The works of this proceedings can promote development of civil and hydraulic engineering, resource sharing, flexibility and high efficiency. Thereby, promote scientific information interchange between scholars from the top universities, research centers and high-tech enterprises working all around the world.
Big Data in Engineering Applications
This book presents the current trends, technologies, and challenges in Big Data in the diversified field of engineering and sciences. It covers the applications of Big Data ranging from conventional fields of mechanical engineering, civil engineering to electronics, electrical, and computer science to areas in pharmaceutical and biological sciences. This book consists of contributions from various authors from all sectors of academia and industries, demonstrating the imperative application of Big Data for the decision-making process in sectors where the volume, variety, and velocity of information keep increasing. The book is a useful reference for graduate students, researchers and scientists interested in exploring the potential of Big Data in the application of engineering areas.
