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Titlebook: New Advances in Soft Computing in Civil Engineering; AI-Based Optimizatio Gebrail Bekda?,Sinan Melih Nigdeli Book 2024 The Editor(s) (if ap

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發(fā)表于 2025-3-21 17:18:33 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱New Advances in Soft Computing in Civil Engineering
副標題AI-Based Optimizatio
編輯Gebrail Bekda?,Sinan Melih Nigdeli
視頻videohttp://file.papertrans.cn/665/664757/664757.mp4
概述Introduce fundamental concepts and advanced adaptations in civil engineering.Covers a wide range of civil engineering applications.Summarizes advancements integrating artificial intelligence and machi
叢書名稱Studies in Systems, Decision and Control
圖書封面Titlebook: New Advances in Soft Computing in Civil Engineering; AI-Based Optimizatio Gebrail Bekda?,Sinan Melih Nigdeli Book 2024 The Editor(s) (if ap
描述.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 optim
出版日期Book 2024
關(guān)鍵詞Algorithms; Artificial Intelligence; Artificial Neural Networks; Evolutionary Algorithms; Genetic Algori
版次1
doihttps://doi.org/10.1007/978-3-031-65976-8
isbn_softcover978-3-031-65978-2
isbn_ebook978-3-031-65976-8Series ISSN 2198-4182 Series E-ISSN 2198-4190
issn_series 2198-4182
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Forecasting of Lake Level by Soft Computing Approaches,addition to being models used successfully in hydrological modelling of civil engineering, the changes in modelling performance with the number of iterations, kernel functions, optimization algorithms, and data input sets that constitute the internal dynamite of the techniques are investigated. The
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A Review of Deformations Prediction for Oil and Gas Pipelines Using Machine and Deep Learning,ecisely predict oil and gas pipeline deformations compared to typical techniques. Assessing ML predictive methods employed on reservoir or synthetic data for pipeline deformations can assist in developing monitoring tools for pipelines, reducing cost and time. It is expected that the development of
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Area Optimization of Bending Members with Different Shapes in Terms of Pure Bending, of the system. Following this step, the distances from the centres of each component to the axes are calculated, enabling the computation of moment of inertia for both directions. Once the moments of inertia are obtained, the bending moments and stresses are computed. Considering that the design of
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Book 2024his 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 optim
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Gebrail Bekda?,Sinan Melih Nigdeliround state F(2P3/2) and the excited state F*(2P1/2) in?the?F+D2 reaction, and exploits the breakdown of BOA in the low collision energy..978-3-642-39755-4978-3-642-39756-1Series ISSN 2190-5053 Series E-ISSN 2190-5061
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