Applications of Artificial Neural Networks and Machine Learning in Civil Engineering

Inbunden, Engelska, 2024

Av Ali Kaveh

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This book provides different applications of artificial neural networks (ANN) and machine learning (ML) in various problems of material science, structural optimization, and optimal analysis of structures in twenty two chapters.

Produktinformation

Utgivningsdatum2024-07-30
Mått155 x 235 x 31 mm
Vikt988 g
FormatInbunden
SpråkEngelska
SerieStudies in Computational Intelligence
Antal sidor474
Upplaga2024
FörlagSpringer International Publishing AG
ISBN9783031660504

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Byggnadsteknik inom Naturvetenskap och teknik
Artificiell intelligens inom Data och it

Artificial Intelligence Background, Applications and Future.- Buckling Resistance Prediction of High Strength Steel Columns Using Metaheuristic Trained Artificial Neural Networks.- The Use of Artificial Neural Networks and Metaheuristic Algorithms to Optimize the Compressive Strength of Concrete.- Design of Double Layer Grids Using Backpropagation Neural Networks.- Analysis of Double Layer Barrel Vaults Using Different Neural Networks.- BP and RBF Neural Networks for Predicting Displacements and Design of Schwedler dome.- Structural Optimization by Gradient Based Neural Networks.- Comparative Study of Backpropagation and Improved Counter propagation Neural Nets in Structural Analysis and Optimization.- Hybrid ECBO ANN Algorithm for Shear Strength of Partially Grouted Masonry Walls.- Shape Optimization of Arch Dams with Frequency Constraints by Enhanced Charged System Search Algorithm and Neural Network.- Estimation of the Vulnerability of the Concrete Structures Using Artificial Neural Networks.- Efficient Training of Artificial Neural Networks Using Different Meta heuristic Algorithms for Predicting the FRP Strength.- A Metaheuristic Based Artificial Neural Network for Plastic Limit Analysis of Frames.- Wavefront Reduction Using Graphs, Neural Networks and Genetic Algorithm.- Optimal Design of Transmission Towers Using Genetic Algorithm and Neural Networks.- Stimating the Vulnerability of the Concrete Moment Resisting Frame Structures Using Artificial Neural Networks.- A Hybrid Graph Neural Method for Domain Decomposition.- GMDH based Prediction of Shear Strength of FRP RC Beams With and Without Stirrups.- Efficient Training of Two ANNs Using Four Meta-heuristic Algorithms for Predicting the FRP Strength.- New Predictive Models for Prediction of Bond Strength Between FRP Reinforcements Externally Glued on Masonry Units.- Kernel Extreme Learning Machine Application in Prediction of Bond Strength Between EBR FRP and Concrete Substrate.- Development of Predictive Models for Shear Strength of HSC Slender Beams Without Web Reinforcement Using Machine Learning Based Techniques.