Applications of Artificial Neural Networks and Machine Learning in Civil Engineering

AvAli Kaveh

Inbunden, Engelska, 2024

Del i serien Studies in Computational Intelligence

2 117 kr

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Häftad

2 117 kr

Beskrivning

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

Utgivningsdatum:2024-07-30
Mått:155 x 235 x 31 mm
Vikt:988 g
Format:Inbunden
Språk:Engelska
Serie:Studies in Computational Intelligence
Antal sidor:474
Upplaga:2024
Förlag:Springer International Publishing AG
ISBN:9783031660504

Utforska kategorier

Artificiell intelligens inom Data och IT
Byggnadsteknik inom Naturvetenskap och teknik

Innehållsförteckning

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.