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Produktbild: Applications of Artificial Neural Networks and Machine Learning in Civil Engineering
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Applications of Artificial Neural Networks and Machine Learning in Civil Engineering

Aus der Reihe Studies in Computational Intelligence
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175,99 € UVP 213,99 €

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

31.07.2025

Abbildungen

XVI, 474 p. 277 illus., 194 illus. in color.

Verlag

Springer

Seitenzahl

474

Maße (L/B/H)

23,5/15,5/2,5 cm

Gewicht

828 g

Sprache

Englisch

ISBN

978-3-031-66053-5

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

31.07.2025

Abbildungen

XVI, 474 p. 277 illus., 194 illus. in color.

Verlag

Springer

Seitenzahl

474

Maße (L/B/H)

23,5/15,5/2,5 cm

Gewicht

828 g

Sprache

Englisch

ISBN

978-3-031-66053-5

Herstelleradresse

Springer-Verlag GmbH
Tiergartenstr. 17
69121 Heidelberg
DE

Email: ProductSafety@springernature.com

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  • Produktbild: Applications of Artificial Neural Networks and Machine Learning in Civil Engineering

  • 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.