Vor Ort
Merkzettel Warenkorb
Produktbild: Applications of Artificial Neural Networks and Machine Learning in Civil Engineering
- 19%

Applications of Artificial Neural Networks and Machine Learning in Civil Engineering

Aus der Reihe Studies in Computational Intelligence
19% sparen

171,99 € UVP 213,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

30.07.2024

Abbildungen

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

Verlag

Springer

Seitenzahl

474

Maße (L/B/H)

24,1/16/3,1 cm

Gewicht

988 g

Sprache

Englisch

ISBN

978-3-031-66050-4

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

30.07.2024

Abbildungen

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

Verlag

Springer

Seitenzahl

474

Maße (L/B/H)

24,1/16/3,1 cm

Gewicht

988 g

Sprache

Englisch

ISBN

978-3-031-66050-4

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

Noch keine Bewertungen vorhanden

Verfassen Sie die erste Bewertung zu diesem Artikel

Helfen Sie anderen Kundinnen und Kunden durch Ihre Meinung.

Kundinnen und Kunden meinen

Bewertungen (0)
  • 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.