Applications of Artificial Neural Networks and Machine Learning in Civil Engineering (Record no. 62009)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 05236nam a22005175i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-031-66051-1 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | DE-He213 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20251030112656.0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr nn 008mamaa |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 240729s2024 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9783031660511 |
| -- | 978-3-031-66051-1 |
| 024 7# - OTHER STANDARD IDENTIFIER | |
| Standard number or code | 10.1007/978-3-031-66051-1 |
| Source of number or code | doi |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | Q342 |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | UYQ |
| Source | bicssc |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | COM004000 |
| Source | bisacsh |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | UYQ |
| Source | thema |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Kaveh, Ali. |
| Relator term | author. |
| Relator code | aut |
| -- | http://id.loc.gov/vocabulary/relators/aut |
| 9 (RLIN) | 70082 |
| 245 10 - TITLE STATEMENT | |
| Title | Applications of Artificial Neural Networks and Machine Learning in Civil Engineering |
| Medium | [electronic resource] / |
| Statement of responsibility, etc. | by Ali Kaveh. |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2024. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Cham : |
| Name of producer, publisher, distributor, manufacturer | Springer Nature Switzerland : |
| -- | Imprint: Springer, |
| Date of production, publication, distribution, manufacture, or copyright notice | 2024. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | XVI, 474 p. 277 illus., 194 illus. in color. |
| Other physical details | online resource. |
| 336 ## - CONTENT TYPE | |
| Content type term | text |
| Content type code | txt |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type term | computer |
| Media type code | c |
| Source | rdamedia |
| 338 ## - CARRIER TYPE | |
| Carrier type term | online resource |
| Carrier type code | cr |
| Source | rdacarrier |
| 347 ## - DIGITAL FILE CHARACTERISTICS | |
| File type | text file |
| Encoding format | |
| Source | rda |
| 490 1# - SERIES STATEMENT | |
| Series statement | Studies in Computational Intelligence, |
| International Standard Serial Number | 1860-9503 ; |
| Volume/sequential designation | 1168 |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 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. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | 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. Nowadays, the world has witnessed unprecedented advances in technology and computer science. Artificial intelligence has emerged as a top field captivating global attention. Often referred to as AI, this technology stands apart from other disciplines as it aims to design machines and systems that exhibit intelligence, learn autonomously, and make decisions akin to humans. In order to comprehend the impact of this innovation, one must delve into the workings of artificial intelligence, trace its historical evolution from inception to the present day, and explore its diverse applications in domains like medicine, transportation, broadcasting, and marketing. Artificial intelligence introduces a transformative element to our reality, fostering significant breakthroughs and innovations. The book is used in any AI course, in particular, in Civil Engineering. It is also utilized in various fields of Industrial Civil Engineering. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | |
| 9 (RLIN) | 64052 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | |
| 9 (RLIN) | 64052 |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | |
| 710 2# - ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | SpringerLink (Online service) |
| 773 0# - HOST ITEM ENTRY | |
| Title | Springer Nature eBook |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Relationship information | Printed edition: |
| International Standard Book Number | 9783031660504 |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Relationship information | Printed edition: |
| International Standard Book Number | 9783031660528 |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Relationship information | Printed edition: |
| International Standard Book Number | 9783031660535 |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| Uniform title | Studies in Computational Intelligence, |
| International Standard Serial Number | 1860-9503 ; |
| Volume number/sequential designation | 1168 |
| 9 (RLIN) | 68854 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://doi.org/10.1007/978-3-031-66051-1">https://doi.org/10.1007/978-3-031-66051-1</a> |
| 912 ## - | |
| -- | ZDB-2-INR |
| 912 ## - | |
| -- | ZDB-2-SXIT |
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