Institutional Repository
Technical University of Crete
Technical University of Crete
EN
|
EL
| URI | http://purl.tuc.gr/dl/dias/1216A46D-06F0-4344-8786-5367CB56BD47 | - |
| Identifier | https://doi.org/10.1016/j.engstruct.2023.116912 | - |
| Identifier | https://www.sciencedirect.com/science/article/pii/S0141029623013275 | - |
| Language | en | - |
| Extent | 20 pages | en |
| Title | A data-driven, machine learning scheme used to predict the structural response of masonry arches | en |
| Creator | Motsa Siphesihle Mpho | en |
| Creator | Stavroulakis Georgios | en |
| Creator | Σταυρουλακης Γεωργιος | el |
| Creator | Drosopoulos Georgios | en |
| Creator | Δροσοπουλος Γεωργιος | el |
| Publisher | Elsevier | en |
| Description | Siphesihle Mpho Motsa has been supported by Erasmus + Program within the framework of action “International Credit Mobility” between the Technical University of Crete, School of Production Engineering and Management and the University of KwaZulu-Natal, department of Civil engineering under Structural Engineering & Computational Mechanics (SECM) Group. | en |
| Content Summary | A data-driven methodology is proposed, for the investigation of the ultimate response of masonry arches. Aiming to evaluate their structural response in a computationally efficient framework, machine learning metamodels, in the form of artificial neural networks, are adopted. Datasets are numerically built, integrating Matlab, Python and commercial finite element software. Heyman’s assumptions are adopted within non-linear finite element analysis, incorporating contact-friction laws between adjacent stones, to capture failure in the arch. The artificial neural networks are trained, validated, and tested using the least square minimization technique. It is shown that the proposed scheme can be used to provide a fast and accurate prediction of the deformed geometry, the collapse mechanism and the ultimate load. Cases studies demonstrate the efficiency of the method in random, new arch geometries. Relevant Matlab/Python scripts and datasets are provided. The method can be extended towards structural health monitoring and the concept of digital twin. | en |
| Type of Item | Peer-Reviewed Journal Publication | en |
| Type of Item | Δημοσίευση σε Περιοδικό με Κριτές | el |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2025-07-30 | - |
| Date of Publication | 2023 | - |
| Subject | FEM | en |
| Subject | Machine Learning | en |
| Subject | Artificial Neural Network | en |
| Subject | Multi-hinge failure | en |
| Subject | Damage Prediction | en |
| Subject | Masonry Arches | en |
| Subject | Data-driven Mechanics | en |
| Subject | Digital Twin | en |
| Bibliographic Citation | S. M. Motsa, G. Ε. Stavroulakis, and G. A. Drosopoulos, “A data-driven, machine learning scheme used to predict the structural response of masonry arches,” Eng. Struct., vol. 296, Dec. 2023, doi: 10.1016/j.engstruct.2023.116912. | el |
Copyright © DIAS 2013
