Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
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| URI | http://purl.tuc.gr/dl/dias/1216A46D-06F0-4344-8786-5367CB56BD47 | - |
| Αναγνωριστικό | https://doi.org/10.1016/j.engstruct.2023.116912 | - |
| Αναγνωριστικό | https://www.sciencedirect.com/science/article/pii/S0141029623013275 | - |
| Γλώσσα | en | - |
| Μέγεθος | 20 pages | en |
| Τίτλος | A data-driven, machine learning scheme used to predict the structural response of masonry arches | en |
| Δημιουργός | Motsa Siphesihle Mpho | en |
| Δημιουργός | Stavroulakis Georgios | en |
| Δημιουργός | Σταυρουλακης Γεωργιος | el |
| Δημιουργός | Drosopoulos Georgios | en |
| Δημιουργός | Δροσοπουλος Γεωργιος | el |
| Εκδότης | Elsevier | en |
| Περιγραφή | 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 |
| Περίληψη | 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 |
| Τύπος | Peer-Reviewed Journal Publication | en |
| Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | el |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2025-07-30 | - |
| Ημερομηνία Δημοσίευσης | 2023 | - |
| Θεματική Κατηγορία | FEM | en |
| Θεματική Κατηγορία | Machine Learning | en |
| Θεματική Κατηγορία | Artificial Neural Network | en |
| Θεματική Κατηγορία | Multi-hinge failure | en |
| Θεματική Κατηγορία | Damage Prediction | en |
| Θεματική Κατηγορία | Masonry Arches | en |
| Θεματική Κατηγορία | Data-driven Mechanics | en |
| Θεματική Κατηγορία | Digital Twin | en |
| Βιβλιογραφική Αναφορά | 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 |
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