Prediction of the Response of Masonry Walls under Blast Loading Using Artificial Neural Networks
A methodology to predict key aspects of the structural response of masonry walls under blast loading using artificial neural networks (ANN) is presented in this paper. The failure patterns of masonry walls due to in and out-of-plane loading are complex due to the potential opening and sliding of the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Infrastructures |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2412-3811/9/1/5 |
| _version_ | 1797343394560737280 |
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| author | Sipho G. Thango Georgios A. Drosopoulos Siphesihle M. Motsa Georgios E. Stavroulakis |
| author_facet | Sipho G. Thango Georgios A. Drosopoulos Siphesihle M. Motsa Georgios E. Stavroulakis |
| author_sort | Sipho G. Thango |
| collection | DOAJ |
| description | A methodology to predict key aspects of the structural response of masonry walls under blast loading using artificial neural networks (ANN) is presented in this paper. The failure patterns of masonry walls due to in and out-of-plane loading are complex due to the potential opening and sliding of the mortar joint interfaces between the masonry stones. To capture this response, advanced computational models can be developed requiring a significant amount of resources and computational effort. The article uses an advanced non-linear finite element model to capture the failure response of masonry walls under blast loads, introducing unilateral contact-friction laws between stones and damage mechanics laws for the stones. Parametric finite simulations are automatically conducted using commercial finite element software linked with MATLAB R2019a and Python. A dataset is then created and used to train an artificial neural network. The trained neural network is able to predict the out-of-plane response of the masonry wall for random properties of the blast load (standoff distance and weight). The results indicate that the accuracy of the proposed framework is satisfactory. A comparison of the computational time needed for a single finite element simulation and for a prediction of the out-of-plane response of the wall by the trained neural network highlights the benefits of the proposed machine learning approach in terms of computational time and resources. Therefore, the proposed approach can be used to substitute time consuming explicit dynamic finite element simulations and used as a reliable tool in the fast prediction of the masonry response under blast actions. |
| first_indexed | 2024-03-08T10:46:57Z |
| format | Article |
| id | doaj.art-23e9377486884c09b2c7adb8f574e2fb |
| institution | Directory Open Access Journal |
| issn | 2412-3811 |
| language | English |
| last_indexed | 2024-03-08T10:46:57Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Infrastructures |
| spelling | doaj.art-23e9377486884c09b2c7adb8f574e2fb2024-01-26T17:03:53ZengMDPI AGInfrastructures2412-38112023-12-0191510.3390/infrastructures9010005Prediction of the Response of Masonry Walls under Blast Loading Using Artificial Neural NetworksSipho G. Thango0Georgios A. Drosopoulos1Siphesihle M. Motsa2Georgios E. Stavroulakis3Discipline of Civil Engineering, University of KwaZulu Natal, Durban 4041, South AfricaDiscipline of Civil Engineering, University of KwaZulu Natal, Durban 4041, South AfricaDiscipline of Civil Engineering, University of KwaZulu Natal, Durban 4041, South AfricaSchool of Production Engineering & Management, Technical University of Crete, 73100 Chania, Crete, GreeceA methodology to predict key aspects of the structural response of masonry walls under blast loading using artificial neural networks (ANN) is presented in this paper. The failure patterns of masonry walls due to in and out-of-plane loading are complex due to the potential opening and sliding of the mortar joint interfaces between the masonry stones. To capture this response, advanced computational models can be developed requiring a significant amount of resources and computational effort. The article uses an advanced non-linear finite element model to capture the failure response of masonry walls under blast loads, introducing unilateral contact-friction laws between stones and damage mechanics laws for the stones. Parametric finite simulations are automatically conducted using commercial finite element software linked with MATLAB R2019a and Python. A dataset is then created and used to train an artificial neural network. The trained neural network is able to predict the out-of-plane response of the masonry wall for random properties of the blast load (standoff distance and weight). The results indicate that the accuracy of the proposed framework is satisfactory. A comparison of the computational time needed for a single finite element simulation and for a prediction of the out-of-plane response of the wall by the trained neural network highlights the benefits of the proposed machine learning approach in terms of computational time and resources. Therefore, the proposed approach can be used to substitute time consuming explicit dynamic finite element simulations and used as a reliable tool in the fast prediction of the masonry response under blast actions.https://www.mdpi.com/2412-3811/9/1/5blastmasonryin-plane deflectionout-of-plane deflectionexplicit dynamic non-linear finite element analysismachine learning |
| spellingShingle | Sipho G. Thango Georgios A. Drosopoulos Siphesihle M. Motsa Georgios E. Stavroulakis Prediction of the Response of Masonry Walls under Blast Loading Using Artificial Neural Networks Infrastructures blast masonry in-plane deflection out-of-plane deflection explicit dynamic non-linear finite element analysis machine learning |
| title | Prediction of the Response of Masonry Walls under Blast Loading Using Artificial Neural Networks |
| title_full | Prediction of the Response of Masonry Walls under Blast Loading Using Artificial Neural Networks |
| title_fullStr | Prediction of the Response of Masonry Walls under Blast Loading Using Artificial Neural Networks |
| title_full_unstemmed | Prediction of the Response of Masonry Walls under Blast Loading Using Artificial Neural Networks |
| title_short | Prediction of the Response of Masonry Walls under Blast Loading Using Artificial Neural Networks |
| title_sort | prediction of the response of masonry walls under blast loading using artificial neural networks |
| topic | blast masonry in-plane deflection out-of-plane deflection explicit dynamic non-linear finite element analysis machine learning |
| url | https://www.mdpi.com/2412-3811/9/1/5 |
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