A Neural Network Inverse Optimization Procedure for Constitutive Parameter Identification and Failure Mode Estimation of Laterally Loaded Unreinforced Masonry Walls
A new Neural Network Optimization (NNO) algorithm for constitutive material parameter identification based on inverse analysis of experimental tests of small-scale masonry prisms under compressive loads is presented. The Concrete Damaged Plasticity (CDP) constitutive model is used for the brick and...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | CivilEng |
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| Online Access: | https://www.mdpi.com/2673-4109/2/4/51 |
| _version_ | 1797505786317897728 |
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| author | Qudama Albu-Jasim George Papazafeiropoulos |
| author_facet | Qudama Albu-Jasim George Papazafeiropoulos |
| author_sort | Qudama Albu-Jasim |
| collection | DOAJ |
| description | A new Neural Network Optimization (NNO) algorithm for constitutive material parameter identification based on inverse analysis of experimental tests of small-scale masonry prisms under compressive loads is presented. The Concrete Damaged Plasticity (CDP) constitutive model is used for the brick and mortar of the Unreinforced Masonry (URM) walls. By comparisons with experimental data taken from laboratory tests, it is demonstrated that the constitutive parameters calibrated by application of the proposed inverse optimization procedure on the small-scale (prism) experimental results are sufficiently accurate to allow for the prediction of the mechanical response of large-scale URM walls subject to compressive and lateral loads. This eliminates the need for large-scale URM wall experimental tests for the identification of their material properties, making the calibration process more economic. After verifying the accuracy of the calibrated constitutive parameters based on the above comparisons, a numerical parametric study is performed for the investigation of the effect of material behavior and geometrical aspect ratios on the failure mechanisms of large-scale URM walls. |
| first_indexed | 2024-03-10T04:24:18Z |
| format | Article |
| id | doaj.art-dad38215c8cd4fdd92c193a10c1bbb28 |
| institution | Directory Open Access Journal |
| issn | 2673-4109 |
| language | English |
| last_indexed | 2024-03-10T04:24:18Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | CivilEng |
| spelling | doaj.art-dad38215c8cd4fdd92c193a10c1bbb282023-11-23T07:44:40ZengMDPI AGCivilEng2673-41092021-11-012494396810.3390/civileng2040051A Neural Network Inverse Optimization Procedure for Constitutive Parameter Identification and Failure Mode Estimation of Laterally Loaded Unreinforced Masonry WallsQudama Albu-Jasim0George Papazafeiropoulos1Department of Civil and Environmental Engineering, College of Engineering, Texas A&M University, 400 Bizzell Street, College Station, TA 77840, USADepartment of Structural Engineering, Zografou Campus, National Technical University of Athens, 9, Iroon Polytechniou Street, 15780 Athens, GreeceA new Neural Network Optimization (NNO) algorithm for constitutive material parameter identification based on inverse analysis of experimental tests of small-scale masonry prisms under compressive loads is presented. The Concrete Damaged Plasticity (CDP) constitutive model is used for the brick and mortar of the Unreinforced Masonry (URM) walls. By comparisons with experimental data taken from laboratory tests, it is demonstrated that the constitutive parameters calibrated by application of the proposed inverse optimization procedure on the small-scale (prism) experimental results are sufficiently accurate to allow for the prediction of the mechanical response of large-scale URM walls subject to compressive and lateral loads. This eliminates the need for large-scale URM wall experimental tests for the identification of their material properties, making the calibration process more economic. After verifying the accuracy of the calibrated constitutive parameters based on the above comparisons, a numerical parametric study is performed for the investigation of the effect of material behavior and geometrical aspect ratios on the failure mechanisms of large-scale URM walls.https://www.mdpi.com/2673-4109/2/4/51unreinforced masonry wallsartificial neural networkoptimizationinverse problem |
| spellingShingle | Qudama Albu-Jasim George Papazafeiropoulos A Neural Network Inverse Optimization Procedure for Constitutive Parameter Identification and Failure Mode Estimation of Laterally Loaded Unreinforced Masonry Walls CivilEng unreinforced masonry walls artificial neural network optimization inverse problem |
| title | A Neural Network Inverse Optimization Procedure for Constitutive Parameter Identification and Failure Mode Estimation of Laterally Loaded Unreinforced Masonry Walls |
| title_full | A Neural Network Inverse Optimization Procedure for Constitutive Parameter Identification and Failure Mode Estimation of Laterally Loaded Unreinforced Masonry Walls |
| title_fullStr | A Neural Network Inverse Optimization Procedure for Constitutive Parameter Identification and Failure Mode Estimation of Laterally Loaded Unreinforced Masonry Walls |
| title_full_unstemmed | A Neural Network Inverse Optimization Procedure for Constitutive Parameter Identification and Failure Mode Estimation of Laterally Loaded Unreinforced Masonry Walls |
| title_short | A Neural Network Inverse Optimization Procedure for Constitutive Parameter Identification and Failure Mode Estimation of Laterally Loaded Unreinforced Masonry Walls |
| title_sort | neural network inverse optimization procedure for constitutive parameter identification and failure mode estimation of laterally loaded unreinforced masonry walls |
| topic | unreinforced masonry walls artificial neural network optimization inverse problem |
| url | https://www.mdpi.com/2673-4109/2/4/51 |
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