R/C buildings’ seismic damage prediction based on semi-supervised automatic differentiation variational inference deep autoencoder
Structural damage from earthquakes has been assessed using a variety of methodologies, both statistical and, more recently, utilizing Machine Learning (ML) algorithms. The effectiveness of data-driven procedures, even when applied to extremely time-consuming scenarios and data sets that reflect subs...
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| Veröffentlicht in: | Journal of physics. Conference series Jg. 2647; H. 19; S. 192015 - 192023 |
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| Sprache: | Englisch |
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01.06.2024
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| Abstract | Structural damage from earthquakes has been assessed using a variety of methodologies, both statistical and, more recently, utilizing Machine Learning (ML) algorithms. The effectiveness of data-driven procedures, even when applied to extremely time-consuming scenarios and data sets that reflect substantial expertise and research, completely depends on the quality of the underlying data. The performance of the intelligent model can also be impacted by a lack of in-depth knowledge and expertise in using complex machine learning architectures. This can also prevent some crucial hyperparameters from being adjusted, which ultimately reduces the algorithm’s reliability and generalizability. The present research offers a Bayesian-based semi-supervised Automatic Differentiation Variational Inference (ADVI) deep autoencoder for forecasting seismic damage of R/C buildings. It is a state-of-the-art, intelligent technology that automatically converts the variables in the issue into actual coordinate space using an upgraded ADVI technique. Finally, using a brand-new Adaptive Learning Rate Gradient Algorithm (ALRGA), it chooses a technique in this area that is a function of the changed variables and optimizes its parameters. Using the sophisticated ADVI technique to establish a posterior distribution without having an analytical solution is an upgraded version of the semi-supervised learning method. Estimating seismic damage to buildings is accelerated and greatly simplified by the suggested methodology, which eliminates the computational complexity of the analytical methods. By performing Nonlinear Time History Analyses of 3D R/C structures exposed to 65 earthquakes, a realistic dataset for the model evaluation is produced. The system’s strong generalizability and the proposed methodology’s detailed convergence stability reveal that it is a valuable method that can outperform other ML algorithms. |
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| AbstractList | Structural damage from earthquakes has been assessed using a variety of methodologies, both statistical and, more recently, utilizing Machine Learning (ML) algorithms. The effectiveness of data-driven procedures, even when applied to extremely time-consuming scenarios and data sets that reflect substantial expertise and research, completely depends on the quality of the underlying data. The performance of the intelligent model can also be impacted by a lack of in-depth knowledge and expertise in using complex machine learning architectures. This can also prevent some crucial hyperparameters from being adjusted, which ultimately reduces the algorithm’s reliability and generalizability. The present research offers a Bayesian-based semi-supervised Automatic Differentiation Variational Inference (ADVI) deep autoencoder for forecasting seismic damage of R/C buildings. It is a state-of-the-art, intelligent technology that automatically converts the variables in the issue into actual coordinate space using an upgraded ADVI technique. Finally, using a brand-new Adaptive Learning Rate Gradient Algorithm (ALRGA), it chooses a technique in this area that is a function of the changed variables and optimizes its parameters. Using the sophisticated ADVI technique to establish a posterior distribution without having an analytical solution is an upgraded version of the semi-supervised learning method. Estimating seismic damage to buildings is accelerated and greatly simplified by the suggested methodology, which eliminates the computational complexity of the analytical methods. By performing Nonlinear Time History Analyses of 3D R/C structures exposed to 65 earthquakes, a realistic dataset for the model evaluation is produced. The system’s strong generalizability and the proposed methodology’s detailed convergence stability reveal that it is a valuable method that can outperform other ML algorithms. |
| Author | Demertzis, K Iliadis, L Kostinakis, K Morfidis, K |
| Author_xml | – sequence: 1 givenname: K surname: Demertzis fullname: Demertzis, K organization: Hellenic Open University School of Science and Technology, Informatics Studies, Aristotle 18, 26335, Patra, Greece – sequence: 2 givenname: K surname: Kostinakis fullname: Kostinakis, K organization: Aristotle University campus Assistant Professor, Department of Civil Engineering, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece – sequence: 3 givenname: K surname: Morfidis fullname: Morfidis, K organization: Earthquake Planning and Protection Organization (EPPO-ITSAK) Assistant Researcher, Terma Dasylliou, 55535, Thessaloniki, Greece – sequence: 4 givenname: L surname: Iliadis fullname: Iliadis, L organization: Democritus University of Thrace School of Engineering, Department of Civil Engineering, Kimmeria, Xanthi, Greece |
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| Cites_doi | 10.3390/app122110754 10.1016/j.engstruct.2018.03.028 10.1061/(ASCE)ST.1943-541X.0000209 10.1016/j.advengsoft.2017.01.001 10.1016/j.engstruct.2019.109436 10.3390/rs11232765 |
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| DOI | 10.1088/1742-6596/2647/19/192015 |
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| References | Gomez-Cabrera (JPCS_2647_19_192015bib1) 2022; 12 Masi (JPCS_2647_19_192015bib18) 2011; 137 Morfidis (JPCS_2647_19_192015bib8) 2017 JPCS_2647_19_192015bib4 Carr (JPCS_2647_19_192015bib15) 2006 JPCS_2647_19_192015bib5 Morfidis (JPCS_2647_19_192015bib11) 2019 JPCS_2647_19_192015bib14 Morfidis (JPCS_2647_19_192015bib7) 2017; 106 JPCS_2647_19_192015bib13 Gunturi (JPCS_2647_19_192015bib16) 1992 Naeim (JPCS_2647_19_192015bib17) 2011 Demertzis (JPCS_2647_19_192015bib3) 2023; 63 Kostinakis (JPCS_2647_19_192015bib12) 2020; 75 Crisafulli (JPCS_2647_19_192015bib6) 1997 Morfidis (JPCS_2647_19_192015bib10) 2019; 197 Morfidis (JPCS_2647_19_192015bib9) 2018; 165 Nex (JPCS_2647_19_192015bib2) 2019; 11 |
| References_xml | – volume: 75 start-page: 295 year: 2020 ident: JPCS_2647_19_192015bib12 article-title: Optimization of the seismic performance of masonry infilled R/C buildings at the stage of design using artificial neural networks publication-title: Structural Engineering and Mechanics – year: 2019 ident: JPCS_2647_19_192015bib11 – year: 1992 ident: JPCS_2647_19_192015bib16 – volume: 12 start-page: 10754 year: 2022 ident: JPCS_2647_19_192015bib1 article-title: Review of Machine-Learning Techniques Applied to Structural Health Monitoring Systems for Building and Bridge Structures publication-title: Appl. Sci. doi: 10.3390/app122110754 – ident: JPCS_2647_19_192015bib4 – volume: 165 start-page: 120 year: 2018 ident: JPCS_2647_19_192015bib9 article-title: Approaches to the rapid seismic damage prediction of r/c buildings using artificial neural networks publication-title: Eng Struct doi: 10.1016/j.engstruct.2018.03.028 – ident: JPCS_2647_19_192015bib5 – volume: 63 year: 2023 ident: JPCS_2647_19_192015bib3 article-title: An interpretable machine learning method for the prediction of R/C buildings’ seismic response publication-title: J. Build. Eng. – year: 1997 ident: JPCS_2647_19_192015bib6 – year: 2017 ident: JPCS_2647_19_192015bib8 – volume: 137 start-page: 367 year: 2011 ident: JPCS_2647_19_192015bib18 article-title: Selection of natural and synthetic accelerograms for seismic vulnerability studies on reinforced concrete frames publication-title: J Struct Eng doi: 10.1061/(ASCE)ST.1943-541X.0000209 – volume: 106 start-page: 1 year: 2017 ident: JPCS_2647_19_192015bib7 article-title: Seismic parameters’ combinations for the optimum prediction of the damage state of R/C buildings using neural networks publication-title: Advances in Engineering Software doi: 10.1016/j.advengsoft.2017.01.001 – volume: 197 year: 2019 ident: JPCS_2647_19_192015bib10 article-title: Comparative evaluation of MFP and RBF neural networks’ ability for instant estimation of r/c buildings’ seismic damage level publication-title: Engineering Structures doi: 10.1016/j.engstruct.2019.109436 – ident: JPCS_2647_19_192015bib13 – ident: JPCS_2647_19_192015bib14 – year: 2006 ident: JPCS_2647_19_192015bib15 – year: 2011 ident: JPCS_2647_19_192015bib17 – volume: 11 start-page: 2765 year: 2019 ident: JPCS_2647_19_192015bib2 article-title: Structural Building Damage Detection with Deep Learning: Assessment of a State-of-the-Art CNN in Operational Conditions publication-title: Remote Sens. doi: 10.3390/rs11232765 |
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| SubjectTerms | Adaptive algorithms Adaptive learning Algorithms Buildings Complexity Damage assessment Differentiation Earthquake damage Earthquake prediction Earthquakes Exact solutions Inference Machine learning Mathematical analysis Semi-supervised learning Structural damage Upgrading |
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| Title | R/C buildings’ seismic damage prediction based on semi-supervised automatic differentiation variational inference deep autoencoder |
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