Application of the polynomial chaos expansion method for forecasting structural response of two full-scale case studies
Predicting the behavior of engineering structures with high accuracy remains a challenging task as a result of their continuous interaction with the immediate environment and varying operating conditions. In that context, forecasting tools are primarily focused on the creation of a model of a so-cal...
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| Format: | Article |
| Language: | English |
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Society for Materials and Structures testing of Serbia
2025-01-01
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| Series: | Građevinski Materijali i Konstrukcije |
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| Online Access: | https://scindeks-clanci.ceon.rs/data/pdf/2217-8139/2025/2217-81392501001G.pdf |
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| _version_ | 1850155016512339968 |
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| author | Georgijev Viktor Bogoevska Simona |
| author_facet | Georgijev Viktor Bogoevska Simona |
| author_sort | Georgijev Viktor |
| collection | DOAJ |
| description | Predicting the behavior of engineering structures with high accuracy remains a challenging task as a result of their continuous interaction with the immediate environment and varying operating conditions. In that context, forecasting tools are primarily focused on the creation of a model of a so-called baseline system. This established model serves as a foundation for identifying changes when new outputs deviate from the predictions made by the model. Physics-based numerical models, like the finite element method, often carry significant uncertainty stemming from assumptions regarding structural characteristics, environmental influences, and various loads affecting the system under study. Consequently, identifying the source of any existing discrepancies between obtained model results and measured data is difficult. This paper demonstrates a straightforward implementation of the polynomial chaos expansion method for the formulation of prognostic data-driven models targeted at tracking changes in continuously measured structural response. The method's effectiveness and positive features are showcased via practical application onto two full-scale engineering structures: a concrete arch dam and an industrial steel chimney. The models utilize environmental as well as response data collected over two years and two months of monitoring of these structures, respectively. The obtained results reveal the models' considerable potential as a long-term monitoring tool for autonomous assessment of structural behavior. |
| format | Article |
| id | doaj-art-10b9ae3744ec44fabdfd44e2aed261f6 |
| institution | OA Journals |
| issn | 2217-8139 2335-0229 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Society for Materials and Structures testing of Serbia |
| record_format | Article |
| series | Građevinski Materijali i Konstrukcije |
| spelling | doaj-art-10b9ae3744ec44fabdfd44e2aed261f62025-08-20T02:25:07ZengSociety for Materials and Structures testing of SerbiaGrađevinski Materijali i Konstrukcije2217-81392335-02292025-01-0168111210.5937/GRMK2400013G2217-81392501001GApplication of the polynomial chaos expansion method for forecasting structural response of two full-scale case studiesGeorgijev Viktor0https://orcid.org/0009-0009-3190-7223Bogoevska Simona1https://orcid.org/0000-0003-2803-9057Matrics engineering GmbH, Munich, GermanyUniversity of Ss. Cyril and Methodius, Faculty of Civil Engineering, Skopje, North MacedoniaPredicting the behavior of engineering structures with high accuracy remains a challenging task as a result of their continuous interaction with the immediate environment and varying operating conditions. In that context, forecasting tools are primarily focused on the creation of a model of a so-called baseline system. This established model serves as a foundation for identifying changes when new outputs deviate from the predictions made by the model. Physics-based numerical models, like the finite element method, often carry significant uncertainty stemming from assumptions regarding structural characteristics, environmental influences, and various loads affecting the system under study. Consequently, identifying the source of any existing discrepancies between obtained model results and measured data is difficult. This paper demonstrates a straightforward implementation of the polynomial chaos expansion method for the formulation of prognostic data-driven models targeted at tracking changes in continuously measured structural response. The method's effectiveness and positive features are showcased via practical application onto two full-scale engineering structures: a concrete arch dam and an industrial steel chimney. The models utilize environmental as well as response data collected over two years and two months of monitoring of these structures, respectively. The obtained results reveal the models' considerable potential as a long-term monitoring tool for autonomous assessment of structural behavior.https://scindeks-clanci.ceon.rs/data/pdf/2217-8139/2025/2217-81392501001G.pdfstructural health monitoringpolynomial chaos expansiondata-driven modelsengineering structuressensitivityuncertaintyforecasting |
| spellingShingle | Georgijev Viktor Bogoevska Simona Application of the polynomial chaos expansion method for forecasting structural response of two full-scale case studies Građevinski Materijali i Konstrukcije structural health monitoring polynomial chaos expansion data-driven models engineering structures sensitivity uncertainty forecasting |
| title | Application of the polynomial chaos expansion method for forecasting structural response of two full-scale case studies |
| title_full | Application of the polynomial chaos expansion method for forecasting structural response of two full-scale case studies |
| title_fullStr | Application of the polynomial chaos expansion method for forecasting structural response of two full-scale case studies |
| title_full_unstemmed | Application of the polynomial chaos expansion method for forecasting structural response of two full-scale case studies |
| title_short | Application of the polynomial chaos expansion method for forecasting structural response of two full-scale case studies |
| title_sort | application of the polynomial chaos expansion method for forecasting structural response of two full scale case studies |
| topic | structural health monitoring polynomial chaos expansion data-driven models engineering structures sensitivity uncertainty forecasting |
| url | https://scindeks-clanci.ceon.rs/data/pdf/2217-8139/2025/2217-81392501001G.pdf |
| work_keys_str_mv | AT georgijevviktor applicationofthepolynomialchaosexpansionmethodforforecastingstructuralresponseoftwofullscalecasestudies AT bogoevskasimona applicationofthepolynomialchaosexpansionmethodforforecastingstructuralresponseoftwofullscalecasestudies |