Enhancing structural health monitoring with machine learning for accurate prediction of retrofitting effects
Structural health in civil engineering involved maintaining a structure's integrity and performance over time, resisting loads and environmental effects. Ensuring long-term functionality was vital to prevent accidents, economic losses, and service interruptions. Structural health monitoring (SH...
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AIMS Press
2024-10-01
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| Series: | AIMS Mathematics |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/math.20241472?viewType=HTML |
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| author | A. Presno Vélez M. Z. Fernández Muñiz J. L. Fernández Martínez |
| author_facet | A. Presno Vélez M. Z. Fernández Muñiz J. L. Fernández Martínez |
| author_sort | A. Presno Vélez |
| collection | DOAJ |
| description | Structural health in civil engineering involved maintaining a structure's integrity and performance over time, resisting loads and environmental effects. Ensuring long-term functionality was vital to prevent accidents, economic losses, and service interruptions. Structural health monitoring (SHM) systems used sensors to detect damage indicators such as vibrations and cracks, which were crucial for predicting service life and planning maintenance. Machine learning (ML) enhanced SHM by analyzing sensor data to identify damage patterns often missed by human analysts. ML models captured complex relationships in data, leading to accurate predictions and early issue detection. This research aimed to develop a methodology for training an artificial intelligence (AI) system to predict the effects of retrofitting on civil structures, using data from the KW51 bridge (Leuven). Dimensionality reduction with the Welch transform identified the first seven modal frequencies as key predictors. Unsupervised principal component analysis (PCA) projections and a K-means algorithm achieved $ 70 \% $ accuracy in differentiating data before and after retrofitting. A random forest algorithm achieved $ 99.19 \% $ median accuracy with a nearly perfect receiver operating characteristic (ROC) curve. The final model, tested on the entire dataset, achieved $ 99.77 \% $ accuracy, demonstrating its effectiveness in predicting retrofitting effects for other civil structures. |
| format | Article |
| id | doaj-art-20b4c56dbc1b4f04bc99cbffc8549c5e |
| institution | OA Journals |
| issn | 2473-6988 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | AIMS Mathematics |
| spelling | doaj-art-20b4c56dbc1b4f04bc99cbffc8549c5e2025-08-20T02:12:38ZengAIMS PressAIMS Mathematics2473-69882024-10-01911304933051410.3934/math.20241472Enhancing structural health monitoring with machine learning for accurate prediction of retrofitting effectsA. Presno Vélez0M. Z. Fernández Muñiz1 J. L. Fernández Martínez2Department of Mathematics, Group of inverse problems, optimization and machine learning, Oviedo University, c/Federico García Lorca 18, Oviedo 33007, SpainDepartment of Mathematics, Group of inverse problems, optimization and machine learning, Oviedo University, c/Federico García Lorca 18, Oviedo 33007, SpainDepartment of Mathematics, Group of inverse problems, optimization and machine learning, Oviedo University, c/Federico García Lorca 18, Oviedo 33007, SpainStructural health in civil engineering involved maintaining a structure's integrity and performance over time, resisting loads and environmental effects. Ensuring long-term functionality was vital to prevent accidents, economic losses, and service interruptions. Structural health monitoring (SHM) systems used sensors to detect damage indicators such as vibrations and cracks, which were crucial for predicting service life and planning maintenance. Machine learning (ML) enhanced SHM by analyzing sensor data to identify damage patterns often missed by human analysts. ML models captured complex relationships in data, leading to accurate predictions and early issue detection. This research aimed to develop a methodology for training an artificial intelligence (AI) system to predict the effects of retrofitting on civil structures, using data from the KW51 bridge (Leuven). Dimensionality reduction with the Welch transform identified the first seven modal frequencies as key predictors. Unsupervised principal component analysis (PCA) projections and a K-means algorithm achieved $ 70 \% $ accuracy in differentiating data before and after retrofitting. A random forest algorithm achieved $ 99.19 \% $ median accuracy with a nearly perfect receiver operating characteristic (ROC) curve. The final model, tested on the entire dataset, achieved $ 99.77 \% $ accuracy, demonstrating its effectiveness in predicting retrofitting effects for other civil structures. https://www.aimspress.com/article/doi/10.3934/math.20241472?viewType=HTMLhealth monitoringretrofittingmachine learning |
| spellingShingle | A. Presno Vélez M. Z. Fernández Muñiz J. L. Fernández Martínez Enhancing structural health monitoring with machine learning for accurate prediction of retrofitting effects AIMS Mathematics health monitoring retrofitting machine learning |
| title | Enhancing structural health monitoring with machine learning for accurate prediction of retrofitting effects |
| title_full | Enhancing structural health monitoring with machine learning for accurate prediction of retrofitting effects |
| title_fullStr | Enhancing structural health monitoring with machine learning for accurate prediction of retrofitting effects |
| title_full_unstemmed | Enhancing structural health monitoring with machine learning for accurate prediction of retrofitting effects |
| title_short | Enhancing structural health monitoring with machine learning for accurate prediction of retrofitting effects |
| title_sort | enhancing structural health monitoring with machine learning for accurate prediction of retrofitting effects |
| topic | health monitoring retrofitting machine learning |
| url | https://www.aimspress.com/article/doi/10.3934/math.20241472?viewType=HTML |
| work_keys_str_mv | AT apresnovelez enhancingstructuralhealthmonitoringwithmachinelearningforaccuratepredictionofretrofittingeffects AT mzfernandezmuniz enhancingstructuralhealthmonitoringwithmachinelearningforaccuratepredictionofretrofittingeffects AT jlfernandezmartinez enhancingstructuralhealthmonitoringwithmachinelearningforaccuratepredictionofretrofittingeffects |