Enhancing structural health monitoring with machine learning for accurate prediction of retrofitting effects

Abstract

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.

Keywords

• health monitoring
• retrofitting
• machine learning