A New Damage Index Based on Statistical Features, PCA, and Mahalanobis Distance for Detecting and Locating Cables Loss in a Cable-Stayed Bridge

Cable-stayed bridges are widely used all around the world. Unfortunately, during their service life, they are exposed to adverse conditions that may cause their deterioration and, consequently, their collapse. Vibration-based structural health monitoring techniques have become the most promising alternatives for efficiently detecting and locating damage into civil structures. In this regard, this paper presents a new methodology based on statistical features, Principal component analysis (PCA), and Mahalanobis distance (MD) for detecting and locating a cable loss in the Rio Papaloapan bridge (RPB) using vibration signals. It is based on the extraction of a set of statistical time features (STFs) from vibration signals, which are analyzed using the autocorrelation function (ACF) to denoise and strengthen the features found in them. Then PCA-based models are computed by using the STFs to enhance the damage location process. Then a new damage index based on MD is proposed to indicate if a damage exists and its location.

Automated summary

This paper presents a methodology based on statistical features, Principal component analysis (PCA), and Mahalanobis distance (MD) for detecting and locating a cable loss in a bridge. Statistical time features (STFs) are extracted from vibration signals, analyzed using the autocorrelation function (ACF), and used to compute PCA-based models, followed by a new damage index based on MD for indicating the existence and location of damage.