Damage cross detection between bridges monitored within one cluster using the difference ratio of projected strain monitoring data

Abstract

The bridges monitored within one cluster refer to several medium- and small-span beam bridges with the same or similar structural characteristics located in a local road network, and these bridges suffer the similar traffic and environmental loads. It is still little research on how to detect the damage of all bridges monitored within one cluster utilizing the above-mentioned characteristics of these bridges. To address this issue, a method is proposed for the damage cross-detection between bridges monitored within one cluster by using the difference ratio of projected strain monitoring data under time-varying environmental temperatures. First, a damage feature is established by using the difference ratio of projected strain monitoring data obtained from the same cross-section position of any two bridges monitored within one cluster that have similar or identical structural characteristics. On this basis, the relationship between the statistical characteristics of the proposed damage feature and the degree of structural similarity between two bridges are discussed in detail. Second, a damage detection index is presented by calculating the subspace angle between two damage features. Then, combined with kernel density estimation and a cross-validation strategy, the proposed index is implemented to detect the damage of all bridges monitored within one cluster. Finally, numerical simulation examples are utilized to analyse and discuss the application limitations, noise resistance performance and structural damage sensitivity of the proposed method. Moreover, the effectiveness of the proposed method is also demonstrated by using the strain monitoring data obtained from actual bridges monitored within one cluster.

Condition Diagnosis of Long-Span Bridge Pile Foundations Based on the Spatial Correlation of High-Density Strain Measurement Points

“…To improve the speed of calculation, the soil was simulated by spring elements. The substructure of the bridge is a pile group, incorporating 35…”

“…To improve the speed of calculation, the soil was simulated by spring elements. The substructure of the bridge is a pile group, incorporating 35 A random load was applied to the bridge, and the COMBIN14 element was used to simulate the soil layer around the pile foundation. All degrees of freedom were constrained at the bottom of the side tower.…”

“…In recent years, with the progress of sensor technology, especially the development of distributed optical fiber sensors [33] and the improvement in the theory of structural health monitoring [34,35], many scholars have studied detection and monitoring methods for pile foundations. Concerning the application of point strain sensors in pile foundation monitoring, Shu et al [36] proposed a damage identification method for pile foundations by using two symmetrically arranged strain sensor arrays.…”

Detecting cracks in concrete structures with the baseline model of the visual characteristics of images

“…To address this issue, the structure health monitoring (SHM) technique has been proposed to diagnose structural damage and assess the condition of a structure by utilizing a large amount of monitoring data (Amezquita‐Sanchez & Adeil, 2015a; Y. Liu et al., 2021; S. Zhang & Liu, 2020). In the early state of SHM development, vibration‐based damage detection and model update techniques were extensively studied (Y. Liu & Zhang, 2017; C. Yang & Liu, 2021) and have been widely applied to actual structures (Cao et al., 2021; Curry & Cremona, 2012; Ou & Li, 2010). In recent years, with the promotion of visual sensing equipment and the significant progress of computer vision (CV) technology, CV‐based SHM technology has received extensive attention in the field of SHM (Gao & Mosalam, 2018; Xue & Li, 2018; X. Zhang et al., 2019), and CV modules have gradually become an indispensable part of modern SHM frameworks (Dai & Cao, 2017; Y.‐F.…”

Bayesian Updates for an Extreme Value Distribution Model of Bridge Traffic Load Effect Based on SHM Data

“…In recognition of this, many studies have been conducted to improve the measurement of the site-specific traffic load. One of the most effective methods is the weigh-in-motion (WIM) technology [19][20][21], while others have tried to obtain traffic load effects directly through structural health monitoring (SHM) technology [22][23][24][25].…”