Damage Detection Based on Structural Responses Induced by Traffic Load: Methodology and Application

Abstract

In consideration of the important role that bridges play in transportation system, their safety, durability and serviceability have always been deeply concerned. Recently, many long-span bridges have been instrumented with Structural Health Monitoring Systems (SHMS) to provide bridge engineers with the information needed for decisions-making in management and maintenance. However, efficient use of monitoring data remains a challenge confronted before engineers. Recently, methodologies based on monitoring data while robust to random disturbance and sensitive to damage have received worldwide attention. In this context, this study proposes an innovative damage detection methodology based on structural responses induced by traffic load. First, vehicle-induced strain responses are found to be separable from the strain induced by operational loads, owing to their unique characteristics. This is achieved by a detailed investigation on the relationship between strain measurements and operational loads including temperature, wind as well as vehicles based on long-term monitoring data. From the vehicular load and pertinent strain response, the strain influence line (SIL) can be further identified. As a structural signature, SIL can be used to provide a reasonable assessment of the bridge health condition at least in the vicinity of strain monitoring point. Two damage indexes are therefore derived from the identified SIL, which are promising for damage evaluation because they are: (a) capable of revealing structural deterioration; (b) immune to influences of environmental changes; (c) adaptable to the random characteristic exhibited by long-term monitoring data. Besides, the SIL identification procedure and its theoretical basis are elaborated to respectively handle the case where the vehicle load is available or not, which is also applicable to identify the influence line of other measurements such as stresses. The proposed damage methodology is applied to the cable-stayed bridge spanning the main navigation channel of Shanghai Yangtze River Bridge, and the result shows its effectiveness.

Bridge influence line identification based on adaptive B‐spline basis dictionary and sparse regularization

“…One type of simplification is to identify the coefficients of functions to represent BIL, instead of BIL directly. It is usually assumed that the coefficients corresponding to different discrete positions of BIL are irrelevant or weakly correlated, but the identified BIL may contain incredibly large fluctuations . Another type of simplification is to identify coefficients of a pre‐determined fitted function of BIL.…”

“…In recent years, many studies have been conducted to identify BIL by using dynamic testing methods . It is difficult to directly identify the continuous curve of BIL, and thus some assumptions are adopted to simplify the algorithms.…”

“…The existing researches of BIL identification using dynamic test methods demonstrate that the measured responses are inevitably interfered by the dynamic effects of the moving vehicle and the other loading as well as the measurement noise, which largely increases the difficulty of accurate identification . To avoid the BIL identification method being sensitive to the signal interferences, another challenge is how to properly deal with the ill‐posed inverse problem.…”

Vehicle-Assisted Techniques for Health Monitoring of Bridges

“…Failure of civil structures may lead to catastrophic consequences in terms of human life and economic assets. Then it is of utmost importance to ensure the structural integrity of bridges to meet safety, durability, and serviceability requirements [1]. Structural health monitoring (SHM) is a highly active research area with promising technological implications to evaluate structural performance and to identify damage during the service life of a structure [2].…”

Damage quantification of beam structures using deflection influence lines

“…Chen et al explored a group of damage localization indices on the basis of stress ILs for a long‐span suspension bridge and validated their effectiveness through a numerical case study of the Tsing Ma Bridge, whose main span is 1,377 m. Zhu et al further improved the accuracy of damage localization by fusing the information of multiple ILs. Meanwhile, the feasibility of extracting ILs of a bridge from moving‐vehicle‐induced responses has also been verified by using various methods …”