Search

In this article, the condition assessment of a cable-stayed bridge using remote sensing is presented. The displacement influence line (DIL) of the bridge under the live load tests is measured for a discrete number of target points. Three different remote sensing techniques including, laser scanning, terrestrial robotic total station and digital levelling are adopted for this purpose. It is demonstrated that DIL obtained by non-contact system is capable of identifying an emulated damage in an actual operating system. The contribution of the work is fourfold. First, a damage index based on the displacement profile of the bridge under the weigh-in-motion is extracted from the non-contact sensing system. Second, our study compares three different remote sensing techniques, namely, digital levelling, robotic total station and laser scanning and uses the measurements to validate the finite element model. Third, the effectiveness of the proposed method for structural damage identification is validated in a real-world large-scale operating structure. Finally, it is validated that strain-based influence line is highly likely to misidentify damage especially when the location of damage is not in the close proximity of the sensor; however, DIL is a better damage indicator even if damage occurs far from the measurement point. [ABSTRACT FROM AUTHOR]

Highlights • A novel and robust damage identification technique based on the Rotation Influence Line (RIL) is proposed which has not been reported in the literature. • The method only relies on two measurement points at either end of the bridge. • The method is capable of identifying damage, even far from the sensor location. • The validation of the technique is demonstrated through extensive numerical and field test investigations on a statically indeterminate cable-stayed bridge structure. • It is demonstrated that the conventional strain-based measurement is very likely to misidentify cable damage even with extreme case of cable loss. Abstract This study proposes a new damage identification technique for condition assessment of bridge structures. The method is based on the concept of rotation influence line (RIL) at the bridge bearing locations, and solely relies on measurements obtained from two points at either end of the bridge e.g., RIL R and RIL L. The sensitivity of the rotation measurement to damage is first investigated using a 1-D simply supported beam model and it is demonstrated that unlike conventional strain measurements, the rotation measurement is capable of providing useful information about damage even though it is far from the measurement point. Further, an existing cable-stayed bridge is considered to validate the capability of RIL in damage identification. A comprehensive three-dimensional finite element model (FEM) of the bridge is established and calibrated using the measured static and dynamic responses. Numerous field tests are conducted on this large-scale structure to extract static and dynamic characteristics, including natural frequencies, mode shapes and influence lines (ILs). Sixteen hypothetical damage scenarios are induced in the numerical model, including symmetric and asymmetric cases of cable loss. A damage index based on the normalised discrepancy of RIL between the benchmark state and an unknown state is introduced and through extensive investigations it is demonstrated that regardless of damage location, either RIL R or RIL L (or both) can successfully identify the induced damage in all of the sixteen damage scenarios. In contrast, the success of strain-based measurement is highly dependent on the closeness of damage to the sensor location, thus a much higher number of strain gauge sensors is required not to misidentify damage. The contribution of this work is four-fold. First, a novel and robust damage identification technique based on RIL is proposed which has not been reported in the literature. Second, the method solely relies on two measurement points e.g., two tilt meters at either end of the bridge and it is capable of identifying damage, even far from the sensor location. Third, the validation of the technique is demonstrated through extensive numerical and field test investigations on a statically indeterminate cable-stayed bridge structure. Finally, it is demonstrated that the conventional strain-based measurement is very likely to misidentify cable damage even with extreme case of cable loss. [ABSTRACT FROM AUTHOR]

A large-scale cable-stayed bridge in the state of New South Wales, Australia, has been extensively instrumented with an array of accelerometer, strain gauge, and environmental sensors. The real-time continuous response of the bridge has been collected since July 2016. This study aims at condition assessment of this bridge by investigating three aspects of structural health monitoring including damage detection, damage localization, and damage severity assessment. A novel data analysis algorithm based on incremental multi-way data analysis is proposed to analyze the dynamic response of the bridge. This method applies incremental tensor analysis for data fusion and feature extraction, and further uses one-class support vector machine on this feature to detect anomalies. A total of 15 different damage scenarios were investigated; damage was physically simulated by locating stationary vehicles with different masses at various locations along the span of the bridge to change the condition of the bridge. The effect of damage on the fundamental frequency of the bridge was investigated and a maximum change of 4.4% between the intact and damage states was observed which corresponds to a small severity damage. Our extensive investigations illustrate that the proposed technique can provide reliable characterization of damage in this cable-stayed bridge in terms of detection, localization and assessment. The contribution of the work is threefold; first, an extensive structural health monitoring system was deployed on a cable-stayed bridge in operation; second, an incremental tensor analysis was proposed to analyze time series responses from multiple sensors for online damage identification; and finally, the robustness of the proposed method was validated using extensive field test data by considering various damage scenarios in the presence of environmental variabilities. [ABSTRACT FROM AUTHOR]