Narrow Results

A novel method was proposed for the moving load identification of bridges based on the influence line theory and distributed optical fiber sensing technique. The method of load and vehicle speed identification was firstly theoretically studied, and then numerical simulation was also performed to study its accuracy and robustness. The numerical results showed that this method was characterized by high accuracy and excellent resistance to noise. Finally, the load identification of an actual continuous pre-stressed concrete beam bridge was carried out with the proposed method. The bridge consists of four pre-stressed box beams. At the same time, a weigh-in-motion system was also installed about 200 m in front of the bridge to measure the speed and moving loads with a purpose of comparing the load identification of the proposed method. Long gauge fiber Bragg grating (FBG) sensors with a gauge length of 1.0 m were adhered to the bottom of the beams. The individual loaded vehicles and the corresponding structure response were mainly monitored as standard samples, and the speed and weight of the sample vehicles were monitored and identified with the proposed method. The results revealed that the distributed long gauge FBG sensors were capable of sensing the structure response precisely and identifying the traffic load. On the basis of the design information and ambient vibration testing results, a refined model was established and the response under unit moving load was acquired for load identification. It was also shown that the sensors in different positions can achieve accurate vehicle speed and weight, the relative error of which are within 10% and 15%, respectively.

Numerous long-span suspension bridges have been built worldwide over the past few decades. To ensure the safety of such bridges and their users during the bridge service life, several bridges have been equipped with Structural Health Monitoring Systems (SHMSs), which measure dynamic bridge responses and various loading types on-site. Integrating SHMS and damage detection technology for condition assessment of these bridges has become a new development trend. Recent studies have proven that stress influence line (SIL)-based damage indices achieve excellent damage detection performance for a long suspension bridge. However, an accurate and prompt manner of identifying the SIL of a long suspension bridge is important to facilitate the development of the SIL for an effective damage index. Identifying the SIL from field measurement data under in-service conditions has several advantages over the traditional static loading test. This study proposes and develops a new SIL identification method by integrating the least squares solution and Weighted Moving Average (WMA) based on the measured train information and the corresponding train-induced stress time history. The efficacy of the proposed method is validated through its application to Tsing Ma Bridge (TMB). The good agreement between the identified and baseline SILs for a typical diagonal truss member verifies the effectiveness of the proposed method. Furthermore, robustness testing is performed by identifying SIL on the basis of information on different trains and train-induced stress responses and by identifying the SIL of different types of bridge components. Results indicate the feasibility of the application of the proposed approach to SIL identification for long-span bridges.

Bridge influence lines (BILs) and bridge influence surfaces (BISs) are inherent static parameters of bridges which can be extracted from moving vehicle-induced bridge responses. Compared with dynamic parameters, these parameters are directly related to the stiffness and internal forces in each cross-section of a bridge therefore can be considered as an effective bridge metamodel. To accelerate the engineering practice of BIL- and BIS-based bridge evaluation, this paper first briefly reviews the current BIL and BIS field test and identification methods. Then, the bridge evaluation guidelines of China and the United States are introduced as the basis of the evaluation methods. Engineering application scenarios for bridge evaluation, including permit load verification, performance degradation checking, and load carrying capacity evaluation, are summarized, and a multiple-scenario bridge evaluation method is established. At the end of this paper, an evaluation example of a four-span continuous bridge is presented to illustrate the application procedure and verify the effectiveness of the proposed method. The outcomes of this paper provide a promising application method of field test BILs and BISs, which may help bridge engineers more effectively use these parameters in engineering practice.

Simply supported bridges are the main bridge types in many transportation systems, and their damage can significantly reduce their load-carrying capacity. To detect possible damage, the time history and spatial distribution of the static responses of bridges (deflection, rotation, and strain influence lines/deformation curves) have recently been proposed as important indicators due to their good local damage detection ability and low testing cost. This paper attempts to establish connections between different static curve-based damage indicators through the various forms of Maxwell-Betti’s law. Damage indicators related to seven static curves are then rewritten as a unified framework. The framework states that all these static curves are directly related to the flexural stiffness distribution of the main girder for the simply supported bridge. Then, the theoretical formulations for the difference between the static curves of bridges in intact and damaged states are derived, and the response difference surfaces (RDSs) are plotted to visualize the sensitivity of different static curves to damage. Sensor placement suggestions for stiffness degradation evaluation are presented based on the damage sensitivity analysis at the end of this paper. The results of this study provide comprehensive theoretical support for static response-based damage identification of simply supported bridges.

Many damage identification methods are based on a comparison of structure response between healthy and damaged states. However, the healthy state response of especially old bridges is rarely available. In this paper, a new output-only damage identification approach is proposed to locate damage in multi-span bridges. In this method, only the damaged state strain and displacement influence lines are required. This approach is theoretically explained for a three-span girder. The middle span, the health condition of which is in quest, is modeled as a single-span beam, supported by rotational springs with unknown stiffness. A new algorithm is proposed to estimate the healthy state displacement influence line of the middle point of the span. In this algorithm, the ratio of the flexural stiffness of the girder to the spring’s rotational stiffness is determined in such a way that the estimated displacement, is as close to the measured response, as possible. Then, the estimated displacement is scaled based on two constraints. First, it is expected to be lower than the pseudo-static component of measured displacement because damage increases the response globally. Second, the difference between the scaled and pseudo-static component of the measured response is minimized. Eventually, damage is located using a novel damage index based on the local deviation of pseudo-static component of measured displacement from the estimated scaled response. The proposed method is numerically evaluated in a three-span girder. The damage is successfully identified in all different scenarios. Also, its application to Powder Mill Pond Bridge is numerically investigated which yielded promising results.