Indirect Approach to Identify Girder Bridge Element Stiffness Based on Blind Source Separation

Abstract

The indirect method of using a passing vehicle to identify modal properties of a girder bridge has become attractive recently. Compared to the direct method, which requires a lot of sensors installed directly on the bridge itself, the indirect method only requires a single sensor installed on the vehicle to indirectly measure the response of the bridge. However, it is difficult to eliminate the adverse effect of road surface roughness. An indirect approach based on blind source separation is proposed for the first time in this study to identify the bridge element stiffness where two movable vehicles are used. Two identical vehicles stay at rest at the designated measurement points and their vertical accelerations are collected. After one measurement, the two vehicles move to other designated measurement points and the accelerations are collected again. The same procedure is repeated until the two vehicles have moved over all the designated measurement points. Then the blind source separation technique is employed to extract the fundamental mode shape of the bridge and the improved direct stiffness method is adopted to estimate the bridge element stiffness based on the collected data, which are used to monitor the health of the bridge structure and to maintain structure safety and natural symmetry. The proposed method only requires the output response of the vehicle due to the involvement of the blind separation technique. In addition, the proposed method can overcome the adverse effect of road surface roughness because the vehicles only move between two measurements and they stay at rest during one measurement. Numerical simulation was conducted to validate the proposed method, and the effect of various factors such as bridge damping ratio and measurement noise was investigated. Field measurement on Min-Xie bridge in Chongqing city was also carried out to further investigate the feasibility of the proposed method and showed that it can perform well in extracting the fundamental mode shape and evaluating bridge element stiffness.

Discussion on a Vehicle–Bridge Interaction System Identification in a Field Test

“…If these direct bridge monitoring techniques can be utilized, more efficient and reliable parameter estimation may be possible. Additionally, Yang et al [ 36 ] proposed a method to evaluate the stiffness of a bridge estimated from vehicle vibration. Based on the estimated bridge stiffness, the deflection of the bridge, when given a specific load, is calculated by the finite element method.…”

“…By taking the difference in the acceleration of the front and rear wheels in the frequency domain, it is possible to suppress the influence of road unevenness and identify the bridge’s natural frequency. In addition, estimations of mode shapes [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ] and damping [ 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 ] were also performed. Yang et al [ 35 ] proposed a method for estimating the bridge mode shapes from the acceleration responses on towed vehicles.…”

“…Next, the bridge stiffness is calculated from the estimated bridge mode shape. From the estimated bridge stiffness, the bridge deflection was calculated using the finite element method and compared to the measured values [ 36 ]. Other methods have been proposed that use machine learning to identify bridge damage from multiple data [ 45 ] and estimate bridge vibration from vehicles traveling at high speeds [ 46 ].…”

Symmetry in Safety and Disaster Prevention Engineering

“…Yang et al [2] established a new indirect approach to identify the stiffness of symmetric girder bridge elements to detect the deflection of the bridge. Two identical vehicles stayed at rest at the designated measurement points and their vertical accelerations were collected.…”

Precise Positioning Method of Moving Laser Point Cloud in Shield Tunnel Based on Bolt Hole Extraction

“…Although the accuracy of these methods is high, the efficiency is low [6][7][8][9][10][11]. In addition, the traditional methods for modern structure detection require complex instruments and a large number of sensors installed on the main body of the structure, which brings great challenges to deformation and damage detection [12][13][14]. Mobile laser scanning technology can obtain high-precision and high-density tunnel laser point clouds in a short time and has gradually become an important means of deformation detection for rail transit tunnels.…”