scholarly journals Comprehensive Study of Moving Load Identification on Bridge Structures Using the Explicit Form of Newmark-β Method: Numerical and Experimental Studies

2021 ◽  
Vol 13 (12) ◽  
pp. 2291
Author(s):  
Solmaz Pourzeynali ◽  
Xinqun Zhu ◽  
Ali Ghari Zadeh ◽  
Maria Rashidi ◽  
Bijan Samali

Bridge infrastructures are continuously subject to degradation due to aging and excess loading, placing users at risk. It has now become a major concern worldwide, where the majority of bridge infrastructures are approaching their design life. This compels the engineering community to develop robust methods for continuous monitoring of bridge infrastructures including the loads passing over them. Here, a moving load identification method based on the explicit form of Newmark-β method and Generalized Tikhonov Regularization is proposed. Most of the existing studies are based on the state space method, suffering from the errors of a large discretization and a low sampling frequency. The accuracy of the proposed method is investigated numerically and experimentally. The numerical study includes a single simply supported bridge and a three-span continuous bridge, and the experimental study includes a single-span simply supported bridge installed by sensors. The effects of factors such as the number of sensors, sensor locations, road roughness, measurement noise, sampling frequency and vehicle speed are investigated. Results indicate that the method is not sensitive to sensor placement and sampling frequencies. Furthermore, it is able to identify moving loads without disruptions when passing through supports of a continuous bridge, where most the existing methods fail.

2016 ◽  
Vol 16 (04) ◽  
pp. 1640021 ◽  
Author(s):  
Cai Qian Yang ◽  
Dan Yang ◽  
Yi He ◽  
Zhi Shen Wu ◽  
Ye Fei Xia ◽  
...  

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.


2021 ◽  
Vol 14 (1) ◽  
pp. 119
Author(s):  
Solmaz Pourzeynali ◽  
Xinqun Zhu ◽  
Ali Ghari Zadeh ◽  
Maria Rashidi ◽  
Bijan Samali

Bridge infrastructures are always subjected to degradation because of aging, their environment, and excess loading. Now it has become a worldwide concern that a large proportion of bridge infrastructures require significant maintenance. This compels the engineering community to develop a robust method for condition assessment of the bridge structures. Here, the simultaneous identification of moving loads and structural damage based on the explicit form of the Newmark-β method is proposed. Although there is an extensive attempt to identify moving loads with known structural parameters, or vice versa, their simultaneous identification considering the road roughness has not been studied enough. Furthermore, most of the existing time domain methods are developed for structures under non-moving loads and are commonly formulated by state-space method, thus suffering from the errors of discretization and sampling ratio. This research is believed to be among the few studies on condition assessment of bridge structures under moving vehicles considering factors such as sensor placement, sampling frequency, damage type, measurement noise, vehicle speed, and road surface roughness with numerical and experimental verifications. Results indicate that the method is able to detect damage with at least three sensors, and is not sensitive to sensors location, vehicle speed and road roughness level. Current limitations of the study as well as prospective research developments are discussed in the conclusion.


2021 ◽  
Vol 11 (2) ◽  
pp. 853 ◽  
Author(s):  
Jing Yang ◽  
Peng Hou ◽  
Caiqian Yang ◽  
Yang Zhang

In order to improve the accuracy of load identification and study the influence of transverse distribution, a novel method was proposed for the moving load identification based on strain influence line and the load transverse distribution under consideration. The load identification theory based on strain influence line was derived, and the strain integral coefficient was proposed for the identification. A series of numerical simulations and experiments were carried out to verify the method. The numerical results showed that the method without considering the load transverse distribution was not suitable for solving the space problem, and the method with the load transverse distribution under consideration has a high identification accuracy and excellent anti-noise performance. The experimental results showed that the speed identification error was smaller than ±5%, and the vehicle speed had no obvious influence on the identification results of the vehicle weight. Moreover, the average identification error of the vehicle weight was smaller than ±10%, and the error of more than 90% of samples was smaller than ±5%.


2021 ◽  
Vol 16 (3) ◽  
pp. 131-158
Author(s):  
Qingqing Zhang ◽  
Wenju Zhao ◽  
Jian Zhang

Moving load identification has been researched with regard to the analysis of structural responses, taking into consideration that the structural responses would be affected by the axle parameters, which in its turn would complicate obtaining the values of moving vehicle loads. In this research, a method that identifies the loads of moving vehicles using the modified maximum strain value considering the long-gauge fiber optic strain responses is proposed. The method is based on the assumption that the modified maximum strain value caused only by the axle loads may be easily used to identify the load of moving vehicles by eliminating the influence of these axle parameters from the peak value, which is not limited to a specific type of bridges and can be applied in conditions, where there are multiple moving vehicles on the bridge. Numerical simulations demonstrate that the gross vehicle weights (GVWs) and axle weights are estimated with high accuracy under complex vehicle loads. The effectiveness of the proposed method was verified through field testing of a continuous girder bridge. The identified axle weights and gross vehicle weights are comparable with the static measurements obtained by the static weighing.


2019 ◽  
Vol 3 (3/4) ◽  
pp. 257-288 ◽  
Author(s):  
Yun Zhou ◽  
Sai Zhou ◽  
Lu Deng ◽  
Songbai Chen ◽  
Weijian Yi

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