A robust bridge weigh‐in‐motion algorithm based on regularized total least squares with axle constraints

Abstract

Summary The identification of traffic loads, including the axle weight (AW) and the gross vehicle weight (GVW) of vehicles, plays an important role in bridge design refinement, safety evaluation, and maintenance strategies. Bridge weigh in motion (BWIM) is a promising technique to weigh vehicles passing through bridges. Though the state‐of‐the‐art BWIM can accurately identify the GVW, unacceptable weighing errors are reported when identifying the AW of vehicles, particularly for those with closely spaced axles. To address the axle weighing problem, this paper aims to improve the performance of BWIM in weighing individual axle loads apart from the gross vehicle weight. The work first theoretically analyzes the possible sources of errors of existing BWIM algorithms, which are observational errors residing in the BWIM equation, no constraint imposed on individual axle loads, and ill‐conditioned nature. Accordingly, three measures are taken to establish a novel robust BWIM algorithm, which is based on regularized total least squares, as well as imposing constraints on the relationship between axles. To validate the proposed algorithm, a series of weighing experiments are carried out on a high‐fidelity vehicle‐bridge scale model. The corresponding results indicate that the proposed BWIM algorithm significantly outperforms other existing BWIM algorithms in terms of the accuracy and robustness of identifying individual axle weight, while retaining satisfactory identification of the gross vehicle weight as well.

Bridge influence surface identification using a deep multilayer perceptron and computer vision techniques

“…As illustrated in Figure 4, a typical MLP is composed of input, hidden, and output layers: (1) The input layer admits as input the coordinate data x, y ð Þ obtained from the object tracking results via the YOLOv4 model; (2) The hidden layer transmits the features from the input layer to the output layer. A deeper architecture with more hidden layers and neurons can result in improved approximations for more complicated functions, 37 albeit running the risk of over-fitting.…”

“…The identification of moving traffic loads on bridges is critical in bridge design refinement, safety evaluation, and decision-making for maintenance planning. In recent years, based on the theory of influence lines (ILs) 1 and influence surfaces (ISs), 2 bridge weigh-in-motion (BWIM) techniques have emerged as promising tools to identify axle and gross weights of vehicles from measured bridge deformations. 2,3 In addition to being used as the metrics of BWIM, the bridge IL and IS serve as important tools for structural condition assessment and safety evaluation, as they reflect the intrinsic properties of a bridge structure.…”

“…In recent years, based on the theory of influence lines (ILs) 1 and influence surfaces (ISs), 2 bridge weigh-in-motion (BWIM) techniques have emerged as promising tools to identify axle and gross weights of vehicles from measured bridge deformations. 2,3 In addition to being used as the metrics of BWIM, the bridge IL and IS serve as important tools for structural condition assessment and safety evaluation, as they reflect the intrinsic properties of a bridge structure. 4 Specifically, the IL and IS of a bridge essentially are directly linked to its stiffness matrix, implying that variations of the stiffness can be picked up by analyzing the corresponding variations of IL and IS.…”

“…The identification of moving traffic loads on bridges is critical in bridge design refinement, safety evaluation, and decision-making for maintenance planning. In recent years, based on the theory of influence lines (ILs) 1 and influence surfaces (ISs), 2 bridge weigh-in-motion (BWIM) techniques have emerged as promising tools to identify axle and gross weights of vehicles from measured bridge deformations. 2,3…”

“…In recent years, based on the theory of influence lines (ILs) 1 and influence surfaces (ISs), 2 bridge weigh-in-motion (BWIM) techniques have emerged as promising tools to identify axle and gross weights of vehicles from measured bridge deformations. 2,3…”