Axle Configuration and Weight Sensing for Moving Vehicles on Bridges Based on the Clustering and Gradient Method

Wei He,Xuan Kong,Xiaodong Liang,Hong Xie,Lu Deng

doi:10.3390/rs13173477

Abstract

Traffic information, including vehicle weight and axle spacing, is vital for bridge safety. The bridge weigh-in-motion (BWIM) system remotely estimates the axle weights of moving vehicles using the response measured from instrumented bridges. It has been proved more accurate and durable than the traditional pavement-based method. However, the main drawback of conventional BWIM algorithms is that they can only identify the axle weight and the information of axle configuration (the number of axles and axle spacing) is required to be determined using an extra device in advance of the weight identification procedure. Namely, dedicated sensors (pressure-sensitive sensors placed on the deck surface or under the soffit of a bridge) in addition to weighing sensors must be adopted for identifying the axle configuration, which significantly decreases the utility, feasibility, and economic efficiency of BWIM technology. In this study, a new iterative procedure simultaneously identifying axle spacing as well as axle weights and gross weights of vehicles is proposed. The novel method is based on k-means clustering and the gradient descent method. In this method, both the axle weight and the axle location are obtained by using the same global response of bridges; thus the axle detectors are no longer required, which makes it economical and easier to be implemented. Furthermore, the proposed optimization method has good computational efficiency and thus is practical for real-time application. Comprehensive numerical simulations and laboratory experiments based on scaled vehicle and bridge models were conducted to verify the proposed method. The identification results show that the proposed method has good accuracy and high computational efficiency in axle spacing and axle weight identification.

Highlights

Operational traffic load data are vital for the assessment and maintenance of transportation infrastructure [1,2], such as the axle spacing, axle weight, and gross weight of moving vehicles
System, conceptually proposed first by Moses in the 1970s [5], uses an instrumented bridge as a scale to weigh vehicles passing the bridge at a normal highway speed, which provides an effective procedure for reliable measurement of the axle spacing, axle weights, and gross vehicle weights of trucks without interrupting the regular traffic
Axle weight, and gross weight of passing vehicles is vital for traffic monitoring on highway bridges

Summary

Introduction

Operational traffic load data are vital for the assessment and maintenance of transportation infrastructure [1,2], such as the axle spacing, axle weight, and gross weight of moving vehicles. System, conceptually proposed first by Moses in the 1970s [5], uses an instrumented bridge as a scale to weigh vehicles passing the bridge at a normal highway speed, which provides an effective procedure for reliable measurement of the axle spacing, axle weights, and gross vehicle weights of trucks without interrupting the regular traffic. Moses’ original BWIM algorithm or its derivatives are still the theoretical basis of state-of-the-art commercial BWIM systems [4], where the axle weights of vehicles can be determined by minimizing the square of the Euclidean distance of measured bridge responses and those by using the influence line method. Two comprehensive state-of-the-art reviews on existing BWIM algorithms and their recent applications were composed by Yu et al [8] and Lydon et al [9], respectively

Methods

Results

Discussion

Conclusion