Bridge weigh-in-motion (BWIM) systems provide an effective approach to identifying the axle and gross vehicle weights of vehicles as they travel over an instrumented bridge. For the majority of BWIM systems, the vehicle configuration (including axle count and axle spacing) and vehicle speed are prerequisites for identifying the axle and gross weights of vehicles. Existing nothing-on-road (NOR) BWIM systems acquire such data through dedicated sensors, namely, free-of-axle-detector (FAD) sensors, in addition to weighing sensors. These FAD sensors are usually installed on the underside of the bridge deck or girders. This study presents a novel method for identifying the axle spacing and weights of vehicles. It only requires the flexural strain signal recorded from the weighing sensors, leading to both a reduction in the installation cost and broader applications of BWIM systems. The effectiveness and accuracy of the proposed method are demonstrated through numerical simulations. Laboratory experiments based on a scaled vehicle–bridge interaction (VBI) model were also conducted for verification. The results show that the proposed method has good accuracy for axle spacing and axle weight identification.

One of the major drawbacks with most bridge weigh-in-motion (BWIM) methods that adopt the Moses algorithm is the need for acquisition of vehicle speed and axle spacing by using additional devices, such as free-of-axle detectors. This study presents a novel virtual simply supported beam (VSSB) method, which uses weighing sensors to directly identify the speed and the axle spacing of passing vehicles on bridges, making it very desirable for commercial BWIM systems. Numerical simulations and model tests are carried out to study the effectiveness and accuracy of the proposed method. The results show that the proposed method can successfully identify the vehicle speed and axle spacing with good accuracy. The proposed method also proves reliable under noisy conditions.

Traffic monitoring, particularly on the gross vehicle weight (GVW) and axle weights (AWs) of heavy trucks, provides valuable information for the design and performance evaluation of bridges. Bridge weigh-in-motion (BWIM) is a recently developed technology that uses the bridge as a scale to estimate vehicle weights. For BWIM systems, the acquisition of vehicle speed and axle spacing (AS) is a prerequisite for accurate identification of the AWs and GVW. Traditionally, axle detectors are placed on the road surface to detect vehicle axles. However, axle detectors are not durable due to their exposure to the traffic. Also, their installation and maintenance also cause disruption to the traffic. For these reasons, the concept of the nothing-on-road (NOR) BWIM is proposed. Most existing NOR BWIM systems require additional sensors for axle detection, which limits their applicability. In this paper, a novel equivalent shear force method (ESF) is proposed to identify vehicle speed and AS by using the flexural strain signal acquired from the weighting sensors. Compared with the existing NOR BWIM systems, the proposed method does not require additional sensors for axle detection, making it desirable for commercial BWIM systems. The effectiveness and accuracy of the proposed method are demonstrated through numerical simulations and validated through an experiment using scaled model tests. Parametric studies are also conducted to investigate the effects of various factors on the accuracy of the proposed method.

A bridge influence line (BIL) and a bridge influence surface (BIS) reflect the relationship between the bridge responses and the loads on the bridge and have been commonly used in techniques such as bridge damage detection, bridge safety evaluation, bridge model correction, bridge weigh-in-motion, and so on. Conventionally, a BIL can be extracted from the bridge response under a moving vehicle with known axle loads, while a BIS can then be obtained by lateral interpolation from BILs obtained for key transverse positions. However, in the traditional BIL-extracting methods, the transverse distance between the coaxial wheels was not considered; that is, the coaxial left and right wheel loads are simply treated as one concentrated load (usually referred to as the axle load). Therefore, the obtained BIL is the bridge response under two loads rather than one. Hence, errors may be introduced to the obtained BILs and propagated to the interpolated BIS. Moreover, the potentially significant effect of the unbalance of coaxial wheel loads on BIL identification is ignored. In this research, a new method for determining a genuine BIL and BIS is proposed. The wheel load rather than the axle load was taken into calculation in this method, where the effect of the transverse distance between the coaxial wheels and the unbalance of the coaxial wheel loads was naturally taken into consideration. Laboratory experiments and numerical simulations were performed to verify the effectiveness of the proposed method. Furthermore, a comprehensive parametric analysis was conducted to investigate the effects of some important parameters such as vehicle velocity, lateral deviation of the center of gravity of vehicles, axle count, and road surface condition on the identification performance. The results show that the BIL and BIS can be identified with satisfactory accuracy. Both the bridge influence line (BIL) and the bridge influence surface (BIS) have been commonly used in techniques such as bridge damage detection, bridge safety evaluation, bridge model correction, and bridge weigh-in-motion because they reflect the relationship between the bridge responses and the loads on the bridge. However, in the traditional vehicle-based BIL/BIS calibration methods, the track width of the calibration vehicle was ignored, leading to the accuracies of the calibrated influence lines/surfaces being affected by the track width and the transverse unbalance of the calibration vehicle. To address this problem, a new method, which can eliminate the negative effects of the track width and transverse unbalance, is proposed in this study. It provides a more accurate solution for bridge influence lines/surfaces than the traditional methods. The obtained bridge influence line/surface offers a more accurate input for related technologies and applications such as the bridge weigh-in-motion techniques and therefore improves the accuracy of these related technologies.

Traffic loading monitoring plays an important role in bridge structural health monitoring, which is helpful in overloading detection, transportation management, and safety evaluation of transportation infrastructures. Bridge weigh-in-motion (BWIM) is a method that treats traffic loading monitoring as an inverse problem, which identifies the traffic loads of the target bridge by analyzing its dynamic strain responses. To achieve accurate prediction of vehicle loads, the configuration of axles and vehicle velocity must be obtained in advance, which is conventionally acquired via additional axle-detecting sensors. However, problems arise from additional sensors such as fragile stability or expensive maintenance costs, which might plague the implementation of BWIM systems in practice. Although data-driven methods such as neural networks can estimate traffic loadings using only strain sensors, the weight data of vehicles crossing the bridge is difficult to obtain. In order to overcome these limitations, a modified encoder-decoder architecture grafted with signal-reconstruction layer is proposed in this paper to identify the properties of moving vehicles (i.e., velocity, wheelbase, and axle weight) using merely the bridge dynamic response. Encoder-decoder is an unsupervised method extracting higher features from original data. The numerical bridge model based on vehicle-bridge coupling vibration theory is established to illustrate the applicability of this new encoder-decoder method. The identification results demonstrate that the proposed approach can predict traffic loadings without using additional sensors and without requiring vehicle weight labels. Parametric studies also show that this new approach achieves better stability and reliability in identifying the properties of moving vehicles, even under the circumstances of large data pollution.