Management Strategies for Special Permit Vehicles for Bridge Loading

An examination of weigh-in-motion data collected recently at sites in five European countries has shown that vehicles with weights well in excess of the normal legal limits are found on a daily basis. These vehicles would be expected to have permits issued by the responsible authorities. It can be seen from the measurements that most of them are travelling at normal speeds. Photographic evidence indicates that, while many are accompanied by an escort vehicle, normal traffic is flowing alongside in other lanes. As European freight volume grows, the frequency of these special vehicles can be expected to increase. Hence, the probability of them meeting a heavy truck on a bridge also increases. Gross vehicle weights in excess of 100 t have been observed at all sites, and are a daily occurrence in the Netherlands. Most of these extremely heavy vehicles are either mobile cranes or low loaders carrying construction equipment. Both types have multiple axles at very close spacing, and the gross weight and axle layout have implications for bridge loading. This paper presents findings based on a simulation model which incorporates the load effects for all observed truck types on short to medium span bridges. It is evident that special vehicles govern the lifetime maximum bridge loading, and the occurrence of extremely heavy trucks is sufficiently frequent that meeting events can be expected during the design lives of the bridges. The effects of different management strategies for special permit vehicles are modelled and the results are presented

Site Specific Modelling of Traffic Loading on Highway Bridges

Accurate traffic loading models based on measured weigh-in-motion (WIM) data are essential for the accurate assessment of existing bridges. Much work has been published on the Monte Carlo simulation of single lanes of heavy vehicle traffic, and this can easily be extended to model the loading on bridges with two streams of traffic in opposing directions. However, a typical highway bridge will have multiple lanes in the same direction, and various types of correlation are evident in measured traffic, such as groups of very heavy vehicles travelling together and heavy vehicles being overtaken by lighter ones. These traffic patterns affect the probability and magnitude of “multiple presence” loading events on bridges, and are significant for maximum lifetime. This paper analyses traffic patterns using multi-lane WIM data collected at four European sites. It describes an approach to the Monte Carlo simulation of this traffic which seeks to replicate the observed patterns of vehicle weights, vehicle gaps and speeds by applying variable bandwidth kernel density estimators to empirical traffic patterns. This allows the observed correlation structure to be accurately simulated but also allows for unobserved patterns to be simulated. The process has been optimised so as to make it possible to simulate traffic loading on bridges over periods of 1,000 years or more, and this removes much of the variability associated with estimating characteristic maximum load effects. The results show that the patterns of correlation in the observed traffic have a small but significant effect on bridge loading

Monitoring the Condition of a Bridge using a Traffic Speed Deflectometer Vehicle Travelling at Highway Speed

The Traffic Speed Deflectometer (TSD) is a vehicle incorporating a set of laser Doppler vibrometers on a straight beam to measure the relative velocity between the beam and the pavement surface. This paper describes a numerical study to see if a TSD could be used to detect damage in a bridge. From this measured velocity it is possible to obtain the curvature of the bridge, from whose analysis, it will be demonstrate that information on damage can be extracted. In this paper a Finite Element model is used to simulate the vehicle crossing a single span bridge, for which deflections and curvatures are calculated. From these numerical simulations, it is possible to predict the change in the curvature signal when the bridge is damaged. The method looks promising and it suggests that this drive-by approach is more sensitive to damage than sensors installed on the bridge itself

Modeling Extreme Traffic Loading On Bridges Using Kernel Density Estimators

Kernel density estimators are a non-parametric method of estimating the probability density function of sample data. In this paper, the method is applied to find characteristic maximum daily truck weights on highway bridges. The results are then compared with the conventional approac

The influence of correlation on the extreme traffic loading of bridges

Accurate traffic loading models based on measured data are essential for the accurate assessment of existing bridges. There are well-established methods for the Monte Carlo simulation of single lanes of traffic, and this can easily be extended to model the loading on bridges with two independent streams of traffic in opposing directions. However, a typical highway bridge will have multiple lanes in the same direction, and various types of correlation are evident in measured traffic. This paper analyses traffic patterns using multi-lane WIM data collected at two European sites. It describes an approach to the Monte Carlo simulation of this traffic which applies variable bandwidth kernel density estimators to empirical traffic patterns of vehicle weights, gaps and speeds. This method provides a good match with measured data for multi-truck bridge loading events, and it is shown that correlation has a small but significant effect on lifetime maximum load effects

Probabilistic Bridge Weigh-in-Motion

Conventional bridge weigh-in-motion (BWIM) uses a bridge influence line to find the axle weights of passing vehicles that minimize the sum of squares of differences between theoretical and measured responses. An alternative approach, probabilistic bridge weigh-in-motion (pBWIM), is proposed here. The pBWIM approach uses a probabilistic influence line and seeks to find the most probable axle weights, given the measurements. The inferred axle weights are those with the greatest probability amongst all possible combinations of values. The measurement sensors used in pBWIM are similar to BWIM, containing free-of-axle detector (FAD) sensors to calculate axle spacings and vehicle speed and weighing sensors to record deformations of the bridge. The pBWIM concept is tested here using a numerical model and a bridge in Slovenia. In a simulation, two hundred randomly generated 2-axle trucks pass over a 6 m long simply supported beam. The bending moment at mid-span is used to find the axle weights. In the field tests, seventy-seven pre-weighed trucks traveled over an integral slab bridge and the strain response in the soffit at mid-span was recorded. Results show that pBWIM has good potential to improve the accuracy of BWIM

Estimating Characteristic Bridge Loads On A Non-Primary Road Network

When collecting truck loading data on a primary road network a common approach is to install a large network of permanent pavement-based Weigh-In-Motion systems. An alternative to this approach would be to use one or more portable Bridge Weigh-In-Motion systems which could be moved between bridges at regular intervals to determine the traffic loading throughout the network. A data collection strategy is needed to put such a system to best use. This paper details the data collection strategies which were examined for the National Roads Authority in Ireland. The use of urban economic concepts including Central Place Theory are discussed as methods for analysing which roads are expected to experience the greatest truck loading

Vulnerability assessment of existing bridges to scour: an indirect monitoring approach

Detecting scour in railway bridges is possible by locating accelerometers and GPS on carriages of passing trains and processing the resulting signals. This research aims to detect scour based on these drive-by measurements, obtained from an instrumented passing vehicle. Signals from multiple train passages will be collected before and after cour repair to determine the change in bridge behavior. Measurements from a train in the UK passing over the Carlisle Bridge will be provided through In2Track3, an ongoing Horizon 2020 project. In the first stage of the numerical approach, off-bridge conditions are considered. The carriage vibrational responses to track with different ground conditions – represented by altering the stiffnesses in a Winkler spring model – are calculated. In second stage, the bridge ‘apparent profile’(AP), which is made up of the true profile on the bridge plus components of bridge/track deflection, will be computed. The Moving Reference Influence Line, i.e., deflection per unit load at a moving reference point, is found from the measured deflections. Bridge support stiffnesses will be modified to represent the loss of stiffness due to scour. Then, signals from the instrumented in-service train carriage i.e., measured AP, will be processed. Finally, an optimization algorithm will find foundation stiffnesses by minimizing the sum of squared differences between the calculated AP and the corresponding measured AP. The presence of scour will be determined by the difference between the stiffness values in the scoured and repaired cases. The results will help to optimize retrofits or develop mitigation measures to scour.This work was partly financed by FCT / MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under reference UIDB / 04029/2020. This work has also been partly financed within the European Horizon 2020 Joint Technology Initiative Shift2Rail through contract no. 101012456 (IN2TRACK3)

Development and Testing of a Railway Bridge Weigh-in-Motion System

This study describes the development and testing of a railway bridge weigh-in-motion (RB-WIM) system. The traditional bridge WIM (B-WIM) system developed for road bridges was extended here to calculate the weights of railway carriages. The system was tested using the measured response from a test bridge in Poland, and the accuracy of the system was assessed using statically-weighed trains. To accommodate variable velocity of the trains, the standard B-WIM algorithm, which assumes a constant velocity during the passage of a vehicle, was adjusted and the algorithm revised accordingly. The results showed that the vast majority of the calculated carriage weights fell within ±5% of their true, statically-weighed values. The sensitivity of the method to the calibration methods was then assessed using regression models, trained by different combinations of calibration trains

Scour detection with monitoring methods and machine learning algorithms - a critical review

Foundation scour is a widespread reason for the collapse of bridges worldwide. However, assessing bridges is a complex task, which requires a comprehensive understanding of the phenomenon. This literature review first presents recent scour detection techniques and approaches. Direct and indirect monitoring and machine learning algorithm-based studies are investigated in detail in the following sections. The approaches, models, characteristics of data, and other input properties are outlined. The outcomes are given with their advantages and limitations. Finally, assessments are provided at the synthesis of the research.This research was funded by FCT (Portuguese national funding agency for science, research, and technology)/MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under reference UIDB/04029/2020 and trough the doctoral Grant 2021.06162.BD. This work has also been partly financed within the European Horizon 2020 Joint Technology Initiative Shift2Rail through contract no. 101012456 (IN2TRACK3)