Weigh-in-motion Research Articles

Probabilistic Modeling of Congested Traffic Scenarios on Long-Span Bridges

This paper aims to extend a previously developed probabilistic model for simulating extreme response scenarios to include congested traffic flow on long-span bridges, addressing the challenge of accurately modeling traffic loads under changing conditions. While the model was initially designed for free-flow traffic, this study demonstrates how it can be adapted for congested conditions, with the objective of improving the accuracy of traffic load models. To overcome the limitation of traditional Weigh-in-Motion (WIM) systems in capturing congested traffic, congested flow characteristics were inferred from available free-flow data. The cellular automata (CA) method was applied to generate realistic congested traffic scenarios, which were used as input for the probabilistic model. Key simulation parameters, such as cell length and vehicle weight distribution, were adjusted to reflect congested conditions. The results validate the model’s flexibility, showing how, with the adaptation of some parameters, it can simulate both free-flow and congested traffic patterns effectively. This research provides a basis for improving traffic load models used in the design and assessment of long-span bridges, addressing the current limitations in existing codes and standards.

A bridge dynamic response analysis and load recognition method using traffic imaging

As the primary variable load of bridges, vehicle load is an important parameter for bridge health monitoring. However, traditional Weigh-in-Motion (WIM) systems and the commonly used method of placing sensors on the bridge are challenging to apply in load monitoring for many small and medium-sized bridges. Therefore, this paper proposes a bridge vehicle load identification method based on traffic surveillance video data. Leveraging the surveillance video data on the bridge, without introducing additional hardware devices, the displacement of target points is detected through sub-pixel level image detection algorithms, enabling non-contact measurement of bridge structural response through imaging. A spatiotemporal relationship model of structural displacement, vehicle load, and load distribution is established to solve for vehicle load. Finally, model bridge tests under various loading conditions and engineering practice experiments are conducted to validate the feasibility of the method. The results of the model bridge tests show that the structural displacement measured using traffic video measurement has a deviation of less than 10% compared to the measurements obtained using contact displacement sensors (LVDT), and it can accurately reflect the displacement characteristics of the structure. The results of the field tests demonstrate that the average estimation deviation for heavy vehicle loads ranging from 12 to 18 tons is approximately 18%, meeting the engineering requirements. The proposed method can provide load statistical information for the extensive health monitoring of small and medium-sized bridges and offer a new technical pathway for obtaining bridge load information.

Estimating the frequency of traffic overloading on road bridges

Load limits, which appear to be routinely exceeded by trucks, occasionally result in road bridge failures. Therefore, predicting failures is crucial for safeguarding road safety. Past studies have largely focused on forecasting bridge failure event probability using the reliability analysis method, whilst occasionally accounting for vehicular overloading effects. Only recently, a study has investigated design traffic overloading event frequency using generalised linear regression models (GLRMs), including a power component and negative binomial regressions (NBRs). However, as far as the authors know, artificial neural network models (ANNMs) have never been applied to this field. This paper is an attempt to fill in these gaps. First a frequency-based metric of traffic overloading was adopted as a driver of failure probability. Second, two alternative ‘frequency’ models were specified, calibrated, and validated. The former was based on a GLRM, the latter on ANNMs. Then, these models were compared using regression plots (RPs), measures of errors (MoEs) and the ratio between the number of observed vs predicted design load overcoming events to evaluate their performance. The models analysed more than 2 million weigh-in-motion (WIM) data records from a pilot station on a bridge on a heavily used ring road in Brescia (Italy). Results showed that ANNMs outperformed GLRMs. ANNMs have a higher correlation coefficient (between predicted and target frequencies), lower MoEs, and a closer-to-unity ratio (between predicted and target frequencies). These findings may increase prediction accuracy of design traffic overloading events and give road authorities more effective traffic management to protect bridges from load hazards.

Operational Dynamic Load Testing for Repair Recommendations Case Study of Access Road to Vehicle Weight in Motion

The indication of inaccuracies in vehicle weight measurement using Weight in Motion (WIM) technology on mining roads has been identified due to the non-fulfillment of the design criteria required by the WIM manufacturer. This study aims to identify the gap between the actual pavement conditions and the design criteria and to design improvements to eliminate this gap. The method employed involves dynamic load testing, followed by processing the test data with operational modal analysis (OMA) to obtain dynamic parameters that will serve as a reference for calculating the pavement capacity. From OMA, a natural structural frequency of 26.342 Hz was obtained, while the theoretical calculation based on the design criteria was 29.481 Hz. This frequency decrease indicates structural damage of 10.65% and a capacity reduction of 26.8% from the design criteria. This finding is reinforced by a critical damping ratio of 5.25%, which is higher than the damping for intact concrete, ranging between 2%-5%. This indicates energy dissipation through concrete cracks. It is recommended to inject the cracks in the existing pavement with a cement base and perform a 130 mm overlay with mortar grout (fc’= 50 MPa) with one layer of M12 wire mesh. The connection with the old pavement will use concrete bonding and D13-600/600 chemical anchors. With this improvement, the structural capacity will become 104% of the design criteria.

Development of a Practical Procedure for Data-Driven Weigh-in-Motion Equipment Calibration Scheduling to Assure Data Accuracy and Consistency over Time

Weigh-in-motion (WIM) technology is a traffic monitoring technology that highway agencies use to obtain information about the weight, axle loading, and configuration of heavy vehicles moving at operational speed. To ensure high-quality WIM data, the Federal Highway Administration (FHWA) recommends regular calibration of WIM equipment. This study addresses the need to optimize the allocation of the limited resources that agencies have for WIM equipment calibration by developing a procedure for data-driven calibration scheduling. This was accomplished through an analysis of WIM measurement errors from test truck data collected during field performance validation and calibration events and an analysis of monthly changes in truck weight and axle loading characteristics, based on WIM data collected between calibration events. The analysis results were used to draw conclusions on the functional performance of different WIM sites. The study also demonstrates how the newly developed National Cooperative Highway Research Program (NCHRP) WIM Data Quality Assurance Analysis Tool can be used to compute truck weight and axle loading parameters and visualize data analysis results using four case studies: two WIM sites with piezo quartz sensors in asphalt pavements and two WIM sites with bending plate sensors in concrete pavements. This paper provides a practical procedure and recommendations that highway agencies can use to develop data-driven WIM calibration schedules that will ensure consistent high-quality WIM data for sites managed by an agency with the aid of the NCHRP WIM Data Quality Assurance Analysis Tool.

Addressing wander effect in vehicle weight monitoring: An advanced hybrid weigh-in-motion system integrating computer vision and in-pavement sensors

The paper addressed the challenges of the two main weigh-in-motion (WIM) systems through a hybrid WIM system, which combines embedded in-pavement sensor systems and computer vision to enhance accuracy while minimizing human intervention. To establish a cost-effective system, this study employed a standard camera integrated with computer vision technique to improve the precision of identifying wheel location and automatically adjust distance calibration, particularly in response to variations in camera angle. In addition, the system achieved real-time vehicle weight detection efficiently by addressing the vehicle wander effect concerns of the in-pavement sensors through camera using computer vision. The system can achieve weight accuracy notably exceeding ASTM 1318E-09 standards, with over 90% accuracy for distances below 0.1 m, demonstrating its potential value in assisting traffic analysis and pavement maintenance by offering real-time, accurate WIM measurements at a low cost for instrumentation.

An accurate and low-cost vehicle-induced deflection prediction framework for long-span bridges using deep learning and monitoring data

Accurate vehicle-induced deflection prediction is critical to bridge structural management and maintenance. However, most existing studies use finite element models and weigh-in-motion (WIM) system to predict the deflection of bridges, the cost of building finite element models is expensive. An accurate and low-cost vehicle-induced deflection prediction framework is developed in this study. In this framework, the calculation method of the spatio-temporal distribution matrix of the vehicle loads is proposed. A fusion attention mechanism bidirectional long short-term memory (ATT-BiLSTM) model is used to predict vehicle-induced deflection. Taking a suspension bridge in China as an example, the accuracy of short- and long-term vehicle-induced deflection prediction is tested, and different deep learning models are used for comparison. The results show that the proposed framework can predict short-term vehicle-induced deflection with a mean absolute error (MAE) of less than 2 mm, which can also accurately predict the extrema of vehicle-induced deflection within a certain time window. The proposed framework is independent of finite element models and lowers the cost of bridge deflection prediction.

Comparative study on fatigue evaluation of suspenders by introducing actual vehicle trajectory data

Suspenders play a crucial role in transmitting loads from the bridge deck to the main cable in a suspension bridge. They are susceptible to fatigue due to repeated dynamic loads, particularly traffic loads. Traffic Load Models (TLMs), typically created using Monte–Carlo simulation and Weigh-In-Motion (WIM) data, are employed to evaluate this fatigue. However, these models often overlook practical vehicle trajectories and spatio-temporal distribution, which compromises the precision of fatigue assessments. In this study, we introduce a novel 2D Intelligent Driver Model (2D-IDM) that incorporates actual vehicle trajectories, with a particular focus on transverse vehicle movement. This enhancement aims to improve the fidelity of existing TLMs. To provide a clear, qualitative, and quantitative understanding of the effects of fatigue evaluation with or without actual trajectory characteristics, we have structured this paper as a comparative study. We compare our proposed model, denoted as TLM S-3, with two observation-based models (O-1 and O-2) and two simulation-based models (S-1 and S-2). We conducted an experimental case study on a long-span suspension bridge, where the actual traffic load trajectory was obtained using a WIM-Vision integrated system. To calculate fatigue damage considering both longitudinal and transverse directions, we established a multi-scale Finite Element Model (FEM) using solid element types to simulate the bridge girder. This model can generate the stress influence surface of the bridge and has been verified in both static and dynamic aspects. Our detailed comparative analysis demonstrates the consistency of the proposed 2D-IDM with the actual measured traffic load trajectories. This indicates that our approach can enhance the fidelity and precision of fatigue evaluations for bridge suspenders.

A spatiotemporal distribution identification method of vehicle weights on bridges by integrating traffic video and toll station data

Real-time monitoring of the spatiotemporal distribution of vehicle weights on bridge decks is an important component of bridge structural health monitoring systems. However, it is still a challenge to identify the spatiotemporal distribution of vehicle weights on the whole bridge deck because the existing identification techniques are based on the theory of line of influence or need to install a weight-in-motion (WIM) system on the bridge. This paper proposes an information fusion-based identification method for the spatiotemporal distribution of vehicle weights without WIM installation, in which, (1) the traffic videos acquired by multiple cameras arranged along both sides of the bridge are used to detect the spatiotemporal distribution and license plate of the vehicles, and the weights obtained from the toll station are linked to the vehicles by matching the license plates. In addition, (2) a digital image correlation (DIC)-based vehicle tracking method is proposed to solve the problems of frame drop and missing detection and (3) a polynomial fitting-based coordinate transformation method is proposed to avoid the derivation of complicated coordinate conversion formula related to pinhole camera. The efficiency and accuracy of the proposed identification approach are verified by the field data collected from a cable-stayed bridge and nearby toll stations. The results indicate that our proposed method is a feasible and reliable solution for identifying spatiotemporal distribution of vehicle weights on bridges.

The Risk of Failure Assessment in Bina Marga Standard Designed Prestressed Concrete Girder Bridges under B-WIM Load Measurement

The use of precast prestressed concrete girder bridges in Indonesia has been increasing rapidly due to their high quality, reliability, and faster construction on site. The girder components are typically designed for a specific bridge span and can be prefabricated. The Directorate General of Highways of the Ministry of PUPR (Bina Marga) has released a standard design for prestressed concrete girder bridges with a typical span of up to 40 m. This design is based on the bridge loading standard SNI 1725 2016, which determines the live traffic load through consensus due to limited data on actual traffic load measurement results. However, the Ministry of PUPR has been implementing actual traffic load measurements using weigh-in-motion (WIM) technology to directly measure the load of passing vehicles. In this study, a risk assessment of the failure risk of a standard Bina Marga bridge with a 40-m span prestressed concrete girder type was conducted based on B-WIM load measurements. The results of this assessment indicate that the standard Bina Marga bridge has a failure risk of 1.48 x 10-4, which is smaller than the acceptable risk of failure according to the AASHTO LRFD Bridge Design Specification as referenced in SNI 1725 2016.

Weigh-in-Motion Site for Type Approval of Vehicle Mass Enforcement Systems in Poland.

The need to protect road infrastructure makes it necessary to direct the mass enforcement control of motor vehicles. Such control, in order to fulfil its role, must be continuous and universal. The only tool currently known to achieve these goals are weigh-in-motion (WIM) systems. The implementation of mass enforcement WIM systems is possible only if the requirements for their metrological properties are formulated, followed by the implementation of administrative procedures for the type approval of WIM systems, rules for their metrological examination, and administrative regulations for their practical use. The AGH University of Krakow, in cooperation with the Central Office of Measures (Polish National Metrological Institute), has been conducting research in this direction for many years, and, now, as part of a research project financed by the Ministry of Education and Science. In this paper, we describe a unique WIM system located in the south of Poland and the results of over two years of our research. These studies are intended to lead to the formulation of requirements for metrological legalisation procedures for this type of system. Our efforts are focused on implementing WIM systems in Poland for direct mass enforcement. The tests carried out confirmed that the constructed system is fully functional. Its equipment with quartz and bending plate load sensors allows for the comparison of both technologies and the measurement of many parameters of the weighed vehicle and environmental parameters affecting weighing accuracy. The tests confirmed the stability of its metrological parameters. The GVW maximal measurement error does not exceed 5%, and the single axle load maximal measurement error does not exceed 12%. The sensors of the environmental parameters allow for the search for correlations between weighing accuracy and the intensity of these parameters.

Assessing Vehicle Wandering Effects on the Accuracy of Weigh-in-Motion Measurement Based on In-Pavement Fiber Bragg Sensors through a Hybrid Sensor-Camera System.

Weigh-in-motion (WIM) systems are essential for efficient transportation and monitoring parameters such as vehicle number, speed, and weight to ensure regulatory compliance and enhance road safety. Recently, WIM measurements using the Glass Fiber Reinforced Polymer Fiber Bragg Grating (GFRP-FBG) sensors have shown robustness and effectiveness. However, the accuracy of weight evaluation using the WIM systems based on GFRP-FBG sensors can be significantly influenced by the vehicle-wandering effect, which introduces uncertainties in wheel position determination and weight calculations. This paper assessed the impact of vehicle wandering on the accuracy of a WIM measurement system based on GFRP-FBG sensors by utilizing a new hybrid sensor-camera system that integrates roadside cameras and in-pavement GFRP-FBG sensors. The detailed methodology and framework of the developed hybrid system are introduced, followed by field testing on Highway I-94 in the United States. The field testing results indicate that by using the hybrid system, the wheel load detection accuracy of the WIM system based on GFRP-FBG sensors can be controlled to be a Type I or Type III WIM according to the ASTM 1318E-09 standard, with an average accuracy ranging from 87.83% to 94.65%. At the same time, when the wander distance is less than or equal to 9 cm, the developed WIM system proves to be very cost-effective as it only comprises two GFRP-FBG sensors, one temperature FBG sensor, and one camera. These findings indicate the practical potential to enhance the accuracy of WIM systems based on GFRP-FBG sensors designed for highways for low-coast, reliable, and accurate measurements by addressing vehicle wandering effects.

Use of axle load spectra (ALS) for estimating calibration drift in weigh-in-motion (WIM) systems

The road agencies collect and submit weigh-in-motion (WIM) data to the Federal Highway Administration as part of their traffic monitoring program. Therefore, the WIM data should be precise and accurate. One way to evaluate WIM measurement errors is by using the test truck data collected immediately before and after equipment calibration. The limitation of this approach is that the data represent a snapshot in time and may not represent a long-term WIM site performance. This paper presents an approach for estimating WIM system accuracy based on axle load spectra attributes (normalized axle load spectra (NALS) shape factors). This alternative approach allows for characterizing temporal changes in WIM data consistency. The WIM error data collected before and after calibration were related to Class 9 NALS shape factors in the proposed methodology. This paper aims to determine WIM system errors based on axle loading without physically performing WIM equipment performance validation using test trucks. The presented methodology can be used to estimate systematic errors (drift) in the WIM system at any point in time after the equipment calibration. This approach can help highway agencies select optimum timings for routine maintenance and calibration of WIM equipment without compromising its accuracy. The results show that the WIM accuracy for the single axle (SA) and tandem axle (TA) can be estimated with SA and TA NALS shape factors with an acceptable degree of error for bending plate to quartz piezo sensors. Examples are included to demonstrate the application and significance of the developed models.

Evaluation of WIM data consistency based on temporal axle load spectra

It is crucial to evaluate the consistency in weigh-in-motion (WIM) loading over time and quantify the relative accuracy of axle loads based on axle load spectra (ALS) data for different sensor types. This paper presents the temporal evaluation of the ALS from 51 WIM sites and 128 records available in the long-term pavement performance data. Analysis of ALS data over time shows that for single ALS, there is a significant difference in peak loads between the bending plate, and quartz piezo sensor measurements. Also, 100% of the bending plate 86% of the quartz piezo, and 66% of the piezo cables sites exhibited consistent single axle peak loads after 1 year of calibration event. For tandem ALS, significant differences were observed between loaded peaks of bending plate sensor from both quartz piezo and piezo cable sensors. Also, 83% of the bending plate, 78% of the quartz piezo, and 50% of the piezo cable sites exhibited loaded peaks consistently 1 year after calibration. The results show that calibration frequencies longer than 1 year may be acceptable for the bending plate sensors. However, calibration frequencies of at least 1 year for quartz piezo and less than a year for piezo cable sensors are recommended. Pavement designers and analysts should be aware of the changes in WIM data and calibration frequency over time.

Mapping hazardous locations on a road network due to extreme gross vehicle weights

This study investigates the application of hazard maps in identifying critical locations associated with extreme vehicle load events. Examining extreme vehicle loads can offer valuable insights into the reliability of road infrastructure. Weigh-In-Motion (WIM) systems are commonly employed for this purpose; however, their limited availability often necessitates the use of less-sophisticated traffic counters (LSTCs). Unfortunately, LSTCs are unable to measure vehicular axle loads, posing a significant drawback. To overcome this limitation, we propose a methodology that utilizes data from LSTCs to estimate axle loads and map extreme gross vehicle weights. To demonstrate the feasibility of this approach, we present a case study concentrating on the major highway corridors in Mexico. While WIM stations are absent, data from a network of 1,777 counting stations and origin–destination surveys are available. By quantifying Gaussian copula-based Bayesian networks using the existing information, we generate synthetic site-specific axle loads. Subsequently, we calculate gross vehicle weights for selected return periods. These study findings facilitate the identification of hazardous locations. Additionally, we provide an interactive web map and a graphical user interface to generate synthetic axle loads. These tools, along with the proposed methodology, can serve as the foundation for maintenance strategies for existing roads and bridges.

Model Development and Simulation for Predicting Proportional Changes in Traffic Volume as a Consequence of Natural Weather Hazards

Abstract This study used a model to calculate the proportional drop for every vehicle class based on 266 climate patterns consisting of seven temperature groups and varied snowfalls. The winter traffic models use weigh-in-motion (WIM) traffic collected on the commuter roadway for 5 years. The marginal impact and combined effect of meteorological conditions on the proportional decrease in winter traffic volume are evaluated. The predicted percentage decrease in traffic for all three vehicle classes increases as temperature decreases and snowfall increases. Mathematical functions are fitted for the decreased patterns for the considered vehicle type. Roadway authorities may utilize traffic percentage decrease to identify weather-related traffic changes when planning winter highway operation and maintenance.

Probabilistic model of traffic scenarios for extreme load effects in long-span bridges

The traffic scenarios that may cause extreme load effects are of great importance to the safety assessment of bridge structures. The traditional simulation method of traffic flow cannot depict the distribution pattern of vehicles on the bridge deck when the maximum effect is induced. In this paper, a probabilistic Gaussian mixture model (GMM) for heavy vehicle scenarios on the bridge deck under free-flow condition is proposed for long-span bridges based on collected Weigh in Motion (WIM) data. The scenarios of extreme response under free-flow occur more frequently than congestion scenarios and are of similar value and relevance in the daily management and safety assessment of long-span bridges.A non-stationary Poisson process is utilized to simulate the uneven occurrence of heavy vehicles in different lanes, and it is assumed that they are located within the artificially defined cells on the bridge deck. Then, Nataf transformation is employed to consider the correlation of gross vehicle weights (GVWs) within close range in the same lane. The numerical study is carried out on a long-span cable-stayed bridge to investigate the effects of correlation in GVWs and stationarity of vehicle distribution location on the structural responses. The load responses calculated by the proposed model and Monte Carlo method for different effects are compared with the values derived from code model. The results show that with the increase of the correlation level of the neighboring GVWs, the simulated responses are more prone to get extreme values, which means an increasing probability of the most unfavorable spatial distribution of on-bridge vehicles. The same results are also found under the non-stationary simulation state for vehicle location. The non-stationary Poisson process provides an efficient, highly feasible method, which is also in the safe side, for simulating the vehicle spatial distribution for specific effects.

Impact of Heavy Vehicle Axle Overload and Axle Configuration on Road Damage in Afghanistan

Aim: This study aimed to determine the percentage of heavy vehicles, overloaded vehicles, and heaviest vehicle that leads to deterioration of asphalt roads. Furthermore, the study aimed to assess the impact of ESALs and axle configuration on asphalt thickness and subgrade.
 Methods: This research used quantitative truck traffic data collected for three months in the Laghman WIM (Weigh in Motion) station located on the Kabul Torkham National Highway. Observed axle loads and GVW (Gross Vehicle Weight) data collected from April/ 10/2022, up to June/7/2022 average daily truck traffic of 73895 was estimated axle loads converted to the standard axle ESALs (Equivalent Single Axle Loads).
 Results: The percentage of heavy overweight vehicles is 13% while the heaviest vehicle is T2-Bedford truck vehicles at 20.17%. The T2 Bedford truck percentage was 42.17% in overall volume; this truck was overloaded 20.17%, compared to the general trend of Ministry of Public Works (MoPW), the T3 Hino truck contribution was 1.24% in total volume and 18.64% was overloaded, the ST112 semi-trailer contributed 3.02% in total volume, and 8.52% were overloaded. The ST113 5-axle and TT123 6-axle trailers percentile in entire volume was 9.6 % and 40.85% whereas, the overloading degree for these heavy vehicles was 2.29% and 6.72% respectively.
 Conclusion: Overloading percentage is 13% while, T2 Truck is the heaviest vehicle The variation of ESALs and axle configuration on asphalt thickness and subgrade modulus illustrates the higher thickness of asphalt decreases the damage to asphalt roads.
 Recommendations: Afghanistan Weigh-in-Motion stations need professional employees, internet facilities, electricity, and proper databases to record the data. By applying tight legislation and fines overloading will be minimized. Researchers may also research heavy vehicle tires' impact on road damage. Furthermore, it needs to determine the level of fatigue cracking and rutting failures by overloading.

Automated vehicle wheelbase measurement using computer vision and view geometry

For different transportation agencies that monitor vehicle overloads, develop policies to mitigate the impact of vehicles on infrastructure, and provide the necessary data for road maintenance, they all rely on precise, detailed and real-time vehicle data. Currently, real-time collection of vehicle data (type, axle load, geometry, etc) is typically performed through weigh-in-motion (WIM) stations. In particular, the bridge WIM (BWIM) technology, which uses instrumented bridges as weighing platforms, has proven to be the most widely used inspection method. For most of the BWIM algorithms, the position of the vehicle’s axle (i.e. vehicle wheelbase) needs to be measured before calculating the axle load, and the identification of the axle load is very sensitive to the accuracy of the vehicle wheelbase. In addition, the vehicle’s wheelbase is also important data when counting stochastic traffic flow and classifying passing vehicles. When performing these statistics, the amount of data is often very large, and the statistics can take years or even decades to complete. Traditional manual inspection and recording approaches are clearly not up to the task. Therefore, to achieve automatic measurement of the on-road vehicles’ wheelbase, a framework based on computer vision and view geometry is developed. First, images of on-road vehicles are captured. From the images, the vehicle and wheel regions can be accurately detected based on the You Only Look Once version 5 (YOLOv5) architecture. Then, the residual unified network model is improved and an accurate semantic segmentation of the wheel within the bounding box is performed. Finally, a view geometry-based algorithm is developed for identifying vehicle wheelbase. The accuracy of the proposed method is verified by comparing the identified results with the true wheelbases of both two-axle vehicles and multi-axis vehicles. To further validate the effectiveness and robustness of the framework, the effects of important factors, such as camera position, vehicle angle, and camera resolution, are investigated through parametric studies. To illustrate its superiority, the developed vehicle wheelbase measurement algorithm is compared with two other advanced vehicle geometry parameter identification algorithms and the results show that the developed algorithm outperforms the other two methods in terms of the degree of automation and accuracy.

Updated load models for short‐span road bridges in the range of 2‐15 m

AbstractIn recent years, increased effort has focused on developing traffic load models for the safety verification of existing road bridges. In Switzerland for example, this was achieved by updating the alpha reduction factors in the Traffic Load Model 1 (LM1) for new bridges (analogous to the Eurocode LM1 alpha factors), providing a practica approach. The enveloping of the maximum simulated alpha factors, which were calculated by applying measured vehicles to different traffic scenarios, spans and bridge cross‐section types, resulted in the updated LM1 in the SIA 269/1 Standard for Existing Structures. The model development considered spans in the range of 10‐80 m and applied dynamic amplifications as a function of the gross vehicle weight. One limitation of the current updated LM1 is that, as a very high proportion of existing road bridges have spans of less than 15 m, the load effects in this short‐span range tend to be overly conservative. This paper studies the load effect behaviour in the longitudinal and transverse directions, based on actual traffic from Weigh‐in‐Motion (WIM) data. The study utilises all WIM data recorded in Switzerland over the last 10 years. Results from three new short‐span load models are presented which, together, envelope the extreme load effects. A parametric study is presented which highlights some conservatism in the existing code load models. Finally, a case study project is presented, in which these updated models were used to verify the structural safety of a historic road bridge.