# Comparative Accuracy Analysis of Truck Weight Measurement Techniques
## 1 Introduction  
The monitoring of traffic-induced load effects is essential to maintain the safety of the transportation infrastructure. The highway system is a significant part of the national investment, and it requires adequate maintenance. The condition of roads and bridges is important for responding to transportation and economic growth. Therefore, the knowledge of traffic volume and traffic-induced load effects has to be continuously monitored and evaluated. Truck traffic is one of the critical factors influencing transportation safety and efficiency. It is related to a number of traffic accidents, maintenance scheduling, traffic flow interruption, risk control, and management. The number of accidents involving trucks may be two to four times larger than accidents with passenger cars. Traffic weight measurement is required for selecting the design and evaluation loads, as they are needed to design new structures and evaluate existing bridges. Knowledge of the actual live load can serve as a basis for calibration of the live load factors to assure the minimum safety reserve. Development of the live load model requires the measurement of the traffic, number and weight of vehicles, and load distribution on axles. In the infrastructure evaluation, truck assessment is essential since they cause most of the damage to pavements and bridges. The truck is a vehicle class 5 and above based on the Federal Highway Administration classification system, which includes two to seven or more axle single and multi-trailer vehicles. The weight measurements can be conducted by static scales, where the measured vehicle has to stop to be weighed; this approach has been used to collect traffic weight data for 40 years. In 1977 in Ontario (Canada), portable scales were used to obtain data that were used in the development of the current AASHTO LRFD for bridge design. However, the static weighing methods are local and selective, and they could measure only a small fraction of the highway traffic. At present, the vehicle weight of vehicles can be obtained using weigh-in-motion measurements (WIM). WIM measurements allow for a continuous recording of vehicles passing a measurement site. The WIM systems can collect traffic volume, vehicle configuration, load distribution on axles, etc. It is a powerful tool to collect a massive traffic database. Data is recorded for every vehicle, including a detailed description of vehicle configuration, vehicle class, measurement date and time, occupied lane, direction, and moving speed, as well as individual axle weights and axle spacings. To accurately assess the traffic-induced load effects, the data quality has to be verified. There are uncertainties involved in the measurement process that must be considered while dealing with millions of vehicle records. Several factors can affect the accuracy of the weight-in-motion measurements, such as pavement roughness (causing bouncing axle movement or dynamic impact), temperature effects, multiple presence, incorrect vehicle position, etc. Assessment of live load effect plays a key role in designing and evaluating roads and bridges to assure the safety of the infrastructure. Therefore, it is important to adequately assess the load effects to make sure they are not underestimated or overestimated. The underestimation can cause premature damage to bridges and roads, and overestimation can result in a significant cost increase. In this study, weight measurement accuracy was considered for four weighing systems: portable scales, truck weigh station, WIM, and bridge-WIM (B-WIM). The objective of this study was to assess the accuracy of the static and dynamic weighing systems. About 150 trucks were measured using static and dynamic weighing systems, and the results were compared to assess the accuracy. This study's focus was on a comparison of the accuracy of the in-motion measurement and static scales. Static scales can measure only non-moving vehicles or vehicles moving at a very low speed.