Truck weight data plays an important role in weight enforcement and pavement condition assessment. This data is primarily obtained through weigh stations and Weigh‐In‐Motion (WIM) stations which are currently very expensive to install and maintain. This article presents results of the implementation of an inexpensive wireless sensor‐based vibration WIM system. The proposed wireless sensor network (WSN) consists of acceleration sensors that report pavement vibration; vehicle detection sensors that report a vehicle's arrival and departure times; and an access point (AP) that synchronizes all the sensors and records the sensor data. The article also describes a new method for speed compensation, an energy‐efficient algorithm (adaptive sampling method) to increase battery life, and a new modeling procedure to estimate gross vehicle weights. The system deployed near a conventional WIM system on I‐80W in Pinole, CA passed the accuracy standards for WIM systems and outperformed a nearby commercial WIM station, based on conventional technology.
Truck weight data is used in many areas of transportation such as weight enforcement and pavement condition assessment. This paper describes a wireless sensor network (WSN) that estimates the weight of moving vehicles from pavement vibrations caused by vehicular motion. The WSN consists of: acceleration sensors that report pavement vibration; vehicle detection sensors that report a vehicle's arrival and departure times; and an access point (AP) that synchronizes all the sensors and records the sensor data. The paper also describes a novel algorithm that estimates a vehicle's weight from pavement vibration and vehicle detection data, and calculates pavement deflection in the process. A prototype of the system has been deployed near a conventional Weigh-In-Motion (WIM) system on I-80 W in Pinole, CA. Weights of 52 trucks at different speeds and loads were estimated by the system under different pavement temperatures and varying environmental conditions, adding to the challenges the system must overcome. The error in load estimates was less than 10% for gross weight and 15% for individual axle weights. Different states have different requirements for WIM but the system described here outperformed the nearby conventional WIM, and meets commonly used standards in United States. The system also opens up exciting new opportunities for WSNs in pavement engineering and intelligent transportation.
Pavement condition monitoring is required to identify pavements in need of maintenance or rehabilitation. Early identification of reduction in pavement's structural resistance and improving the structural resistance by minor repairs can lead to significantly lower maintenance costs for transportation agencies. In this study, a cost-effective wireless sensor that can be embedded in the road to measure the transient vibrations due to different applied loads was tested to determine its effectiveness in terms of pavement displacement measurements. Test results show that the vibration sensor, combined with the algorithms, can be embedded in new or existing pavements and used as an accurate wireless displacement sensor. The low cost of the sensor system allows the use of these sensors at high densities for monitoring the performance of an entire road network. Outputs from the
SnowFort, an open source wireless sensor network (WSN) for data analytics, is proposed for monitoring infrastructure and environment. The wireless sensing unit is optimized to be low power for extremely long-term deployments. Several features, such as data compression and online reconfiguration, are introduced to further reduce power consumption. A low-power WSN over optimized time division multiple access scheme is designed to be scalable and reliable for a network with hundreds of sensors. Real-time data visualization and analytical tools are provided with a representational state transfer (RESTful) application programming interface. We utilize SnowFort to develop a real-time damage detection application in structural health monitoring. We develop a distributed algorithm robust to data loss and validate it in a laboratory setup.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.