Weigh-in-motion (WIM) sensor based on EM resonant measurements

Liu, C.R.; Guo, Lianhe; Li, J.; Chen, Xuemin

doi:10.1109/aps.2007.4395555

Cited by 9 publications

(5 citation statements)

References 1 publication

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“…BWIM is often conducted by employing strain gauges and the Moses algorithm that optimizes weight factors by minimizing the sum of squares of differences between measured and theoretical strains or deflections [14][15][16], and by employing accelerometers to extract information from dynamic signatures [17] using, for example, the Moving Force Identification (MFI) technique [12,18,19]. Other technologies have been proposed for WIM sensing, including contactless BWIM using two cameras to track deformation and axle spacings [20], piezoelectric sensors to measure pavement response under load [21], fiber optic sensors by measuring changes in the light beam characteristics upon load [22][23][24], and microwave-based sensors identifying changes in electric fields [25].…”

Section: Introductionmentioning

confidence: 99%

A Weigh-in-Motion Characterization Algorithm for Smart Pavements Based on Conductive Cementitious Materials

Birgin

Laflamme

D’Alessandro

et al. 2020

Sensors

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Smart materials are promising technologies for reducing the instrumentation cost required to continuously monitor road infrastructures, by transforming roadways into multifunctional elements capable of self-sensing. This study investigates a novel algorithm empowering smart pavements with weigh-in-motion (WIM) characterization capabilities. The application domain of interest is a cementitious-based smart pavement installed on a bridge over separate sections. Each section transduces axial strain provoked by the passage of a vehicle into a measurable change in electrical resistance arising from the piezoresistive effect of the smart material. The WIM characterization algorithm is as follows. First, basis signals from axles are generated from a finite element model of the structure equipped with the smart pavement and subjected to given vehicle loads. Second, the measured signal is matched by finding the number and weights of appropriate basis signals that would minimize the error between the numerical and measured signals, yielding information on the vehicle’s number of axles and weight per axle, therefore enabling vehicle classification capabilities. Third, the temporal correlation of the measured signals are compared across smart pavement sections to determine the vehicle weight. The proposed algorithm is validated numerically using three types of trucks defined by the Eurocodes. Results demonstrate the capability of the algorithm at conducting WIM characterization, even when two different trucks are driving in different directions across the same pavement sections. Then, a noise study is conducted, and the results conclude that a given smart pavement section operating with less than 5% noise on measurements could yield good WIM characterization results.

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Section: Introductionmentioning

confidence: 99%

A Weigh-in-Motion Characterization Algorithm for Smart Pavements Based on Conductive Cementitious Materials

Birgin

Laflamme

D’Alessandro

et al. 2020

Sensors

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show abstract

“…A novel WIM sensor based on perturbation theory of microwave resonant cavities is presented in [17], and a special fiber optic sensor based on measuring light loss under mechanical stress is discussed in [18]. However, both sensors were tested in a controlled laboratory setting, and challenges regarding road installation and sensor durability under heavy loads were not addressed.…”

Section: Related Workmentioning

confidence: 99%

In-pavement wireless weigh-in-motion

Bajwa

Rajagopal

Coleri

et al. 2013

Proceedings of the 12th International Conference on Information Processing in Sensor Networks

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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.

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“…Although WIM technologies have not advanced much in the last decade, focus has shifted on using multiple WIM sensors to improve system accuracy as opposed to requiring special material pavement near the sensors (FHWA, 2001;Burnos et al, 2007;Kwon, 2016). A novel WIM sensor based on perturbation theory of microwave resonant cavities was presented in Liu et al (2007), and a special fiber optic sensor based on measuring light loss under mechanical stress was discussed in Malla et al (2008). However, both sensors were tested in a controlled laboratory setting, and challenges of road installation and sensor durability under heavy loads were not addressed.…”

Section: Current Wim Technologiesmentioning

confidence: 99%

“…A novel WIM sensor based on perturbation theory of microwave resonant cavities was presented in Liu et al. (), and a special fiber optic sensor based on measuring light loss under mechanical stress was discussed in Malla et al. ().…”

Section: Introductionmentioning

confidence: 99%

Development of a Cost‐Effective Wireless Vibration Weigh‐In‐Motion System to Estimate Axle Weights of Trucks

Bajwa

Coleri

Rajagopal

et al. 2017

Computer aided Civil Eng

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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.

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Weigh-in-motion (WIM) sensor based on EM resonant measurements

Cited by 9 publications

References 1 publication

A Weigh-in-Motion Characterization Algorithm for Smart Pavements Based on Conductive Cementitious Materials

A Weigh-in-Motion Characterization Algorithm for Smart Pavements Based on Conductive Cementitious Materials

In-pavement wireless weigh-in-motion

Development of a Cost‐Effective Wireless Vibration Weigh‐In‐Motion System to Estimate Axle Weights of Trucks

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