Weigh-in-motion (WIM) sensor response model using pavement stress and deflection

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.

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

“…Consequently, changes in the axial load and total load of the vehicle according to SNR were examined to identify the relationship between the influence of noise and the estimated vehicle load. SNR can be expressed as Equation (21), where e(j) is a measured signal and s(j) is a signal without noise. SNR = 10 log 10…”

Section: Vehicle Load Estimation According To the Signal To Noise Rat...mentioning

confidence: 99%

“…In addition to high installation and maintenance costs, the WIM system also requires the vehicles to travel at a low speed for accurate vehicle load measurement. To address these problems, various WIM systems were developed [20][21][22] along with methods using such WIM systems to analyze the vehicle load [23]. A WIM system that performs automatic correction was developed [24] to improve the accuracy of vehicle load measurement, while a study was also conducted to detect bridge damage using a WIM system [25].…”

Section: Introductionmentioning

confidence: 99%

Vehicle Load Estimation Using the Reaction Force of a Vertical Displacement Sensor Based on Fiber Bragg Grating

Kim

Yun

Park

et al. 2022

Accurately calculating the vehicle load acting on a bridge at any one time is crucial to determining the integrity and safety of the bridge. To ensure this integrity and safety, information on the types, characteristics, and load of vehicles that regularly cross the bridge is very important in terms of its structural adequacy and maintenance. In this study, the vehicle load that a bridge will be subjected to was estimated using the reaction force response at the support. To estimate this response to the reaction force, a vertical displacement sensor, developed based on Fiber Bragg Grating (FBG), was applied to the Eradi Quake System (EQS), a commercially available bridge bearing. This vertical displacement sensor can measure the vertical load and has the advantage of being easy to attach and detach. To verify the performance and accuracy of this sensor, this study conducted numerical analysis and vehicle loading tests. It found that the vehicle load can be estimated from the reaction force response, as measured by the vertical displacement sensor on the bridge.

“…In order to control vehicle overload, monitor and evaluate traffic volume, and ensure the safety and efficiency of vehicle transportation, the highway Weigh-In-Motion (WIM) system was produced [ 1 , 2 ]. The WIM system automatically obtains and detects the vehicle load information by embedding load cells on the driving road without affecting the normal driving of the vehicle, so as to fulfill its purpose of overload control [ 3 , 4 ]. The core component of the vehicle WIM system is the weighing sensor.…”

Section: Introductionmentioning

confidence: 99%

Investigation of the Temperature Compensation of Piezoelectric Weigh-In-Motion Sensors Using a Machine Learning Approach

Yang

Yue

Hou

et al. 2022

Piezoelectric ceramics have good electromechanical coupling characteristics and a high sensitivity to load. One typical engineering application of piezoelectric ceramic is its use as a signal source for Weigh-In-Motion (WIM) systems in road traffic monitoring. However, piezoelectric ceramics are also sensitive to temperature, which affects their measurement accuracy. In this study, a new piezoelectric ceramic WIM sensor was developed. The output signals of sensors under different loads and temperatures were obtained. The results were corrected using polynomial regression and a Genetic Algorithm Back Propagation (GA-BP) neural network algorithm, respectively. The results show that the GA-BP neural network algorithm had a better effect on sensor temperature compensation. Before and after GA-BP compensation, the maximum relative error decreased from about 30% to less than 4%. The sensitivity coefficient of the sensor reduced from 1.0192 × 10−2/°C to 1.896 × 10−4/°C. The results show that the GA-BP algorithm greatly reduced the influence of temperature on the piezoelectric ceramic sensor and improved its temperature stability and accuracy, which helped improve the efficiency of clean-energy harvesting and conversion.