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

Abstract

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.

Smart Graphite–Cement Composite for Roadway-Integrated Weigh-In-Motion Sensing

“…Values of sensitivity, or gauge factors, are consistent with literature [ 50 ] and increase with increasing graphite-to-cement ratio up to 20%, reaching a maximum at 20% if results from the 40% are ignored due to the unreliability of the samples. This is in agreement with the analytical results.…”

“…In a first study on self-sensing smart materials, the authors have numerically studied an algorithm enabling WIM for estimating the axle number and weights along with the vehicle speed [50]. Here, research is extended to studying the electromechanical properties of scalable, graphite-based smart materials to empower the formulation of more accurate models and field deployments of the WIM technology.…”

Hybrid Carbon Microfibers-Graphite Fillers for Piezoresistive Cementitious Composites

“…An advantage of self-sensing structural materials over traditional techniques is in the enhanced mechanical bonding [ 18 , 19 ] and durability [ 20 , 21 ]. They are also known to yield better sensing performance resulting from the significantly enhanced piezoresistive effect.…”

Estimation of the Vehicle Speed Using Cross-Correlation Algorithms and MEMS Wireless Sensors

“…The overall correct classification rate was above 80% with only two of the 54 body classes possessing the correct classification rate below 60%. Birgin [ 4 ] combined a piezoresistive material with a cement concrete to develop a novel material. This material paved on the bridge was utilized as the sensors in the WIM system to determine the vehicle classification and the vehicle weight [ 4 ].…”