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Sensors (Basel). 2018 Jun 29;18(7). pii: E2092. doi: 10.3390/s18072092.

Smart Sensing of Pavement Temperature Based on Low-Cost Sensors and V2I Communications.

Author information

1
Centre for Automation and Robotics (UPM-CSIC), Universidad Politécnica de Madrid, Calle José Gutiérrez Abascal, 2, 28006 Madrid, Spain. jorge.godoy@upm.es.
2
Centre for Automation and Robotics (UPM-CSIC), Consejo Superior de Investigaciones Científicas, Ctra. de Campo Real, km 0.200, Arganda del Rey, 28500 Madrid, Spain. rodolfo.haber@csic.es.
3
GEOCISA Geotecnia y Cimientos S.A, Los Llanos de Jérez, 10-12, 28820 Coslada, Madrid, Spain. jmunozn@geocisa.com.
4
Centre for Automation and Robotics (UPM-CSIC), Universidad Politécnica de Madrid, Calle José Gutiérrez Abascal, 2, 28006 Madrid, Spain. fernando.matia@upm.es.
5
Centre for Automation and Robotics (UPM-CSIC), Consejo Superior de Investigaciones Científicas, Ctra. de Campo Real, km 0.200, Arganda del Rey, 28500 Madrid, Spain. alvaro.garcia.r@car.upm-csic.es.

Abstract

Nowadays, the preservation, maintenance, rehabilitation, and improvement of road networks are key issues. Pavement condition is highly affected by environmental factors such as temperature and humidity, hence the importance of building databases enriched with real-time information from monitoring systems that enable the analysis and modeling of the road properties. Information and communication technologies, and specifically wireless sensor networks and computational intelligence methods, are enabling the design of new monitoring systems. The main goal of this work is the design of a pavement monitoring system for measuring temperature at internal layers. The proposed solution is based on low-cost and robust temperature sensors, vehicle-to-infrastructure communications, allowing one to transmit information directly from probes to a moving auscultation vehicle, and a neural network-based model for prediction pavement temperature. User requirements drive probes’ design to a modular device, with easy installation, low cost, and reduced energy consumption. Results of the test and validation experiments show both the benefits and viability of the proposed system, which reflect in an accuracy improvement and reduction in routine test duration. Finally, data collected over a year is applied to assess the performance of BELLS3 models and the suggested neural network for predicting pavement temperature. The dynamic behavior of the predicted temperature and the mean absolute error of the neural network-based model are better than the BELL3 model, demonstrating the suitability of the proposed pavement monitoring system.

KEYWORDS:

modeling; multilayer perceptron; pavement monitoring; vehicle-to-infrastructure; wireless sensor network

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