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

1
Centre for Automation and Robotics (UPM-CSIC), Universidad Politécnica de Madrid, Calle José Gutiérrez Abascal, 2, 28006 Madrid, Spain
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
3
GEOCISA Geotecnia y Cimientos S.A, Los Llanos de Jérez, 10-12, 28820 Coslada, Madrid, Spain
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(7), 2092; https://doi.org/10.3390/s18072092
Received: 31 May 2018 / Revised: 25 June 2018 / Accepted: 26 June 2018 / Published: 29 June 2018
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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. View Full-Text
Keywords: pavement monitoring; vehicle-to-infrastructure; wireless sensor network; modeling; multilayer perceptron pavement monitoring; vehicle-to-infrastructure; wireless sensor network; modeling; multilayer perceptron
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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MDPI and ACS Style

Godoy, J.; Haber, R.; Muñoz, J.J.; Matía, F.; García, Á. Smart Sensing of Pavement Temperature Based on Low-Cost Sensors and V2I Communications. Sensors 2018, 18, 2092.

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