Mr. Gustavo Boada-Parra Profile

Gustavo Boada-Parra

at Univ Politécnica de Madrid

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Proceedings Article | 19 October 2023 Presentation + Paper

Non-destructive testing methods for road pavement health monitoring: electromechanical assessment of self-sensing asphalt materials

Federico Gulisano, Freddy Richard Apaza Apaza, Daniel Gálvez-Pérez, Rafael Jurado-Piña, Gustavo Boada-Parra, Juan Gallego

Proceedings Volume 12734, 127340R (2023) https://doi.org/10.1117/12.2680606

KEYWORDS: Mixtures, Asphalt pavements, Artificial neural networks, Digital signal processing, Resistance, Piezoresistivity, Nondestructive evaluation, Roads

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Nowadays, the use of Non-destructive Testing Methods (NDTs) has been consolidated for the Structural Health Monitoring (SHM) of road pavements and the continuous and rapid assessment of transport infrastructures. These methods are crucial to support Public Authorities and infrastructure managers in the decision-making processes, for programming more effective maintenance actions. Pavement instrumentation with different kinds of embedded sensors is generally employed to acquire long-term monitoring data. However, several limitations of intrusive sensors are related to the risk of premature damage and deterioration. Amongst the most recent advances in NDT methods, the use of asphalt-based self-sensing materials has progressively emerged as a promising technique for the ground-based health monitoring of road pavements. These kinds of stimuli-responsive materials can be designed by dispersing conductive carbon-based nanomaterials throughout the host-insulating asphalt pavement. More specifically, the proposed NDT sensing methodology is based on the piezoresistive effect, consisting of a change in the electrical response of the pavement when subjected to strain or damage. Real-time reliable data about the structural health condition of road pavements can be therefore obtained by measuring the electrical response of the pavement, implementing a sensing procedure. This research aims at assessing the strain and load sensing response of piezoresistive asphalt mixtures. To this purpose, electromechanical laboratory tests were conducted to evaluate the electrical response of asphalt mixtures under dynamic loading. A tailored digital signal processing and machine learning algorithms were also developed to analyze the electrical signal generated by the material and provide insightful information about its structural behavior.

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