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Gulisano F.; Apaza Apaza F.r.; Gálvez-Pérez D.; Jurado-Piña R.; Boada-Parra G.; Gallego J.AuthorApaza Apaza, Freddy RichardAuthorGalvez Perez, DanielAuthorJurado-Piña RAuthorBoada Parra, Luis GustavoAuthorGallego JAuthor

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February 21, 2024
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Non-destructive testing methods for road pavement health monitoring: Electromechanical assessment of self-sensing asphalt materials

Publicated to:Proceedings Of Spie - The International Society For Optical Engineering. 12734 - 2023-01-01 12734(), DOI: 10.1117/12.2680606

Authors: Gulisano F; Apaza Apaza FR; Gálvez-Pérez D; Jurado-Piña R; Boada-Parra G; Gallego J

Affiliations

Universidad Politécnica de Madrid, Departamento de Ingeniería del Transporte, Territorio y Urbanismo, C/Profesor Aranguren 3, Madrid, 28040, Spain - Author

Abstract

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 asphaltbased 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. © 2023 SPIE. All rights reserved.

Keywords

Asphalt materialsAsphalt mixtureAsphalt mixturesBridge decksCarbon fibersContinuous assessmentDecision makingDeteriorationDigitalizationDynamic loadsElectrical responseHealth monitoringLearning algorithmsMachine learningMachine-learningNon destructive testingNon-destructive testingNondestructive examinationNondestructive testing methodRoad pavementsRoads and streetsSelf-sensingStructural health monitoring

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Proceedings Of Spie - The International Society For Optical Engineering, Q3 Agency Scopus (SJR), its regional focus and specialization in Electrical and Electronic Engineering, give it significant recognition in a specific niche of scientific knowledge at an international level.

From a relative perspective, and based on the normalized impact indicator calculated from the Field Citation Ratio (FCR) of the Dimensions source, it yields a value of: 1.66, which indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: Dimensions Sep 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-09-23, the following number of citations:

  • Scopus: 4
  • Google Scholar: 4

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-09-23:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 9 (PlumX).

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (GULISANO, FEDERICO) and Last Author (GULISANO, FEDERICO).