Evaluation Study of Pavement Condition Using Digital Twins and Deep Learning on IMU Signals

Luis-Dagoberto Gurrola-Mijares, José-Manuel Mejía-Muñoz, Oliverio Cruz-Mejía (), Abraham-Leonel López-León and Leticia Ortega-Máynez
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Luis-Dagoberto Gurrola-Mijares: Departamento de Ingeniería Eléctrica, Instituto de Ingenieria y Tecnologia, Universidad Autónoma de Ciudad Juárez, Ciudad Juarez 32310, Mexico
José-Manuel Mejía-Muñoz: Departamento de Ingeniería Eléctrica, Instituto de Ingenieria y Tecnologia, Universidad Autónoma de Ciudad Juárez, Ciudad Juarez 32310, Mexico
Oliverio Cruz-Mejía: Departamento de Ingeniería Industrial, FES Aragón, Universidad Nacional Autónoma de México, Mexico 57171, Mexico
Abraham-Leonel López-León: Departamento de Ingeniería Civil y Ambiental, Instituto de Ingenieria y Tecnologia, Universidad Autónoma de Ciudad Juárez, Ciudad Juarez 32310, Mexico
Leticia Ortega-Máynez: Departamento de Ingeniería Eléctrica, Instituto de Ingenieria y Tecnologia, Universidad Autónoma de Ciudad Juárez, Ciudad Juarez 32310, Mexico

Future Internet, 2025, vol. 17, issue 10, 1-19

Abstract: Traditional road asset management relies on periodic, often inefficient, inspections. Digital Twins offer a paradigm shift towards proactive, data-driven maintenance by creating a real-time virtual replica of physical infrastructure. This paper proposes a comprehensive, formalized framework for a highway Digital Twin, structured into three integrated components: a Physical Space, which defines key performance indicators through mathematical state vectors; a Data Interconnection layer for real-time data processing; and a Virtual Space equipped with hybrid models. We provide a formal definition of these state vectors and a dynamic synchronization mechanism between the physical and virtual spaces. In this study, we focused on pavement condition assessment by using a data-driven component using accessible technology. This study show the synergy between the Digital Twin and deep learning, specifically by integrating advanced analytical models within the Virtual Space for intelligent pavement condition assessment. To validate this approach, a case study was conducted to classify road surface anomalies using low-cost Inertial Measurement Unit (IMU) data. We evaluated several machine learning classifiers and introduced a novel parallel Gated Recurrent Unit network. The results demonstrate that our proposed architecture achieved superior performance, with an accuracy of 89.5% and an F1-score of 0.875, significantly outperforming traditional methods. The findings validate the viability of the proposed Digital Twin framework and highlight its potential to achieve high-precision pavement monitoring using low-cost sensor data, a critical step towards intelligent road infrastructure management.

Keywords: digital twin; road infrastructure; pavement condition; predictive maintenance; machine learning; deep learning; intelligent transportation systems; pavement condition (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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