Open Access

ARTICLE

Health Monitoring and Maintenance of Urban Road Infrastructure Using Temporal Convolutional Networks with Adaptive Activation

Zongqi Li¹, Hongwei Zhao^2,*, Jianyong Guo²

1 Computer Science and Technology, Beijing Jiaotong University (Weihai), Weihai, 266440, China
2 The Center of Network Security and Informatization, Beijing Jiaotong University, Beijing, 100044, China

* Corresponding Author: Hongwei Zhao. Email:

Computer Modeling in Engineering & Sciences 2025, 144(1), 345-357. https://doi.org/10.32604/cmes.2025.066175

Received 31 March 2025; Accepted 16 June 2025; Issue published 31 July 2025

Abstract

Monitoring the condition of road infrastructure is crucial for maintaining its structural integrity and ensuring safe transportation. This study proposes a deep learning framework based on Temporal Convolutional Networks (TCN) integrated with Adaptive Parametric Rectified Linear Unit (APReLU) to predict future road subbase strain trends. Our model leverages time-series strain data collected from embedded triaxial sensors within a national highway, spanning August 2021 to June 2022, to forecast strain dynamics critical for proactive maintenance planning. The TCN-APReLU architecture combines dilated causal convolutions to capture long-term dependencies and APReLU activation functions to adaptively model nonlinear strain patterns, addressing limitations of traditional ReLU in handling bidirectional strain signals (compressive and tensile). Comparative experiments demonstrate TCN-APReLU’s superior performance. These improvements highlight its enhanced accuracy in predicting strain accumulation under cyclic traffic loads, enabling maintenance teams to prioritize interventions 5–7 days before critical thresholds (e.g., >100 µε) are exceeded. This work provides a robust data-driven solution for urban road health monitoring, emphasizing scalability through parallelizable convolutions and adaptability to sensor noise. Future extensions will integrate multi-modal data to further generalize predictions across diverse infrastructure scenarios.

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Keywords

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