@Article{cmes.2025.066175,
AUTHOR = {Zongqi Li, Hongwei Zhao, Jianyong Guo},
TITLE = {Health Monitoring and Maintenance of Urban Road Infrastructure Using Temporal Convolutional Networks with Adaptive Activation},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {144},
YEAR = {2025},
NUMBER = {1},
PAGES = {345--357},
URL = {http://www.techscience.com/CMES/v144n1/63278},
ISSN = {1526-1506},
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.},
DOI = {10.32604/cmes.2025.066175}
}