PSI - Issue 84

Andy Duarte-Taño et al. / Procedia Structural Integrity 84 (2026) 280–287

287

highlighting the increased sensitivity of field measurements to environmental variability and measurement noise. An important practical implication: while aggressive compression may be feasible under controlled conditions, real structural monitoring scenarios require moderate compression levels to maintain diagnostic reliability. Despite this limitation, the proposed lightweight architecture demonstrated strong capability to preserve dominant modal characteristics without relying on prior modal knowledge or sparsity assumptions. Future work will focus on implementing the proposed model on low-power microcontroller units for wireless sensor nodes, enabling on-board data compression prior to transmission. Acknowledgements This work was supported by Grant PRE2021-097396 funded by MICIU/AEI/10.13039/501100011033 and, as appropriate, by ``ESF Investing in your future'', by ``ESF+'' or by ``European Union NextGenerationEU/PRTR''. This work was also supported by FABRE—“Research consortium for the evaluation and monitoring of bridges, viaducts and other structures” (www.consorziofabre.it/en) within the activities of theFABRE-ANAS 2021–2026 research program. Any opinions expressed in this paper do not necessarily reflect the views of the funders or the individuals who produced the non-original documentation analyzed in this article. References Delgadillo, R.M., Casas, J.R., 2022. Bridge damage detection via improved completed ensemble empirical mode decomposition with adaptive noise and machine learning algorithms. Structural Control and Health Monitoring 29(8), e2966. Ni, Y.C., Alamdari, M.M., Ye, X.W., Zhang, F.L., 2021. Fast operational modal analysis of a single-tower cable-stayed bridge by a Bayesian method. Measurement 174, 109048. Zhu, Y.-C., Xie, Y.-L., Au, S.-K., 2018. Operational modal analysis of an eight-storey building with asynchronous data incorporating multiple setups. Engineering Structures 165, 50–62. Jaafar, K.A., Saleh, I.N., Abduladhem, A.A., 2012. Vibration data compression in wireless sensors network. 2012 IEEE International Conference on Signal Processing, Communication and Computing (ICSPCC), pp. 717–722. Alsalaet, J.K., Ali, A.A., 2015. Data compression in wireless sensors network using MDCT and embedded harmonic coding. ISA Transactions 56, 261–267. Talebi-Kalaleh, M., Mei, Q., 2024. Damage detection in bridge structures through compressed sensing of crowdsourced smartphone data. Structural Control and Health Monitoring, Article ID 5436675. Zhou, J., Li, H.-W., Wang, Y.-W., Ni, Y.-Q., 2025. Operational modal damping identification based on compressive sensing. Journal of Civil Structural Health Monitoring, 1–15. Donoho, D.L., 2006. Compressed sensing. IEEE Transactions on Information Theory 52(4), 1289–1306. Bao, Y., Tang, Z., Li, H., 2020. Compressive-sensing data reconstruction for structural health monitoring: a machine-learning approach. Structural Health Monitoring 19(1), 293–304. Zhang, L., Jia, J., Bai, Y., Du, X., Lin, P., Guo, H., 2024. SHM data compression and reconstruction based on IGWO-OMP algorithm. Engineering Structures 314, 118340. An, Y., Xue, Z., Ou, J., 2024. Deep learning-based sparsity-free compressive sensing method for high accuracy structural vibration response reconstruction. Mechanical Systems and Signal Processing 211, 111168. Zonzini, F., Zauli, M., Mangia, M., Testoni, N., De Marchi, L., 2021. Model-assisted compressed sensing for vibration-based structural health monitoring. IEEE Transactions on Industrial Informatics 17(11), 7338–7347. He, K., Zhang, X., Ren, S., Sun, J., 2016. Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , pp. 770–778. Prechelt, L., 2002. Early stopping—but when? In: Neural Networks: Tricks of the Trade . Springer, pp. 55–69. García-Macías, E., Ubertini, F., 2020. MOVA/MOSS: Two integrated software solutions for comprehensive structural health monitoring of structures. Mechanical Systems and Signal Processing 143, 106830. Hernández-Montes, E., Jalon, M.L., Rodríguez-Romero, R., Chiachío, J., Compán-Cardiel, V., Gil-Martin, L.M., 2023. Bayesian structural parameter identification from ambient vibration in cultural heritage buildings: The case of the San Jerónimo monastery in Granada, Spain. Engineering Structures 284, 115924.