Model-based vs. Data-driven Approaches for Anomaly Detection in Structural Health Monitoring: a Case Study
Open accessAbstract
Modern Structural Health Monitoring (SHM) systems are becoming of pervasive use in civil engineering because they can track the structural condition and detect damages of critical and civil infrastructures such as buildings, viaducts, and tunnels. Although noticeable work has been done to improve anomaly detection for ensuring public safety, algorithms that can be executed on low-cost hardware for long-term monitoring are still an open issue to the community. This paper presents a new framework that exploits compression techniques to identify anomalies in the structure, avoiding continuous streaming of raw data to the cloud. We used a real installation on a bridge in Italy to test the proposed anomaly detection algorithm. We trained three compression models, namely a Principal Component Analysis (PCA), a fully-connected autoencoder, and a convolutional autoencoder. Performance comparison is also provided through an ablation study that analyzes the impact of various parameters. Results demonstrate that the model-based approach, i.e., PCA, can reach a better accuracy whereas data-driven models, i.e., autoencoders, are limited by training set size.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Damage-detection strategies for health monitoring can be categorized into model-based and data-based methods.
“Damage-detection strategies for health monitoring can be categorized into model-based and data-based methods [264].”
From Effects of Environmental and Operational Conditions on Structural Health Monitoring and Non-Destructive Testing: A Systematic Review (2023) · cites this paper as [264]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Outlier Detection with Autoencoder Ensembles
Chen, J., Sathe, S. K., Aggarwal, C. C., & Turaga, D. S. (2017) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611974973.11 · Cited by 472 · see citing works
Deep Anomaly Detection with Deviation Networks
Pang, G., Shen, C., & van den Hengel, A. (2019) · ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD) · doi:10.1145/3292500.3330871 · Cited by 455 · see citing works
Structural damage identification based on autoencoder neural networks and deep learning
Pathirage, C. S. N., Li, J., Li, L., Hao, H., Liu, W., & Ni, P. (2018) · Engineering Structures · doi:10.1016/j.engstruct.2018.05.109 · Cited by 415 · see citing works
Long-term monitoring and data analysis of the Tamar Bridge
Cross, E. J., Koo, K. Y., Brownjohn, J., & Keith Worden, B. (2012) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2012.08.026 · Cited by 393 · see citing works
Yan, A.-M., Kerschen, G., Boe, P. D., & Golinval, J. (2005) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2004.12.003 · Cited by 290 · see citing works
Stacked Progressive Auto-Encoders (SPAE) for Face Recognition Across Poses
Kan, M., Shan, S., Chang, H., & Chen, X. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.243 · Cited by 247 · see citing works
Dynamic Detection of False Data Injection Attack in Smart Grid using Deep Learning
Niu, X., Li, J., Sun, J., & Tomsovic, K. L. (2019) · IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) · doi:10.1109/isgt.2019.8791598 · Cited by 144 · see citing works
Structure damage localization with ultrasonic guided waves based on a time–frequency method
Dai, D., & He, Q. (2013) · Signal Processing · doi:10.1016/j.sigpro.2013.05.025 · Cited by 111 · see citing works
Linear approaches to modeling nonlinearities in long-term monitoring of bridges
Figueiredo, E., & Cross, E. J. (2013) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-013-0038-3 · Cited by 91 · see citing works
Bat algorithm with principal component analysis
Cui, Z., Li, F., & Zhang, W. (2018) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-018-0888-4 · Cited by 90 · see citing works
Basharat, A., Çatbaş, F. N., & Shah, M. A. (2005) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomw.2005.6 · Cited by 89 · see citing works
Review on field monitoring of high‐rise structures
Su, J., Xia, Y., & Weng, S. (2020) · Structural Control and Health Monitoring · doi:10.1002/stc.2629 · Cited by 79 · see citing works
Internet of Things for Structural Health Monitoring
Lamonaca, F., Sciammarella, P. F., Scuro, C., Carnì, D. L., & Olivito, R. S. (2018) · IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT) · doi:10.1109/metroi4.2018.8439038 · Cited by 65 · see citing works
Outlier Detection Approaches Based on Machine Learning in the Internet-of-Things
Jiang, J., Han, G., liu, L., Shu, L., & Guizani, M. (2020) · IEEE Wireless Communications · doi:10.1109/mwc.001.1900410 · Cited by 48 · see citing works
Detection of False Data Injection Attacks Using the Autoencoder Approach
Wang, C., Tindemans, S. H., Pan, K., & Pálenský, P. (2020) · International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) · doi:10.1109/pmaps47429.2020.9183526 · Cited by 44 · see citing works
Unsupervised outlier detection in heavy-ion collisions
Thaprasop, P., Zhou, K., Steinheimer, J., & Herold, C. (2020) · Physica Scripta · doi:10.1088/1402-4896/abf214 · Cited by 38 · see citing works
Structural health monitoring: Internet of things application
Abdelgawad, A., & Yelamarthi, K. (2016) · Conference proceedings · doi:10.1109/mwscas.2016.7870118 · Cited by 37 · see citing works
Burrello, A., Marchioni, A., Brunelli, D., Benatti, S., Mangia, M., & Luca Benini, C. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3027102 · Cited by 30 · see citing works
Self-citationLow-cost and distributed health monitoring system for critical buildings
Girolami, A., Brunelli, D., & Benini, L. (2017) · IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS) · doi:10.1109/eesms.2017.8052686 · Cited by 26 · see citing works
Self-citationSiddiqui, F., Sargent, P., & Montague, G. A. (2020) · Advanced Engineering Informatics · doi:10.1016/j.aei.2020.101181 · Cited by 25 · see citing works
Cluster-Based Vibration Analysis of Structures With GSP
Zonzini, F., Girolami, A., Marchi, L. D., Marzani, A., & Brunelli, D. (2020) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2020.2979563 · Cited by 23 · see citing works
Self-citationEnhanced particle swarm optimization based on principal component analysis and line search
Zhao, X., Lin, W., & Zhang, Q. (2014) · Applied Mathematics and Computation · doi:10.1016/j.amc.2013.12.068 · Cited by 22 · see citing works
Structural Health Monitoring and Earthquake Early Warning on 5G uRLLC Network
D’Errico, L., Franchi, F., Graziosi, F., Marotta, A., Rinaldi, C., Boschi, M., & Colarieti, A. (2019) · IEEE World Forum on Internet of Things (WF-IoT) · doi:10.1109/wf-iot.2019.8767329 · Cited by 22 · see citing works
Bearing assessment tool for longitudinal bridge performance
García-Sánchez, D., Fernández-Navamuel, A., Zamora-Sánchez, D., Alvear, D., & Pardo, D. (2020) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-020-00432-1 · Cited by 22 · see citing works
Enhancing Structural Health Monitoring with Vehicle Identification and Tracking
Burrello, A., Brunelli, D., Malavisi, M., & Luca Benini, C. (2020) · IEEE International Instrumentation and Measurement Technology Conference · doi:10.1109/i2mtc43012.2020.9128641 · Cited by 17 · see citing works
Self-citationStacked Face De-Noising Auto Encoders for Expression-Robust Face Recognition
Pathirage, C. S. N., Li, L., Liu, W., & Zhang, M. (2015) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2015.7371310 · Cited by 11 · see citing works
Real-time Structural Health Monitoring System Using Internet of Things and Cloud ComputingPreprint
Chang, H., & Lin, T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.00670 · Cited by 3 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Zinno, R., Haghshenas, S. S., Guido, G., & Vitale, A. (2022) · IEEE Access · doi:10.1109/access.2022.3199443 · Cited by 181 · see citing works
State-of-the-art review on advancements of data mining in structural health monitoring
Gordan, M., Sabbagh-Yazdi, S.-R., Ismail, Z., Ghaedi, K., Carroll, P., McCrum, D. P., & Samali, B. (2022) · Measurement · doi:10.1016/j.measurement.2022.110939 · Cited by 155 · see citing works
Gomez-Cabrera, A., & Escamilla-Ambrosio, P. J. (2022) · Applied Sciences · doi:10.3390/app122110754 · Cited by 146 · see citing works
Keshmiry, A., Hassani, S., Mousavi, M., & Dackermann, U. (2023) · Buildings · doi:10.3390/buildings13040918 · Cited by 96 · see citing works
Zinno, R., Haghshenas, S. S., Guido, G., Rashvand, K., Vitale, A., & Sarhadi, A. (2022) · Applied Sciences · doi:10.3390/app13010097 · Cited by 82 · see citing works
Automated location of steel truss bridge damage using machine learning and raw strain sensor data
Parisi, F., Mangini, A. M., Fanti, M. P., & Adam, J. M. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104249 · Cited by 71 · see citing works
Structural Health Monitoring of Concrete Bridges Through Artificial Intelligence: A Narrative Review
Prakash, V., Debono, C. J., Musarat, M. A., Borg, R. P., Seychell, D., Ding, W., & Shu, J.-P. (2025) · Applied Sciences · doi:10.3390/app15094855 · Cited by 62 · see citing works
Seismic assessment of bridges through structural health monitoring: a state-of-the-art review
Karakostas, C., Quaranta, G., Chatzi, E., Zülfikar, A. C., Çetindemir, O., Roeck, G. D., Döhler, M., Limongelli, M. P., Lombaert, G., Apaydın, N. M., et al. (2023) · Bulletin of Earthquake Engineering · doi:10.1007/s10518-023-01819-3 · Cited by 65 · see citing works
Wen, M., Ibrahim, M. S., Meda, A. H., Zhang, G., & Fan, J. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121832 · Cited by 31 · see citing works
Rădulescu, M., Rădulescu, G. M. T., Naș, S. M., Rădulescu, A. T., & Rădulescu, C. (2024) · Buildings · doi:10.3390/buildings14092811 · Cited by 21 · see citing works
Duran, B., Azam, S. E., & Sanayei, M. (2024) · Infrastructures · doi:10.3390/infrastructures9120229 · Cited by 21 · see citing works
Digital Twins and Civil Engineering Phases: Reorienting Adoption Strategies
Adebiyi, T. A., Ajenifuja, N. A., & Zhang, R. (2024) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4066181 · Cited by 22 · see citing works
Fernandes, T. M., Lopez, R. H., & Ribeiro, D. R. F. (2024) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-024-00821-w · Cited by 18 · see citing works
Combining Compressed Sensing and Neural Architecture Search for Sensor-Near Vibration Diagnostics
Ragusa, E., Zonzini, F., Gastaldo, P., & Marchi, L. D. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3395648 · Cited by 18 · see citing works
Barchi, F., Zanatta, L., Parisi, E., Burrello, A., Brunelli, D., Bartolini, A., & Acquaviva, A. (2021) · Future Internet · doi:10.3390/fi13080219 · Cited by 18 · see citing works
Self-citationTang, Y., Wan, S., Yang, Q., Chen, Z., & Xu, Y. (2025) · Structural Control and Health Monitoring · doi:10.1155/stc/8870840 · Cited by 20 · see citing works
Dang, V.-H., & Nguyen, H. X. (2024) · Journal of Vibration Engineering & Technologies · doi:10.1007/s42417-024-01325-z · Cited by 13 · see citing works
Taxonomic framework for neural network-based anomaly detection in bridge monitoring
Bayane, I., Leander, J., & Karoumi, R. (2025) · Automation in Construction · doi:10.1016/j.autcon.2025.106113 · Cited by 13 · see citing works
A Cepstrum-Informed neural network for Vibration-Based structural damage assessment
Li, L., Brügger, A., Betti, R., Shen, Z., Gan, L., & Gu, H. (2024) · Computers & Structures · doi:10.1016/j.compstruc.2024.107592 · Cited by 9 · see citing works
Increasing the Reliability of Industrial Overhead Cranes by Structural Health Monitoring
Wittenberghe, J. V., Adriano, V., Yilmaz, O., & den Abeele, F. V. (2024) · Procedia Structural Integrity · doi:10.1016/j.prostr.2024.03.012 · Cited by 7 · see citing works
Digital Transformation - Towards New Frontiers and Business Opportunities
Petrillo, A., Felice, F. D., Achim, M. V., & Mirza, N. (2022) · IntechOpen eBooks · doi:10.5772/intechopen.97989 · Cited by 8 · see citing works
AI-based structural health monitoring systems
Keshmiry, A., Hassani, S., & Dackermann, U. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13191-2.00008-0 · Cited by 7 · see citing works
Xu, Y., Hong, W., Noori, M. N., Altabey, W. A., Silik, A., & Farhan, N. S. D. (2024) · Structural durability & health monitoring · doi:10.32604/sdhm.2024.053763 · Cited by 9 · see citing works
Anomaly Detection in Structural Health Monitoring with Ensemble Learning and Reinforcement Learning
Huang, N. (2024) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2024.0150112 · Cited by 6 · see citing works
Enhancing structural anomaly detection using a bounded autoregressive component
Xin, Z., & Goulet, J. A. (2024) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2024.111279 · Cited by 5 · see citing works
Goetz, C., & Humm, B. G. (2025) · Applied Sciences · doi:10.3390/app152111354 · Cited by 2 · see citing works
Bridge damage identification using unsupervised deep learning through variational autoencoders
Zouriq, M. F. A., Linzell, D. G., & Azam, S. E. (2026) · Structural Health Monitoring · doi:10.1177/14759217261417539 · Cited by 2 · see citing works
Zhang, X., Tian, Z., & Chen, Y. (2025) · Symmetry · doi:10.3390/sym17081334 · Cited by 2 · see citing works
Adversarially-Trained Tiny Autoencoders for Near-Sensor Continuous Structural Health Monitoring
Burrello, A., Sintoni, G., Brunelli, D., & Luca Benini, C. (2022) · IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) · doi:10.1109/aicas54282.2022.9869952 · Cited by 2 · see citing works
Self-citationZhang, Q., Yu, G., Chen, P., Zhou, B., & Gou, X. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3579837 · Cited by 1 · see citing works
Applied Mathematics Tools in Digital Transformation
Calabrò, F., Ceseri, M., & Roberto Natalini, D. (2022) · IntechOpen eBooks · doi:10.5772/intechopen.103806 · Cited by 1 · see citing works
Zhang, Q., Gou, X., Zhang, Q., & Yu, G. (2024) · Research Square · doi:10.21203/rs.3.rs-4958971/v1
Azanaw, G. M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5249495
Azanaw, G. M. (2025) · International Journal of Transportation Engineering and Technology · doi:10.11648/j.ijtet.20251102.13
Attention-Fused Staged DWT-LSTM for Fault Diagnosis of Embedded Sensors in Asphalt Pavement
Zhang, J., Duan, H., Lv, S., Ge, D., & Rao, C. (2025) · Materials · doi:10.3390/ma18163917
Peláez-Rodríguez, C., Iglesias-Pordomingo, Á., Salcedo‐Sanz, S., Lorenzana, A., & Magdaleno, Á. (2025) · Applied Sciences · doi:10.3390/app152312731
Zouriq, M. F. A., Linzell, D. G., & Azam, Y. E. (2026) · Journal of Engineering Mechanics · doi:10.1061/jenmdt.emeng-8825
Zhang, W., Qin, X., Yang, Q., Zhang, Q., & Sun, Y. (2026) · Engineering Research Express · doi:10.1088/2631-8695/ae86b1
A Combined Method of Data-Driven Structural Damage Classification by Using Acceleration Data
Adha, A., Kurniawan, M., Syarif, F., Wardana, I. N. K., Jawas, N., Harmiyati, & Laory, I. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-23716-3_35
Feng, Z., Wang, H., Tang, G., & Shao, J. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-02173-4
Shukla, A., Motwani, P., & Rajput, A. S. (2026) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-026-01148-4
- Authors
- Moallemi, A., Burrello, A., Brunelli, D., & Benini, L.
- Affiliations
- University of Bologna (IT)
- University of Trento (IT)
- Venue
- 2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)
- Publisher
- IEEE
- Published
- 2021-05-17
- Pages
- 1-6
- ISSN
- 2642-2069 (ISSN-L)
- OpenAlex
- W3175295277
- Cited by
- 41
- References
- 27
- Citations by year
- 2026: 6 · 2025: 10 · 2024: 13 · 2023: 3 · 2022: 8 · 2021: 1
- FWCI
- 17.89
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- IEEE International Instrumentation and Measurement Technology Conference (publishedVersion)
- PORTO@Iris (submittedVersion, other-oa)
- Iris (University of Trento) (submittedVersion, other-oa)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Keywords
- structural health monitoring, anomaly detection, data compression, principal component analysis, autoencoder, convolutional autoencoder, bridge monitoring, low-cost hardware, cloud data transmission, damage detection
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
