Structural Health Monitoring Techniques

Neueste Paper

• Towards Interpretable Damage Detection based on Aerodynamic Pressure Measurements

  Philip Franz, Max von Danwitz, Gregory Duth\'e, Alexander Popp, Eleni Chatzi · 28. September 2026

  The increasing flexibility of modern large wind turbine blades necessitates cost-efficient and reliable structural monitoring solutions. For this purpose, we propose to use aerodynamic pressure measurements obtained via Aerosense, a novel, non-intrusive and economical sensing system. In former work …

• Fast-varying Natural Frequencies and Damping Ratio Identification for Linear Time-Varying System

  Melisa Bozaci, Alice Cicirello · 18. September 2026

  This work proposes a physics-enhanced machine learning approach for the system identification of Linear Time-Varying (LTV) systems under time-varying operating conditions in terms of fast-varying natural frequencies and damping ratios by combining a long short-term memory network with an Extended Ka…

• Toward Reliable Railway-Bogie Response Prediction Using Multifidelity TDNN and Physics-Informed Residual Learning

  Gyeolhee Lee, Moosun Kim, Taewook Kwon, Jaehun Kim, Dongjin Lee · 14. September 2026

  Railway engineers need simulation models that predict vehicle responses across operating scenarios that cannot be tested exhaustively. Agreement with representative measurements provides essential evidence, but calibration at a limited set of conditions does not guarantee accuracy elsewhere. We pres…

• Field-level prediction of mid-plane stress tensor fields in concrete target penetration: a cross-velocity graph neural operator surrogate

  Wenpu Du, Peng Zhou, Yunlong Xia, Sinuo Xin, Congcong Zhang, Boyang Zhang, Yi Zhang, Wenzheng Xu · 10. September 2026

  Although the impact resistance of concrete has been studied extensively, a framework linking mesoscale heterogeneity to full-field stress-tensor prediction has been lacking. Data were generated with a full-scale aggregate-resolved LS-DYNA model (projectile diameter 45 mm, mass 2.13 kg, target diamet…

• Lowering the Barrier to AI-Driven Inspection: A No-Code Workflow for Automated Structural Defect Detection

  Michael Holm, Tanner McElroy, Xinghang Zhang, Guang Lin · 27. August 2026

  Structural health monitoring (SHM) is essential in modern engineering, providing data for condition-based maintenance, lifecycle assessment, and predictive decision-making. Traditionally, SHM relied on visual inspection to detect defects such as cracks and deformations. Early computer vision (CV) me…

• MoRF-AST: Calibrated Probabilistic Virtual Sensing for Structural Monitoring under Changing Operating Conditions

  Wingho Feng, Quanwang Li, Ming Zhong, Jingyu Yang, Chen Wang · 26. August 2026

  Probabilistic full-field reconstruction provides uncertainty-aware response evidence for structural reliability assessment, yet inference from sparse and noisy measurements remains underdetermined. Most existing methods overlook shifts between offline training and operational distributions. Under su…

• Latent variable models for simultaneous EOV identification and removal in population-based SHM

  M. D. Champneys, M. R. Jones, A. J. Hughes, T. J. Rogers, E. J. Cross, K. Worden · 13. August 2026

  The robust treatment of environmental and operational variability (EOV) is an open challenge in population-based structural health monitoring (PBSHM). The difficulty is compounded in the case that the EOV signals are unmeasured. A common approach in conventional SHM is to apply \emph{projection-base…

• When is the combined load identifiable from a stress-intensity profile? A coupled forward-inverse study on SIFBench finite-element data

  Giansalvo Cirrincione, Filippo Grassia · 16. Juli 2026

  This work studies the inverse problem of recovering the relative magnitudes of the tension, bending, and bearing loads acting on a crack from its stress-intensity-factor profile along the crack front, using the public SIFBench finite-element data. The central claim is not forensic load recovery on f…

• Uncertainty-aware damage identification in short-span bridges via physics-informed variational autoencoder

  Ana Fernandez Navamuel, A. Javier Omella, Diego Zamora-Sanchez, David Pardo · 7. Juli 2026

  Vibration-based damage identification in civil infrastructure is a challenging, ill-posed inverse problem due to measurement noise, sparse sensor arrays, and environmental variability. While deep learning is powerful for system identification, deterministic approaches lack reliable uncertainty quant…

• Robust and Explainable 3D Mode Shape Recognition Using Region-Aware Graph Neural Networks

  Tong Duy Son, Marc Brughmans, Andrey Hense, Kohta Sugiura, Sebastian Ciceo, Paolo di Carlo, Theo Geluk · 3. Juli 2026

  Mode shape recognition is a fundamental task in automotive NVH development, yet it remains dependent on manual visual inspection by experienced engineers. Existing approaches based on engineering heuristics, Modal Assurance Criterion (MAC), or geometry-dependent AI representations often exhibit limi…

• A Synthetic Reliability-Aware PINN Benchmark for Offshore Wind Turbine Support-Structure Monitoring with Bayesian Inverse Identification

  Puneet Kant, Monika Tanwar · 24. Juni 2026

  Reliable structural health monitoring (SHM) of offshore wind turbine (OWT) support structures requires fast state estimation from sparse measurements. Repeated high fidelity finite element or aeroelastic analyses are difficult to use directly in online monitoring loops, while purely data-driven surr…

• Adaptive Distance-Aware Trunk Deep Operator Learning for Long-Span Roadway Bridges

  Bilal Ahmed, Diab W. Abueidda, Waleed El-Sekelly, Tarek Abdoun, Mostafa E. Mobasher · 19. Juni 2026

  Long-span roadway bridges exhibit highly localized structural responses under vehicular loading, making repeated FE analysis computationally expensive for applications such as influence surface generation and structural digital twins. Existing SciML approaches struggle to accurately capture these lo…

• Uncertainty Aware Functional Behavior Prediction and Material Fatigue Assessment for Circular Factory

  Nehal Afifi, Mehdi Khabou, Victor Mas, Jonas Hemmerich, Patric Grauberger, Stefan Dietrich, Volker Schulze, Sven Matthiesen · 5. Juni 2026

  Returned products in circular factories re-enter production with heterogeneous degradation states, usage histories, and remaining capability. Reuse cannot be decided from the current inspection alone, because future function fulfillment and component integrity may evolve differently under the next s…

• SHM-Agents: A Generalist-Specialist Integrated Agent System for Structural Health Monitoring

  Yuequan Bao, Xing Li, Huabin Sun, Dawei Liu, Yuxuan Tian, Haiyang Hu · 14. Mai 2026

  Artificial intelligence is increasingly used to simplify complex tasks. In engineering applications of structural health monitoring (SHM), existing specialized algorithms, while effective, often face high implementation barriers, limited interoperability and complex training procedures. To overcome …

• VFM-SDM: A vision foundation model-based framework for training-free, marker-free, and calibration-free structural displacement measurement

  Qingyu Xian, Hao Cheng, Berend Jan van der Zwaag, Rolands Kromanis, Ozlem Durmaz Incel · 12. Mai 2026

  Reliable displacement measurement is fundamental for structural health monitoring and digital engineering workflows, as it provides direct structural response information. Vision-based measurement has emerged as a promising approach for low-cost, non-contact displacement monitoring. However, its dep…

• GNN for Structural Displacement Prediction

  Hung-Fu Chang, Tzu-Kang Lin, Yung-Li Cheng · 12. Mai 2026

  Accurate prediction of structural displacements under external loading is fundamental to structural health monitoring and seismic safety assessment. Although the finite element method (FEM) remains the prevailing approach because of its high accuracy, its considerable computational cost restricts it…

• Probabilistic data quality assessment for structural monitoring data via outlier-resistant conditional diffusion model

  Qi Li (Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology, School of Civil Engineering, Harbin Institute of Technology, Harbin, 150090, China, Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin, 150090, China), Yong Huang (Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology, School of Civil Engineering, Harbin Institute of Technology, Harbin, 150090, China, Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin, 150090, China), Hui Li (Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology, School of Civil Engineering, Harbin Institute of Technology, Harbin, 150090, China, Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin, 150090, China) · 30. April 2026

  Data quality assessment is an essential step that ensures the reliability of the subsequent structural health monitoring (SHM) tasks. This study proposes a prediction deviation-based SHM data quality assessment method using a univariate implicit auto-regressive model, enabling outlier diagnosis and …

• Monitoring exposure-length variations in submarine power cables using distributed fiber-optic sensing

  Sakiko Mishima, Yoshiyuki Yajima, Noriyuki Tonami, Tomoyuki Hino, Shugo Aibe, Junichiro Saikawa, Koji Mizuguchi · 29. April 2026

  This study proposes an anomaly-detection framework for monitoring exposure-length variations in submarine free-span cables using Distributed Acoustic Sensing (DAS), which is one of the distributed fiber-optic sensing technologies. To address environmental variability and limited training data in off…

• Disentangling Damage from Operational Variability: A Label-Free Self-Supervised Representation Learning Framework for Output-Only Structural Damage Identification

  Xudong Jian, Charikleia Stoura, Simon Scandella, Eleni Chatzi · 22. April 2026

  Damage identification is a core task in structural health monitoring. In practice, however, its reliability is often compromised by confounding non-damage effects, such as variations in excitation and environmental conditions, which can induce changes comparable to or larger than those caused by str…

• Hybrid Spectro-Temporal Fusion Framework for Structural Health Monitoring

  Jongyeop Kim, Jinki Kim, Doyun Lee · 21. April 2026

  Structural health monitoring plays a critical role in ensuring structural safety by analyzing vibration responses from engineering systems. This paper proposes a Spectro-Temporal Alignment framework and a Hybrid Spectro-Temporal Fusion framework that integrate arrival-time interval descriptors with …

• Transformer self-attention encoder-decoder with multimodal deep learning for response time series forecasting and digital twin support in wind structural health monitoring

  Feiyu Zhou, Marios Impraimakis · 3. April 2026

  The wind-induced structural response forecasting capabilities of a novel transformer methodology are examined here. The model also provides a digital twin component for bridge structural health monitoring. Firstly, the approach uses the temporal characteristics of the system to train a forecasting m…

• Event-Based Method for High-Speed 3D Deformation Measurement under Extreme Illumination Conditions

  Banglei Guan, Yifei Bian, Zibin Liu, Haoyang Li, Xuanyu Bai, Taihang Lei, Bin Li, Yang Shang, Qifeng Yu · 31. März 2026

  Background: Large engineering structures, such as space launch towers and suspension bridges, are subjected to extreme forces that cause high-speed 3D deformation and compromise safety. These structures typically operate under extreme illumination conditions. Traditional cameras often struggle to ha…

• Physics-Informed Framework for Impact Identification in Aerospace Composites

  Nat\'alia Ribeiro Marinho, Richard Loendersloot, Jan Willem Wiegman, Frank Grooteman, Tiedo Tinga · 31. März 2026

  This paper introduces a novel physics-informed impact identification (Phy-ID) framework. The proposed method integrates observational, inductive, and learning biases to combine physical knowledge with data-driven inference in a unified modelling strategy, achieving physically consistent and numerica…

• Transfer learning via interpolating structures

  T. A. Dardeno, A. J. Hughes, L. A. Bull, R. S. Mills, N. Dervilis, K. Worden · 25. März 2026

  Despite recent advances in population-based structural health monitoring (PBSHM), knowledge transfer between highly-disparate structures (i.e., heterogeneous populations) remains a challenge. The current work proposes that heterogeneous transfer may be accomplished via intermediate structures that b…

• Traffic and weather driven hybrid digital twin for bridge monitoring

  Phani Raja Bharath Balijepalli, Bulent Soykan, Veeraraghava Raju Hasti · 17. März 2026

  A hybrid digital twin framework is presented for bridge condition monitoring using existing traffic cameras and weather APIs, reducing reliance on dedicated sensor installations. The approach is demonstrated on the Peace Bridge (99 years in service) under high traffic demand and harsh winter exposur…