Structural Health Monitoring

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3 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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Latest papers 53

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WaveGraphNet: Physics-Consistent Guided-Wave Damage Localization through Coupled Inverse-Forward Graph Learning
May 19, 2026Vinay Sharma, Aditya Bharade, Olga FinkGraph Neural Networks Inverse Problems
EPFL, Intelligent Maintenance and Operations Systems, Lausanne, Switzerland
Cracks in the Foundation: A Civil Infrastructure Dataset to Challenge Vision Foundation Models
May 18, 2026Nicola Farronato, Niccolo Avogaro, Thomas Frick +6Vision Foundation Models Instance Segmentation
IBM Research · ETH Zürich · University of Twente
SCRWKV: Ultra-Compact Structure-Calibrated Vision-RWKV for Topological Crack Segmentation
May 14, 2026Hanxu Zhang, Chen Jia, Hui Liu +3Crack Segmentation Semantic Segmentation
Engineering Research Center of Learning-Based Intelligent System, Ministry of Education, Tianjin University of Technology, Tianjin 300384, China · Key Laboratory of Computer Vision and System, Ministry of Education, Tianjin University of Technology, Tianjin 300384, China
SHM-Agents: A Generalist-Specialist Integrated Agent System for Structural Health Monitoring
May 13, 2026Yuequan Bao, Xing Li, Huabin Sun +3LLM Agent Orchestration LLM Agents
Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin · Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin · School of Civil Engineering, Harbin Institute of Technology, Harbin
VFM-SDM: A vision foundation model-based framework for training-free, marker-free, and calibration-free structural displacement measurement
May 10, 2026Qingyu Xian, Hao Cheng, Berend Jan van der Zwaag +2Structural Health Monitoring
Pervasive Systems Research Group, Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Enschede, The Netherlands · Department of Earth Observation Science, Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands · Department of Civil Engineering and Management, Faculty of Engineering Technology, University of Twente, Enschede, The Netherlands
Contour-Native Bridge Defect Detection and Compact Digital Archiving with Frequency-Supervised Fourier Contours
May 9, 2026Jin Liu, Wang Wang, Hongxu Pu +4Frequency-Domain Image Processing Structural Health Monitoring
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430072, China · Sustainability X-Lab, The University of Hong Kong, Hong Kong, China · Department of Cyber Security, Southeast University, No. 2, Southeast University Road, Nanjing, 211102, China +2
GNN for Structural Displacement Prediction
May 8, 2026Hung-Fu Chang, Tzu-Kang Lin, Yung-Li ChengNeural Surrogate Modeling Graph Neural Networks
R. B. Annis School of Engineering University of Indianapolis Indianapolis United States · Department of Civil Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan
DART: A Vision-Language Foundation Model for Comprehensive Rope Condition Monitoring
May 6, 2026Anju Rani, Daniel Ortiz-Arroyo, Petar DurdevicJoint-Embedding Predictive Architecture Large Vision-Language Models
Department of Energy Aalborg University Esbjerg, Denmark 6700
Imagery Dataset for Remaining Useful Life Estimation of Synthetic Fibre Ropes
May 5, 2026Anju Rani, Daniel Ortiz-Arroyo, Petar DurdevicRUL Estimation Predictive Maintenance
Department of Energy Aalborg University Esbjerg, Denmark 6700
Towards Interpretable Damage Detection based on Aerodynamic Pressure Measurements
May 5, 2026Philip Franz, Max von Danwitz, Gregory Duthé +2Structural Health Monitoring
German Aerospace Center (DLR) Institute for the Protection of Terrestrial Infrastructures Rathausallee 12, 53757 St. Augustin, Germany · Institute for Structural Engineering ETH Zürich Stefano-Franscini-Platz 5, 8093 Zürich, Switzerland · Institute for Mathematics and Computer-Based Simulations (IMCS) University of the Bundeswehr Munich Werner-Heisenberg-Weg 39, 85577, Neubiberg, Germany
UnGAP: Uncertainty-Guided Affine Prompting for Real-Time Crack Segmentation
May 4, 2026Conghui Li, Huanyu He, Xin Wang +2Segmentation Uncertainty Estimation Crack Segmentation
School of Information Technology, Monash University, Selangor, Malaysia · Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China · School of Engineering, Monash University, Selangor, Malaysia
Equation-Free Digital Twins for Nonlinear Structural Dynamics
May 1, 2026Mohammad Mahdi Abaei, Ahmad BahooToroody, Arttu Polojärvi +4Koopman Operator Learning Nonlinear System Identification
Department of Mechanical Engineering, Marine Technology and Arctic Group, Aalto University, Finland · Independent Expert, Digital Twin Technology, Ribe, Denmark · Institute for Risk and Reliability, Leibniz University Hannover, Germany
Training-Free Tunnel Defect Inspection and Engineering Interpretation via Visual Recalibration and Entity Reconstruction
Apr 30, 2026Shipeng Liu, Liang Zhao, Dengfeng Chen +1Open-Vocabulary Object Detection Promptable Image Segmentation
Xi’an University of Architecture and Technology, School of mechanical and electrical engineering, Xi’an, 710055, Shaanxi, China · Xi’an University of Architecture and Technology, School of Information and Control Engineering, Xi’an, 710055, Shaanxi, China · Xi’an University of Architecture and Technology, School of Civil Engineering, Xi’an, 710055, Shaanxi, China
Green Physics-Informed Machine Learning Models For Structural Health Monitoring
Apr 30, 2026Daisy R Bradley, Elizabeth J CrossEnergy-Efficient ML Physics-Informed ML
University of Sheffield Sheffield, UK
Robust Lightweight Crack Classification for Real-Time UAV Bridge Inspection
Apr 30, 2026Wei Li, Haisheng Li, Weijie Li +3Convolutional Neural Networks Lightweight CNNs
Bay Area Super Bridge Maintenance Technology Center, Guangdong Provincial Highway Construction Co., Ltd., Guangdong, China · Guangdong AIHISUN Technology Co., Ltd., Guangdong, China
PiGGO: Physics-Guided Learnable Graph Kalman Filters for Virtual Sensing of Nonlinear Dynamic Structures under Uncertainty
Apr 29, 2026Marcus Haywood-Alexander, Gregory Duthé, Eleni ChatziPhysics-Informed ML Virtual Sensing
aDepartment of Civil, Environmental and Geomatic Engineering, ETH Zürich, Wolfgang-Pauli Strasse 5, 8049, Zürich, Switzerland · bFuture Resilient Systems, Singapore-ETH Centre, Singapore, Singapore
Probabilistic data quality assessment for structural monitoring data via outlier-resistant conditional diffusion model
Apr 29, 2026Qi Li, Yong Huang, Hui LiConditional Diffusion Models Structural Health Monitoring
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
Adaptive Meta-Learning Stochastic Gradient Hamiltonian Monte Carlo Simulation for Bayesian Updating of Structural Dynamic Models
Apr 28, 2026Xianghao Meng, James L. Beck, Yong Huang +1Markov Chain Monte Carlo Meta-Learning
Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin, China · Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin, China · Division of Engineering and Applied Science, California Institute of Technology, CA, USA
Monitoring exposure-length variations in submarine power cables using distributed fiber-optic sensing
Apr 27, 2026Sakiko Mishima, Yoshiyuki Yajima, Noriyuki Tonami +4Structural Health Monitoring Anomaly Detection
NEC Corporation, Tokyo, Japan
Probabilistic Graphical Model using Graph Neural Networks for Bayesian Inversion of Discrete Structural Component States
Apr 26, 2026Teng Li, Stephen Wu, Yong Huang +2Graph Neural Networks Bayesian Inverse Problems
Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin 150090, China · Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, School of Civil Engineering, Harbin, Institute of Technology, Harbin 150090, China · The Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, Japan +2
Disentangling Damage from Operational Variability: A Label-Free Self-Supervised Representation Learning Framework for Output-Only Structural Damage Identification
Apr 21, 2026Xudong Jian, Charikleia Stoura, Simon Scandella +1Disentangled Representation Learning Self-Supervised Time Series Representation Learning
Department of Civil, Environmental and Geomatic Engineering, ETH Zürich, Zürich, Switzerland. · Department of Mechanical Engineering, Politecnico di Milano, Italy
Hybrid Spectro-Temporal Fusion Framework for Structural Health Monitoring
Apr 17, 2026Jongyeop Kim, Jinki Kim, Doyun LeeFrequency-Domain Feature Learning Multiscale Time-Series Modeling
aDepartment of Information Technology, Georgia Southern University Statesboro, GA, USA · bDepartment of Mechanical Engineering, Georgia Southern University Statesboro, GA, USA · cDepartment of Civil Engineering and Construction, Georgia Southern University Statesboro, GA, USA
MS-SSE-Net: A Multi-Scale Spatial Squeeze-and-Excitation Network for Structural Damage Detection in Civil and Geotechnical Engineering
Apr 16, 2026Saif ur Rehman Khan, Imad Ahmed Waqar, Arooj Zaib +4Convolutional Neural Networks Structural Health Monitoring
Department of Computer Science, Rhineland-Palatinate Technical2026 University of Kaiserslautern-Landau, orgaddressKaiserslautern, 67663, Germany.Apr · 4BiogentX (biogentx.com, Renala Khurd, District Okara, Punjab, Pakistan. · 2German Research Center for Artificial Intelligence, Kaiserslautern, 67663, Germany. +2

May 19, 2026, cs.LGJ/K move · Enter open · S save

WaveGraphNet: Physics-Consistent Guided-Wave Damage Localization through Coupled Inverse-Forward Graph Learning

Vinay Sharma, Aditya Bharade, Olga Fink

EPFL, Intelligent Maintenance and Operations Systems, Lausanne, Switzerland

Graph Neural Networks Inverse Problems Structural Health Monitoring

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Guided-wave structural health monitoring enables damage localization in composite plates using sparse networks of bonded piezoelectric transducers. However, supervised localization remains weakly constrained when measurements are available from only a limited set of damage locations. Because exhaustive spatial coverage is impractical, models trained at observed locations may generalize poorly to unseen regions. We propose WaveGraphNet, an inverse-forward graph-learning framework for guided-wave damage localization in carbon-fiber-reinforced polymer (CFRP) plates. The sensing network is represented as a graph, with transducers as nodes and measured pitch-catch paths as edges. The inverse branch uses order-invariant message passing and a geometry-constrained decoder to map path-wise energy deviations to a damage coordinate. An independently trained forward branch predicts the energy-deviation pattern associated with a candidate coordinate. At test time, gradients propagated through the forward branch update only the inverse-predicted coordinate to reduce the discrepancy between the measured and predicted response patterns. The framework is evaluated on the OGW-1 benchmark using three spatial hold-out splits in which complete damage regions are excluded from training. Comparisons include non-graph and graph-learning baselines together with the training-free Reconstruction Algorithm for Probabilistic Inspection of Damage (RAPID). Under the validation-selected settings, refined WaveGraphNet achieves the lowest reported test mean absolute error on all three splits. Refinement reduces the standalone localization error by 23.6 percent-67.9 percent, with an average relative reduction of 50.4 percent. These results demonstrate that learned forward-response consistency can improve guided-wave localization under limited spatial training coverage.

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