{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T05:18:58Z","timestamp":1787635138850,"version":"build-2736575974"},"reference-count":40,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T00:00:00Z","timestamp":1787443200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100006734","name":"Princeton University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100006734","id-type":"DOI","asserted-by":"crossref"}]},{"id":[{"id":"https:\/\/ror.org\/00hx57361","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Damage identification in instrumented structures is often framed through modal-parameter changes, finite element updating, or supervised classifiers. These approaches are powerful, but they require an explicit structural model, identified modes, labeled damage cases, or expert choices about reference sensors. This paper introduces a new data-driven and training-free approach with limited physical priors, defining a sensor network where each sensor is a node and the edges are defined from the spatial correlation of sensor responses. The idea is to use each sensor time history as the measured structural dynamics feature while damage is localized from the edges, whose correlations change relative to a baseline. The method is demonstrated on a full-scale seven-story reinforced-concrete shear-wall building tested at UC San Diego, considering four progressive earthquake-induced damage states and one brace-modification state. The results are compared with those obtained from a previously published finite element model. The results reveal that this network-based approach localizes the damage states in agreement with previous studies with limited prior requirements and low computational cost. Beyond damage localization, this network representation provides sensor centrality, allowing informative sensors to be selected from data rather than chosen randomly or only from experimental intuitions. For the case study, using this sensor network, we find the most central sensors, those carrying the most information with reduced trial-and-error and reduced expert intervention, and use them to recover the first three natural frequencies as a secondary dynamic check. The results show that spatial correlation networks can screen for damage, localize affected regions, and guide modal parameter extraction without building an FE model. This study opens a research avenue in which network representations of multi-sensor structural dynamics complement traditional modal analysis for structural health monitoring, with dense or heterogeneous sensing systems.<\/jats:p>","DOI":"10.3390\/s26175333","type":"journal-article","created":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T10:07:53Z","timestamp":1787566073000},"page":"5333","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Training-Free Structural Damage Localization Using Spatial-Correlation Sensor Networks: Full-Scale Validation on a Seven-Story Reinforced-Concrete Building"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-3484-9944","authenticated-orcid":false,"given":"Esmaeil","family":"Ghorbani","sequence":"first","affiliation":[{"name":"Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8849-5751","authenticated-orcid":false,"given":"J\u00fcrgen","family":"Hackl","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1098\/rsta.2006.1928","article-title":"An introduction to structural health monitoring","volume":"365","author":"Farrar","year":"2007","journal-title":"Philos. Trans. R. Soc. A Math. Phys. Eng. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.strusafe.2010.03.006","article-title":"Damage identification study of a seven-story full-scale building slice tested on the UCSD-NEES shake table","volume":"32","author":"Moaveni","year":"2010","journal-title":"Struct. Saf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1098\/rsta.2000.0717","article-title":"Vibration-based structural damage identification","volume":"359","author":"Farrar","year":"2001","journal-title":"Philos. Trans. R. Soc. A"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1177\/1475921704047500","article-title":"Vibration based condition monitoring: A review","volume":"3","author":"Carden","year":"2004","journal-title":"Struct. Health Monit."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1177\/1475921710365419","article-title":"Vibration-based damage identification methods: A review and comparative study","volume":"10","author":"Fan","year":"2011","journal-title":"Struct. Health Monit."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.jsv.2005.07.036","article-title":"Assessment of vibration-based damage identification techniques","volume":"292","author":"Alvandi","year":"2006","journal-title":"J. Sound Vib."},{"key":"ref_7","unstructured":"Friswell, M., and Mottershead, J. (2013). Finite Element Model Updating in Structural Dynamics, Springer Science & Business Media."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1016\/S0168-874X(00)00071-8","article-title":"Civil structure condition assessment by FE model updating: Methodology and case studies","volume":"37","author":"Brownjohn","year":"2001","journal-title":"Finite Elem. Anal. Des."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1006\/mssp.1995.0015","article-title":"Finite element model updating using frequency response function data: I. Theory and initial investigation","volume":"9","author":"Imregun","year":"1995","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_10","first-page":"99","article-title":"The unscented Kalman filter and particle filter methods for nonlinear structural system identification with non-collocated heterogeneous sensing","volume":"16","author":"Chatzi","year":"2009","journal-title":"Struct. Control Health Monit. Off. J. Int. Assoc. Struct. Control Monit. Eur. Assoc. Control Struct."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ymssp.2018.07.041","article-title":"Vibration-based structural health monitoring under changing environmental conditions using Kalman filtering","volume":"117","author":"Erazo","year":"2019","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_12","first-page":"e2908","article-title":"Constrained unscented Kalman filter for parameter identification of structural systems","volume":"29","author":"Li","year":"2022","journal-title":"Struct. Control Health Monit."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4301","DOI":"10.1007\/s11071-024-09760-z","article-title":"Physics-aware tuning of the unscented Kalman filter: Statistical framework for solving inverse problems involving nonlinear dynamical systems and missing data","volume":"113","author":"Ghorbani","year":"2025","journal-title":"Nonlinear Dyn."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1098\/rsta.2006.1938","article-title":"The application of machine learning to structural health monitoring","volume":"365","author":"Worden","year":"2007","journal-title":"Philos. Trans. R. Soc. A Math. Phys. Eng. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1016\/j.jcp.2018.10.045","article-title":"Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations","volume":"378","author":"Raissi","year":"2019","journal-title":"J. Comput. Phys."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"110893","DOI":"10.1016\/j.ymssp.2023.110893","article-title":"Phase-based motion estimation and SVR smooth for target-free 3D deformation measurement using stereophotogrammetry","volume":"206","author":"Wang","year":"2024","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"120029","DOI":"10.1016\/j.measurement.2025.120029","article-title":"Accurate structural displacement measurement via enhanced millimeter-wave radar interferometry using multi-chirp-based adaptive phase unwrapping","volume":"262","author":"Ma","year":"2026","journal-title":"Measurement"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.jsv.2018.01.050","article-title":"Vibration-based damage detection in wind turbine blades using phase-based motion estimation and motion magnification","volume":"421","author":"Sarrafi","year":"2018","journal-title":"J. Sound Vib."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"118390","DOI":"10.1016\/j.engstruct.2024.118390","article-title":"Target-free measurement of cable forces based on computer vision and equivalent frequency difference","volume":"314","author":"Luo","year":"2024","journal-title":"Eng. Struct."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"113966","DOI":"10.1016\/j.ymssp.2026.113966","article-title":"Target-free measurement of cable forces based on video motion magnification and high-order mode shapes","volume":"247","author":"Luo","year":"2026","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2360","DOI":"10.1177\/14759217211056832","article-title":"Restoration of missing structural health monitoring data using spatiotemporal graph attention networks","volume":"21","author":"Niu","year":"2022","journal-title":"Struct. Health Monit."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ni, Y., Jin, Q., and Hu, R. (2025). A Novel Unsupervised Structural Damage Detection Method Based on TCN-GAT Autoencoder. Sensors, 25.","DOI":"10.3390\/s25216724"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1109\/MSP.2012.2235192","article-title":"The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains","volume":"30","author":"Shuman","year":"2013","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"808","DOI":"10.1109\/JPROC.2018.2820126","article-title":"Graph signal processing: Overview, challenges, and applications","volume":"106","author":"Ortega","year":"2018","journal-title":"Proc. IEEE"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1007\/s41109-021-00438-8","article-title":"A computational framework for modeling complex sensor network data using graph signal processing and graph neural networks in structural health monitoring","volume":"6","author":"Bloemheuvel","year":"2021","journal-title":"Appl. Netw. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.istruc.2022.01.066","article-title":"Structural damage detection framework based on graph convolutional network directly using vibration data","volume":"38","author":"Dang","year":"2022","journal-title":"Structures"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Stamatelatos, G., Galanopoulos, G., Zarouchas, D., and Loutas, T. (2025). Graph neural networks for SHM: Exploiting spatial interdependencies of strain data for diagnostics and prognostics. Struct. Health Monit.","DOI":"10.1177\/14759217251386802"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yang, J., Pu, P., and Zhao, Q. (2026). Physics-guided spatiotemporal learning with invariant graph for cross-domain structural damage identification. Struct. Health Monit.","DOI":"10.1177\/14759217261415794"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1016\/j.ymssp.2017.02.026","article-title":"Pairwise graphical models for structural health monitoring with dense sensor arrays","volume":"93","author":"Chen","year":"2017","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1225","DOI":"10.1177\/1475921717750047","article-title":"Structural health monitoring: Closing the gap between research and industrial deployment","volume":"17","author":"Cawley","year":"2018","journal-title":"Struct. Health Monit."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1061\/(ASCE)ST.1943-541X.0000300","article-title":"System identification study of a 7-story full-scale building slice tested on the UCSD-NEES shake table","volume":"137","author":"Moaveni","year":"2011","journal-title":"J. Struct. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1326","DOI":"10.1002\/eqe.4063","article-title":"Time-varying reliability analysis based on hybrid Kalman filtering and probability density evolution","volume":"53","author":"Ghorbani","year":"2024","journal-title":"Earthq. Eng. Struct. Dyn."},{"key":"ref_33","unstructured":"Panagiotou, M. (2008). Seismic Design, Testing and Analysis of Reinforced Concrete Wall Buildings. [Ph.D. Thesis, University of California]."},{"key":"ref_34","unstructured":"Panagiotou, M., Restrepo, J., and Conte, J. (2006). Shake Table Test of 7-Story RC Bearing Wall Building, U.S. National Science Foundation."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1037\/met0000079","article-title":"Comparing the Pearson and Spearman correlation coefficients across distributions and sample sizes: A tutorial using simulations and empirical data","volume":"21","author":"Gosling","year":"2016","journal-title":"Psychol. Methods"},{"key":"ref_36","first-page":"3","article-title":"On the \u201cprobable error\u201d of a coefficient of correlation deduced from a small sample","volume":"1","author":"Fisher","year":"1921","journal-title":"Metron"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"696","DOI":"10.2514\/3.57251","article-title":"Random decrement technique for modal identification of structures","volume":"14","author":"Ibrahim","year":"1977","journal-title":"J. Spacecr. Rocket."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"109060","DOI":"10.1016\/j.ymssp.2022.109060","article-title":"On the theory of random decrement","volume":"173","author":"Brincker","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_39","unstructured":"Brincker, R., and Rodrigues, J. Application of the random decrement technique in operational modal analysis. Proceedings of the 1st International Operational Modal Analysis Conference (IOMAC), Aalborg, Denmark, 26\u201327 April 2005, Aalborg Universitet."},{"key":"ref_40","unstructured":"Ghorbani, E. (2021). Nonlinear Kalman Filtering Based Damage Quantification for Civil Infrastructure. [Ph.D. Thesis, University of Manitoba]."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/17\/5333\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T04:26:49Z","timestamp":1787632009000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/17\/5333"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,23]]},"references-count":40,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["s26175333"],"URL":"https:\/\/doi.org\/10.3390\/s26175333","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,23]]}}}