{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:31:52Z","timestamp":1787027512738,"version":"build-2736575974"},"reference-count":41,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2026,8,16]],"date-time":"2026-08-16T00:00:00Z","timestamp":1786838400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Polymers"],"abstract":"<jats:p>Data-driven models are transforming structural health monitoring (SHM) for composites. However, excessive sensor costs and scarce, noise-contaminated data hinder model accuracy and generalizability. In this study, an electromechanical impedance (EMI)-based physical features and data-driven framework for high-precision damage assessment under conditions with noise and limited data is proposed. An experimental system that incorporates random noise to simulate operational environment noise was used to simulate seven progressive simulated damage states in CFRP laminates. Adaptive low-pass parabolic filtering via a fast Fourier transform smoothing filter (FFT-SF) denoised conductance signals in the frequency domain, increased efficiency over the Hinkley criterion, and significantly suppressed false alarms from sensor drift. Three input variables were selected: the resonant frequency F (reflecting structural stiffness), the resonant amplitude A (reflecting structural damping), and the root mean square deviation (RMSD) index (a statistical measure of spectral deviation). These three variables, two physics-based features and one statistical index, formed the inputs to a three-input artificial neural network (ANN). The fusion model achieved an RMSE of 0.0752 and an R2 of 0.9807 on 105 small samples, significantly outperforming a purely data-driven single-input RMSD-ANN (RMSE of 0.1066; R2 of 0.9612). Critically, Shapley Additive Explanation (SHAP) analysis revealed that the physical features significantly enhance model interpretability and predictive reliability, maintaining &gt;98% simulated damage identification accuracy with extremely limited real data. This provides a cost-effective, high-precision, and scalable paradigm for aerospace composite SHM.<\/jats:p>","DOI":"10.3390\/polym18161995","type":"journal-article","created":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T08:05:41Z","timestamp":1786953941000},"page":"1995","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Electromechanical Impedance-Based Hybrid Physical Features and Data-Driven Framework for Simulated Damage Identification and Prediction of Composites in Noisy Environments"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-5184-7550","authenticated-orcid":false,"given":"Jianguo","family":"Ma","sequence":"first","affiliation":[{"name":"State Key Laboratory for Strength and Vibration of Mechanical Structures, Shaanxi Key Laboratory of Environment and Control for Flight Vehicle, School of Aerospace Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Longlei","family":"Dong","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Strength and Vibration of Mechanical Structures, Shaanxi Key Laboratory of Environment and Control for Flight Vehicle, School of Aerospace Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1126\/science.add4667","article-title":"Machine learning\u2013aided real-time detection of keyhole pore generation in laser powder bed fusion","volume":"379","author":"Ren","year":"2023","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"7562","DOI":"10.1038\/s41467-022-35084-w","article-title":"Data-driven discovery of dimensionless numbers and governing laws from scarce measurements","volume":"13","author":"Xie","year":"2022","journal-title":"Nat. Commun."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"4223","DOI":"10.1038\/s41467-022-31915-y","article-title":"Surrogate- and invariance-boosted contrastive learning for data-scarce applications in science","volume":"13","author":"Loh","year":"2022","journal-title":"Nat. Commun."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"9203","DOI":"10.1038\/s41467-024-53303-4","article-title":"Deep learning resilience inference for complex networked systems","volume":"15","author":"Liu","year":"2024","journal-title":"Nat. Commun."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"9513","DOI":"10.1038\/s41467-024-53654-y","article-title":"Capturing forceful interaction with deformable objects using a deep learning-powered stretchable tactile array","volume":"15","author":"Jiang","year":"2024","journal-title":"Nat. Commun."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2820","DOI":"10.2514\/1.J060131","article-title":"Data-driven aerospace engineering: Reframing the industry with machine learning","volume":"59","author":"Brunton","year":"2021","journal-title":"AIAA J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"112231","DOI":"10.1016\/j.engstruct.2021.112231","article-title":"Sensors for process and structural health monitoring of aerospace composites: A review","volume":"237","author":"Rocha","year":"2021","journal-title":"Eng. Struct."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1038\/d41586-024-02855-y","article-title":"First private spacewalk a success! What the SpaceX mission means for science","volume":"633","author":"Greshko","year":"2024","journal-title":"Nature"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"112994","DOI":"10.1016\/j.compositesb.2025.112994","article-title":"Cryogenic damage mechanism of CFRP laminates under bending load via in-situ fiber-optic acoustic emission and mode decomposition","volume":"308","author":"Su","year":"2026","journal-title":"Compos. Part B-Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"111858","DOI":"10.1016\/j.ymssp.2024.111858","article-title":"Ultrasonic guided wave-based probabilistic diagnostic imaging method with single-path-scattering sparse reconstruction for multi-damage detection in composite structures","volume":"223","author":"Sun","year":"2025","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"110954","DOI":"10.1016\/j.ymssp.2023.110954","article-title":"A novel damage detection method for carbon fibre reinforced polymer structures based on distributed strain measurements with fibre optical sensor","volume":"208","author":"Li","year":"2024","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"29695","DOI":"10.1109\/JSEN.2024.3435416","article-title":"A feasibility study on EMI techniques and disk-shaped PZT sensors for strength monitoring of cement-stabilized loess","volume":"24","author":"Ma","year":"2024","journal-title":"IEEE Sens. J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"100337","DOI":"10.1016\/j.dibe.2024.100337","article-title":"Abnormal data detection for structural health monitoring: State-of-the-art review","volume":"17","author":"Deng","year":"2024","journal-title":"Dev. Built Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/j.ymssp.2019.02.034","article-title":"Comparative analysis of signal processing techniques for impedance-based SHM applications in noisy environments","volume":"126","author":"Baptista","year":"2019","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.measurement.2019.01.054","article-title":"New signal processing approach for structural health monitoring in noisy environments based on impedance measurements","volume":"137","author":"Baptista","year":"2019","journal-title":"Measurement"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"107953","DOI":"10.1016\/j.measurement.2020.107953","article-title":"New imaging algorithm for material damage localisation based on impedance measurements under noise influence","volume":"163","author":"Baptista","year":"2020","journal-title":"Measurement"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1042","DOI":"10.1177\/1045389X221128583","article-title":"Damage detection in noisy environments based on EMI and Lamb waves: A comparative study","volume":"34","author":"Campeiro","year":"2022","journal-title":"J. Intell. Mater. Syst. Struct."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"109634","DOI":"10.1016\/j.ymssp.2022.109634","article-title":"A deep learning approach for electromechanical impedance based concrete structural damage quantification using two-dimensional convolutional neural network","volume":"183","author":"Ai","year":"2023","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"111661","DOI":"10.1016\/j.ymssp.2024.111661","article-title":"Localization and quantification of delamination\/disbond inside a composite lap-joint using novel cross and drive point mechanical impedance based feature","volume":"220","author":"Meher","year":"2024","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"120787","DOI":"10.1016\/j.engstruct.2025.120787","article-title":"Axial pressure and damage identification of plate rubber bearings using transfer learning of electromechanical admittance signals from different PZT transducers","volume":"341","author":"Ai","year":"2025","journal-title":"Eng. Struct."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"112935","DOI":"10.1016\/j.ymssp.2025.112935","article-title":"Automatic assessment of CFRP-concrete interfacial durability exposed to field conditioning and wet-dry cycling using EMI-integrated deep learning technique","volume":"235","author":"Deng","year":"2025","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4807","DOI":"10.1038\/s41467-025-60164-y","article-title":"A physics-informed and data-driven framework for robotic welding in manufacturing","volume":"16","author":"Liu","year":"2025","journal-title":"Nat. Commun."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1038\/s43588-022-00215-2","article-title":"Physics-informed recurrent neural network for time dynamics in optical resonances","volume":"2","author":"Tang","year":"2022","journal-title":"Nat. Comput. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1038\/s43588-023-00412-7","article-title":"Accelerating the design of compositionally complex materials via physics-informed artificial intelligence","volume":"3","author":"Raabe","year":"2023","journal-title":"Nat. Comput. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"119993","DOI":"10.1016\/j.compstruct.2025.119993","article-title":"Quantifying freeze-thaw resistance of fiber-reinforced stabilized loess with electromechanical impedance-based energy dissipation indicators","volume":"379","author":"Ma","year":"2026","journal-title":"Compos. Struct."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"105124","DOI":"10.1016\/j.autcon.2023.105124","article-title":"Cloud-based pipe corrosion monitoring using electromechanical impedance instrumented piezoelectric ring sensor","volume":"156","author":"Fu","year":"2023","journal-title":"Autom. Constr."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"113047","DOI":"10.1016\/j.ymssp.2025.113047","article-title":"Extracting material-dependent attenuation from the PZT monitoring system based on FEM and transformer","volume":"236","author":"Cao","year":"2025","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1177\/05831024030356001","article-title":"Overview of piezoelectric impedance-based health monitoring and path forward","volume":"35","author":"Park","year":"2003","journal-title":"Shock Vib. Dig."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"110411","DOI":"10.1016\/j.measurement.2021.110411","article-title":"Study on pre-damage diagnosis and analysis of adhesively bonded smart PZT sensors using EMI technique","volume":"188","author":"Saravanan","year":"2022","journal-title":"Measurement"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3565","DOI":"10.1038\/s41467-022-31165-y","article-title":"Near-ideal electromechanical coupling in textured piezoelectric ceramics","volume":"13","author":"Yan","year":"2022","journal-title":"Nat. Commun."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1177\/1045389X9400500102","article-title":"Coupled electro-mechanical analysis of adaptive material systems\u2014Determination of the actuator power consumption and system energy transfer","volume":"5","author":"Liang","year":"1994","journal-title":"J. Intell. Mater. Syst. Struct."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"111508","DOI":"10.1016\/j.ymssp.2024.111508","article-title":"A novel multi-damage localization method for polymers and composites based on electromechanical impedance","volume":"216","author":"Singh","year":"2024","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Mirgal, P., Del-R\u00edo-Velilla, D., Wandowski, T., Majewska, K., and Malinowski, P.H. (2025). Temperature-driven changes in electromechanical impedance technique applied for damage detection in 3D printed plates. Health Monitoring of Structural and Biological Systems XIX, SPIE.","DOI":"10.1117\/12.3051775"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"103667","DOI":"10.1016\/j.ndteint.2026.103667","article-title":"Effective damage detection method based on electromechanical impedance and parallel connection of the transducers","volume":"160","author":"Singh","year":"2026","journal-title":"NDT Int."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"115370","DOI":"10.1016\/j.measurement.2024.115370","article-title":"Diagnosis and assessment of cyclic freeze\u2013thaw damage in tunnel lining concrete using piezoelectric-based electromechanical impedance technique","volume":"238","author":"Zhang","year":"2024","journal-title":"Measurement"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"116841","DOI":"10.1016\/j.measurement.2025.116841","article-title":"Electromechanical impedance-based measurements for damage detection and characterization in medium-thick plates","volume":"248","author":"Khayatazad","year":"2025","journal-title":"Measurement"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1177\/1475921717715240","article-title":"Impedance-based damage detection under noise and vibration effects","volume":"17","author":"Campeiro","year":"2018","journal-title":"Struct. Health Monit."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.isatra.2020.11.013","article-title":"A novel zero delay low pass filter: Application to precision positioning systems","volume":"111","author":"Alizadeh","year":"2021","journal-title":"ISA Trans."},{"key":"ref_39","first-page":"28","article-title":"Ensemble hybrid machine learning methods for gully erosion susceptibility mapping: K-fold cross validation approach","volume":"3","author":"Roy","year":"2022","journal-title":"Artif. Intell. Geosci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"112568","DOI":"10.1016\/j.ymssp.2025.112568","article-title":"Feature selection for unsupervised defect detection of a wind turbine blade considering operational and environmental conditions","volume":"230","author":"Ashkarkalaei","year":"2025","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"111120","DOI":"10.1016\/j.ymssp.2024.111120","article-title":"A review on physics-informed data-driven remaining useful life prediction: Challenges and opportunities","volume":"209","author":"Li","year":"2024","journal-title":"Mech. Syst. Signal Process."}],"container-title":["Polymers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-4360\/18\/16\/1995\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:23:41Z","timestamp":1787027021000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-4360\/18\/16\/1995"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,16]]},"references-count":41,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["polym18161995"],"URL":"https:\/\/doi.org\/10.3390\/polym18161995","relation":{},"ISSN":["2073-4360"],"issn-type":[{"value":"2073-4360","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,16]]}}}