Structural Control & Health Monitoring最新文献

{"title":"Field Performance of a New Near-Field Rail Noise Absorber Implemented on Operational Tracks","authors":"Wai Kei Ao,&nbsp;Yuhang Lu,&nbsp;Yu-Ling Wang,&nbsp;Duo Zhang,&nbsp;Yunfan Yang,&nbsp;Yi-Qing Ni","doi":"10.1155/stc/3923698","DOIUrl":"https://doi.org/10.1155/stc/3923698","url":null,"abstract":"<p>This study details the design, numerical modelling and experimental validation of a novel near-field rail noise absorber (NRNA) intended to mitigate railway noise at its source. The evaluation employed a comprehensive methodology that combined finite element (FE) simulations, parameters’ optimal design via physics-informed neural network (PINN) surrogate, semianechoic chamber testing and full-scale field measurements to assess the absorber’s acoustic performance under both squealing and rolling noise scenarios. The FE model and parameters optimisation, based on the Johnson–Champoux–Allard (JCA) framework, effectively simulated near-field acoustic propagation and energy dissipation within the absorber, demonstrating strong concordance with experimental data. Semianechoic chamber experiments verified substantial attenuation in downstream sound pressure levels. Full-scale deployment along an operational urban railway line further substantiated the NRNA’s effectiveness under real-world service conditions. Data from a noise-sensitive receiver (NSR) revealed a clear relationship between train speed and radiated A-weighted sound pressure level, thereby validating the absorber’s practical utility. To characterise this relationship, linear regression, bidirectional long short-term memory (BiLSTM) and temporal convolutional neural network (TCNN) models were developed. The findings indicate that the NRNA offers an efficient, low-profile and scalable approach for near-field noise reduction in rail systems. The integration of numerical, experimental and data-driven techniques provides a robust framework for optimisation and engineering applications, supporting the advancement of quieter and more sustainable urban railway systems.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/3923698","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Graphene-Based Sensing for Structural Health Monitoring","authors":"Anat Menkin,&nbsp;Shani Ligati Schleifer,&nbsp;Noam Avtalion,&nbsp;Assaf Ya’akobovitz,&nbsp;Oren Regev","doi":"10.1155/stc/1933977","DOIUrl":"https://doi.org/10.1155/stc/1933977","url":null,"abstract":"<p>The integration of graphene nanoplatelets (GnPs) into cementitious construction yields a multifunctional real-time structural health monitoring (SHM) material for self-sensing. Above a percolation threshold of 3.2 wt% GnP, a conductive network forms in the cementitious material, leading to a sharp decrease in electrical resistance (∼ 4 orders of magnitude) and a sensitive and stable sensory response under cyclic compressive loading. The values of fractional change in resistance (FCR) upon compression were found to be linearly correlated with stress, reaching 40% at a maximum stress of 11 MPa. The correlation between stress and resistance was calibrated using the stress sensitivity factor (SF = ((FCR)/( <i>σ</i>))·100). The obtained SF was 3.6%/MPa, which is a significant improvement over previously reported studies (twice as high). The reproducible response was independent of the load direction with respect to orientation of the electrodes (parallel or perpendicular to the force direction). Currently used sensors mostly provide global information on large structures and are based on complex, fragile external devices such as cameras, lasers, or ultrasound sensors, which require extensive data analysis and operational overhead. In contrast, the findings of this study highlight the potential of the cementitious composite itself as a scalable, durable, and local sensor for SHM applications in concrete elements.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/1933977","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Active Control of Base-Isolated Buildings for Earthquake Mitigation Based on Predictive Control Approach","authors":"Samira Narimanpoor,&nbsp;Farhad Bayat,&nbsp;Quanmin Zhu","doi":"10.1155/stc/1120393","DOIUrl":"https://doi.org/10.1155/stc/1120393","url":null,"abstract":"<p>The practical application of active control for base-isolated structures must contend with a host of real-world challenges, including the physical limits of isolator displacement and actuator force, mandatory building separation distances, and sensor noise and parameter uncertainties. While passive and semiactive systems remain the preferred solutions for most seismic applications, active control offers a unique advantage for near-fault regions where long-period ground motions can drive passive isolators beyond their displacement capacity. This study develops a model predictive controller (MPC) specifically for such demanding scenarios. Our MPC design carefully balances tracking error against control effort and explicitly enforces hard constraints on the control force and its rate of change associated with the real hydraulic actuators. While traditional tracking MPC is employed here, the framework is positioned as a baseline for future economic MPC (EMPC) formulations that directly optimize actuator energy consumption and displacement limits. The prediction horizon is also tuned to the structure’s dominant dynamics to ensure reliable performance. We test our controller on a 10-degree-of-freedom (DOF) base-isolated building model subjected to three historic earthquakes: Chi-Chi, Shinjuku, and Kobe. The simulation framework, built in MATLAB/MPC3S, is made robust by incorporating a second-order filter to clean sensor noise and by modeling uncertainties in stiffness and damping. The results are clear: Our proposed MPC substantially outperforms an equivalent input disturbance (EID) controller. It achieves dramatic reductions in both floor displacements and velocities, strengthens overall stability, and critically maintains strict adherence to all operational limits, even when faced with significant noise and parameter uncertainties.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/1120393","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Modal Identification via Progressive OPTICS Mode Clustering and Mutually Exclusive True Mode Screening","authors":"Xiaoshun Wu,&nbsp;Minsai Zeng,&nbsp;Jun Ma,&nbsp;Cui Li,&nbsp;Muhao Chen","doi":"10.1155/stc/1241918","DOIUrl":"https://doi.org/10.1155/stc/1241918","url":null,"abstract":"<p>This paper presents an automated method for modal identification for potential applications in long-term structural health monitoring, combining progressive OPTICS (a clustering algorithm named “ordering points to identify the clustering structure”) mode clustering and mutually exclusive true mode screening strategies. Candidate modes are first extracted using a covariance-driven stochastic subspace identification algorithm, followed by stabilization diagram cleaning that integrates hard validation criteria (damping ratio and complex conjugate pairs) and soft validation criteria (mean phase deviation and modal energy level). Mode clustering proceeds in two stages: an initial OPTICS clustering based on frequency-distance constructs stabilization axes, and a final OPTICS clustering based on mode-shape distance refines the results. To address variations in frequency and mode-shape distributions, a sliding-window median threshold strategy and an adaptive bimodal threshold detection strategy are applied sequentially to refine the two OPTICS algorithms. The median damping-ratio method is then used to extract the representative mode for each cluster. A novel mutually exclusive screening strategy distinguishes true from false clusters, which groups clusters with high mode shape similarity into the same set. Using features such as mode quantity, mean phase deviation, and modal energy level, the k-means clustering algorithm is applied iteratively within each set to eliminate false clusters, with the representative modes of true clusters serving as the final results. Both numerical and benchmark analyses demonstrate that the method offers several advantages, including the preservation of weak modes, enhanced aggregation of modal parameters, and robustness in key parameters, such as the estimated number of structural modes and the maximum model order. These characteristics reduce modal omission and false modes in practical applications.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/1241918","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Dual-Stage Stacking–LSTM Prediction Model for Dam Seepage Pressure Based on Feature Selection and Residual-Error Correction Strategy","authors":"Lifu Xu,&nbsp;Xinqiang Niu,&nbsp;Haoran Wang,&nbsp;Tianyou Yan,&nbsp;Mingxia Xie,&nbsp;Yantao Zhu","doi":"10.1155/stc/8217982","DOIUrl":"https://doi.org/10.1155/stc/8217982","url":null,"abstract":"<p>Accurate prediction of seepage pressure is paramount for the structural health monitoring and safety assessment of dams. However, conventional data–driven models frequently exhibit limited generalization and suffer from severe multicollinearity. To overcome these limitations, this study proposes a robust dual-stage seepage pressure prediction framework incorporating feature selection and residual-error correction (REC) strategy. Specifically, a feature selection method combining the variance inflation factor (VIF) and greedy algorithm is developed to eliminate multicollinearity among environmental factors. Subsequently, the stacking ensemble model is employed to capture the complex nonlinear relationships between the optimized environmental factors and seepage pressure. A long short-term memory (LSTM) network is then utilized to extract latent temporal dependencies embedded within the residual sequence of the preliminary prediction. Finally, the corrected residual sequence is superimposed onto the preliminary prediction to generate the final modeling results. The effectiveness of the proposed model is validated using the measured monitoring data from a high arch dam. Results demonstrate that the stacking model substantially outperforms conventional single models and the REC strategy effectively extracts the valuable information within residual sequences, significantly refining the preliminary prediction. Consequently, the proposed dual-stage model tracks the variations of seepage pressure with superior accuracy, providing a reliable tool for seepage pressure prediction and dam safety assessment.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/8217982","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Acoustic Emission–Based High-Velocity Impact Detection in Whipple Shielding Structures","authors":"Samet Ceri,&nbsp;Zahra Sharif Khodaei","doi":"10.1155/stc/7847689","DOIUrl":"https://doi.org/10.1155/stc/7847689","url":null,"abstract":"<p>This study presents a structural health monitoring (SHM) methodology for impact detection and kinetic energy characterization in Whipple shields using acoustic emission (AE) sensing integrated with hybrid finite element–smoothed particle hydrodynamics (FEM–SPH) simulations. The subhypervelocity impact regime (150–700 m/s) was selected as an intermediate validation stage for future extension toward hypervelocity impact applications. Controlled impact experiments on aluminium–composite Whipple shield configurations were performed to characterize AE wave propagation and to validate the numerical framework. The validated FEM–SPH model successfully reproduced both impact damage and AE signal characteristics, with numerical predictions agreeing with experimental measurements within 6.78%. Four physically meaningful AE descriptors—signal amplitude, AE energy, signal duration, and time of interlayer flight (TIF)—were systematically analysed through power-law regression, demonstrating strong correlations with projectile kinetic energy (<i>R</i>² up to 0.987). The results reveal a distinct transition near the ballistic threshold (approximately 60 J or 250 m/s), where AE signatures shift from flexural-wave-dominated responses associated with nonpenetrating impacts to extensional-wave-dominated responses following rear-wall perforation. Building upon these relationships, a multivariable empirical regression model was developed to estimate projectile kinetic energy directly from AE measurements. The proposed model achieved a coefficient of determination of <i>R</i>² = 0.913, a mean absolute percentage error of 8.26%, a maximum validation error of 12.86%, and a penetration classification accuracy of 90.9% using a 60 J ballistic transition, with 9-fold cross-validation confirming the robustness of the predictive framework. The proposed methodology demonstrates that combining AE sensing with validated numerical modelling provides an effective and computationally efficient approach for impact localization, kinetic energy estimation and penetration assessment, offering a practical SHM framework for future spacecraft shielding systems.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/7847689","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-Driven Prognostic Framework for Chloride-Induced Corrosion in Aging Concrete Structures: Integrating Minimal Field Data With Mix-Design Inference","authors":"Jiyoung Min,&nbsp;Woldeamanuel Minwuye Mesfin,&nbsp;Jaehwan Kim,&nbsp;Ki-Tae Park,&nbsp;Gun Kim,&nbsp;Hyeong-Ki Kim","doi":"10.1155/stc/7479965","DOIUrl":"https://doi.org/10.1155/stc/7479965","url":null,"abstract":"<p>Accurate evaluation of chloride-induced corrosion in reinforced concrete (RC) structures is essential for effective maintenance, yet conventional diagnostic approaches require full-depth coring and laboratory testing to determine chloride profiles and diffusion coefficients. These procedures are costly and invasive for large-scale routine inspection. This study proposes a data-driven framework that enables corrosion initiation assessment using only surface chloride measurements combined with machine-learning (ML)–based estimation of chloride diffusion coefficients. To address the large uncertainty associated with unknown mix proportions in existing structures, regional mix-design statistics were incorporated to constrain ML model inputs and reduce prediction variability. The resulting diffusion coefficient distributions were then integrated into a probabilistic chloride ingress model based on <i>fib</i> Model Code 2010, allowing explicit propagation of uncertainties in material properties, cover depth, and environmental exposure through Monte Carlo simulation. Field validation was conducted using concrete cores extracted from roadside barriers and bridge components located in inland and coastal environments. Comparison between predicted and measured chloride profiles demonstrated that the proposed approach successfully captures the range and depth of chloride penetration observed in practice. The results indicate that this minimally invasive method can support early-stage depassivation probability screening, optimize locations requiring detailed testing, and enhance decision-making in infrastructure asset management. It should be noted that this approach assesses the risk of corrosion initiation (i.e., depassivation onset) and not the actual condition or section loss of the embedded reinforcement.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/7479965","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Probabilistic Framework for Stress Resultant Reconstruction From Strain-Gauge Measurements on Wind Turbine Towers","authors":"Marko Kinne,&nbsp;Ronald Schneider,&nbsp;Sylvia Keßler","doi":"10.1155/stc/1460331","DOIUrl":"https://doi.org/10.1155/stc/1460331","url":null,"abstract":"<p>Structural health monitoring systems are increasingly used in wind turbines to estimate cyclic loading effects on towers and foundations for fatigue assessment, inspection planning, and lifetime-extension decisions. This paper presents a probabilistic framework for reconstruction of stress resultants from strain-gauge measurements at instrumented cross-sections of wind turbine towers. The novelty of the methodology lies in combining a time-dependent probabilistic strain-gauge measurement model, in which selected error sources and influence quantities are represented as stochastic processes, with Bayesian calibration from laboratory experiments under prescribed strain and temperature conditions, and subsequent propagation of the calibrated measurement uncertainty through a probabilistic mechanical reconstruction model. The methodology, therefore, accounts consistently for both measurement and mechanical-model uncertainty in the reconstruction of stress resultants. Its application is demonstrated in a case study with sensitivity analyses identifying the dominant contributors to uncertainty in both the strain measurements and the reconstructed stress resultants. By providing probabilistic estimates of reconstructed load effects rather than deterministic point estimates alone, the framework supports uncertainty-informed assessment of fatigue demand and more robust decisions regarding inspection and lifetime extension.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/1460331","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comprehensive Review of Virtual Trial Assembly for Large-Scale Structures Based on 3D Laser Scanning Point Clouds","authors":"Lidu Zhao,&nbsp;Junqiang Wang,&nbsp;Yin Zhou,&nbsp;Hong Zhang,&nbsp;Jianting Zhou,&nbsp;Shuangcheng Zhang","doi":"10.1155/stc/3087872","DOIUrl":"https://doi.org/10.1155/stc/3087872","url":null,"abstract":"<p>Virtual trial assembly (VTA) is emerging as a digital alternative to conventional physical trial assembly by leveraging 3D laser scanning and computer-aided modeling to verify the fit-up of large-scale structures. This paper comprehensively reviews the VTA pipeline across three core stages: (1) multisource data acquisition combining terrestrial laser scanning, handheld scanning, and industrial photogrammetry for global coordinate system establishment; (2) point cloud processing involving preprocessing, deep learning–based semantic segmentation, and assembly-critical feature extraction; and (3) virtual assembly including constrained registration and kinematic chain modeling for multisegment alignment, together with multiobjective optimization for rectification and physics-informed compensation as an emerging direction not yet realized in the cases reviewed. We propose a novel four-level capability maturity model charting VTA’s evolution from offline geometric inspection toward real-time closed-loop digital fabrication; the reviewed cases populate the lower levels, while the upper two levels are defined as forward-looking targets not yet fully demonstrated in practice. The review further identifies technical bottlenecks, including precision-utility trade-offs, real-time computational constraints, geometry-mechanics decoupling, and the absence of standardized validation protocols. Future research directions are outlined to provide a clear roadmap for advancing intelligent and automated construction.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/3087872","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148849069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simulation Analysis and Experimental Study on Multisource Coupled Noise in the Cab of Crawler Combined Harvester Based on Dual-Path Indirect Excitation Noise Transfer Function","authors":"Kuizhou Ji,&nbsp;Yaoming Li,&nbsp;Yanbin Liu,&nbsp;Hanhao Wang,&nbsp;Junhui Cheng","doi":"10.1155/stc/6755649","DOIUrl":"https://doi.org/10.1155/stc/6755649","url":null,"abstract":"<p>This paper establishes a dual-path indirect excitation noise transfer function (NTF) model for the cab of a crawler-type combine harvester under multisource excitation, covering two independent transfer chains: engine–chassis frame–cab and threshing and cleaning frame–cab. Different from single-path traditional noise analysis models for agricultural machinery, the proposed model realizes separate quantification of two structural transfer paths and introduces modal participation factor with modal effective mass calculation, greatly improving the capability to identify dominant resonant structures and vibration directions. It achieves coupled analysis of four excitation sources: engine, threshing drum, vibrating sieve and cutter bar. Finite element simulation and LMS testing show that harmonic resonance at the fundamental frequency of the engine (approximately 40 Hz) and its harmonics, together with harmonics of the fundamental frequencies of the threshing drum, vibrating sieve and cutter bar, is the main cause of excessive cab noise. Mounting points A₁/B₁ of the threshing drum, point D₁ on the right side of the header, and point F₁ on the right side of the vibrating sieve are high-risk excitation sources and exhibit pronounced direction-dominant characteristics. Simulations and tests agree closely in the 20–100 Hz frequency range: The average error of core frequencies is ≤ 9%, sound pressure level error ≤ 15%, and coherence function greater than 0.8, which validates the effectiveness of the model. The study reveals that the asymmetric frame leads to a more significant resonance amplification effect at the right-side mounting points and proposes an optimization strategy combining directional vibration isolation and harmonic control. This dual-path indirect excitation NTF analysis method provides theoretical support and engineering reference for the forward design of cab NVH in combine harvesters.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2026 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/6755649","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148849070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}