Structural Control & Health Monitoring最新文献

{"title":"An Efficient PINN–Based Calibration Method for Mesoscale Peridynamic Concrete Models","authors":"Zhe Lin,&nbsp;Eric Gu,&nbsp;Surong Huang,&nbsp;Lei Wang","doi":"10.1155/stc/6641629","DOIUrl":"https://doi.org/10.1155/stc/6641629","url":null,"abstract":"<div>\u0000 <p>Mesoscale models are crucial for the refined analysis of material damage behaviors. However, it remains a challenging task to calibrate a mesoscale model so as to accurately simulate the mechanical behaviors (MBs) of macroscale structural components. The models may be nonlinear, involve numerous material parameters (MPs), and be large-scale. In addition, solutions to inverse problems may lack accuracy or be nonunique. A recent emerging method, physics-informed neural network (PINN), combines deep learning with physical laws to solve complex problems and significantly reduce computational costs. This paper presents an effective PINN approach for mesoscale model calibration. The approach establishes a relationship between the MPs of a mesoscale model and the MBs of structural components using PINN, with constraints based on known physical relationships. Both forward PINN (MPs as inputs and MBs as outputs) and reverse PINN (swapping inputs and outputs) models are used. Calibration is achieved efficiently by combining the forward PINN model with an optimization algorithm or directly using the reverse PINN model. Validation is performed using a mesoscale concrete model in peridynamics (PDs). The relationship between the elastic modulus of bonds in PD and MBs of components is constrained by physical laws. The datasets are generated through OpenSees analysis. The PINN method demonstrates its effectiveness, particularly with the reverse model, which is both efficient and accurate.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/6641629","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143120182","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":"Modal and Wave Propagation Analysis of Vibration Tests on a Laboratory Building Model Before and After Damage","authors":"Chun-Man Liao","doi":"10.1155/stc/3453150","DOIUrl":"https://doi.org/10.1155/stc/3453150","url":null,"abstract":"<div>\u0000 <p>Weakened structural stiffness is often a consequence of building damage, particularly after severe events such as earthquakes, where compromised structural performance can pose significant risks. To prevent immediate structural failure, an early warning system is essential, which requires inspection of local components. This research aims to achieve that by exploring the wave propagation analysis method, specifically seismic interferometry. Previous studies have applied this method to building structures, treating them as homogeneous layers of grouped floors. By analyzing the wave travel time along the height of these layers, the fundamental period of the building was estimated. However, this approach did not account for local damage or the variability of structural components, similar to the limitations of vibration-based damage detection methods, which mainly identify global changes. Thus, the goal of this paper is to improve structural health monitoring by examining the sensitivity of wave screening, bridging the gap between nondestructive testing and vibration-based damage detection. A half-scale, seven-story building model, characterized by vertical stiffness irregularity and transverse plan asymmetry, was tested in a laboratory setting. Two vertical sensor arrays were placed near corner columns of different sizes, representing both strong and weak structural areas. These arrays recorded floor accelerations in three directions. The study confirmed the effectiveness of wave propagation analysis for detecting damage along the sensor arrays before and after the earthquake. A transmissibility damage indicator was used to correlate changes in wave velocity, providing a quantitative assessment of damage levels along the wave propagation path.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/3453150","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143119061","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":"Automatic Water Seepage Depth Detection in Concrete Structures Using Percussion Method Combined With Deep Learning Network","authors":"Wenjie Huang,&nbsp;Kai Zhou,&nbsp;Jicheng Zhang,&nbsp;Longguang Peng,&nbsp;Guofeng Du,&nbsp;Zezhong Zheng","doi":"10.1155/stc/7386022","DOIUrl":"https://doi.org/10.1155/stc/7386022","url":null,"abstract":"<div>\u0000 <p>Water seepage in concrete can significantly degrade the durability of hydraulic concrete structures. Therefore, this paper introduces a new method that combines the percussion method with deep learning techniques to detect the depth of water seepage in concrete structures. Initially, percussion sound signals were collected for different water seepage depths. Then, the proposed one-dimensional convolutional bidirectional gated recurrent unit (BiGRU) network with wide first-layer kernel (1D-WCBGRU) classifies the percussion sound signals for different water seepage depths. The 1D-WCBGRU uses a wide first convolutional kernel to extract features directly from the original percussion signals without the need to extract features manually. Subsequently, the BiGRU is utilized to capture long short-term information from the data, thereby enhancing feature separability and improving the classification accuracy and robustness of the model. Experiments confirm that the 1D-WCBGRU exhibits excellent performance in the seepage depth detection task compared to traditional learning algorithms.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/7386022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143117478","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":"Assessing Feasibility and Performance of Ultrasonic Guided Wave–Based Numerical–Experimental Methodology for Debonding Monitoring of Adhesive Joints: Application to an Internal Beam of a Battery Box","authors":"Andrea Calvo-Echenique,&nbsp;Mario Sánchez,&nbsp;Emmanuel Duvivier,&nbsp;Clara Valero,&nbsp;Agustín Chiminelli","doi":"10.1155/stc/1711913","DOIUrl":"https://doi.org/10.1155/stc/1711913","url":null,"abstract":"<div>\u0000 <p>Multimaterial solutions that combine adhesively bonded composite and metallic parts are being widely proposed as lightweighting strategies to reduce environmental impact. However, the introduction of adhesive interphases in components subjected to fatigue loads is a major concern in terms of durability, reliability and maintainability. Structural health monitoring (SHM) techniques can play a key role in providing structures with self-sensing capabilities. Although the use of ultrasonic guided wave (UGW) monitoring for predicting the damage of in-service adhesive joints has been proved feasible, several challenges remain, including the generation of large and high-quality data sets and the scalability of damage detection algorithms for real-world use cases. After a wide literature review of available algorithms and simulation techniques, the simplest yet accurate methods have been selected to build a methodology that may eventually be fostered with more complex models. In this work, a numerical–experimental integrative methodology is proposed to train predictive algorithms minimizing the need for extensive experimental campaigns, by creating synthetic data sets through physics-based simulation models. Although several features have been detected as damage-sensitive, simple regression models using the root-mean-square density (RMSD) have been trained and validated as damage indicators. The feasibility of this approach has been proven in a real subcomponent with an error below 2% in the debonding length prediction, calculated as the ratio of the Euclidean distance between actual debonding and predicted debonding to the total inspection length.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/1711913","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143115510","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":"Dynamic Vibration Characteristics and Mitigation of the Stress-Ribbon Bridge by Using a Rail-Damper System","authors":"Kun Xu,&nbsp;HanShuo Wang,&nbsp;Meng Wang,&nbsp;Bin Liu,&nbsp;Satish Nagarajaiah,&nbsp;Qiang Han","doi":"10.1155/stc/3296513","DOIUrl":"https://doi.org/10.1155/stc/3296513","url":null,"abstract":"<div>\u0000 <p>Due to its simple and beautiful architectural appearance, the stress-ribbon bridge (SRB) has been gradually built around the world as a pedestrian or traffic bridge. However, as characterized by low bending stiffness and low damping ratio features, SRB is prone to the dynamic effects of external excitations, such as pedestrians, vehicles, and/or winds. To control the vertical vibration of the SRB, a rail-damper system is proposed in this study. In the proposed scheme, the rotation of the handrails triggered by the flexural deformation of the SRB is utilized to drive the viscous dampers installed between the adjacent handrails. The governing equations of the proposed control system are established. The key design parameters and their influences on the dynamic properties of the control system are systematically investigated. The control performances of the proposed rail-damper system are further investigated through an SRB numerical model subjected to pedestrian excitations. It is discovered that the rail-damper system can offer considerable supplemental damping to the structural modes through reasonable design, achieving satisfactory control performances. To gain the excellent effect of the proposed rail-damper system in real applications, a nondimensional rail stiffness of no less than 1000 is recommended, and the stiffness of the damper should be controlled as small as possible.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/3296513","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143112516","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":"Performance of Real-Time Hybrid Simulation for Hunting Dampers of High-Speed Trains","authors":"Zhen Wang,&nbsp;Jiajun Xiao,&nbsp;Baoan Zhang,&nbsp;Ge Yang,&nbsp;Bin Wu,&nbsp;Xuejun Jia","doi":"10.1155/stc/4984025","DOIUrl":"https://doi.org/10.1155/stc/4984025","url":null,"abstract":"<div>\u0000 <p>One favorable solution to the issue of hunting instability of high-speed trains is to install hunting dampers. However, the nonlinearity of dampers and their interaction with a train present significant challenges in accurately analyzing the dynamic behaviors of both dampers and trains. To address these challenges, we present and investigate a real-time hybrid simulation (RTHS) for hunting dampers of high-speed trains and propose an improved two-stage adaptive time-delay compensation method to resolve its demanding delay issue. This innovative approach combines a numerical train model with a full-scale physical hunting damper, providing a versatile method for simulating and analyzing various dynamic behaviors. The train model incorporates 17 degrees of freedom and accounts for the nonlinear wheel–rail contact relationship to more faithfully represent the dynamic response of the train. A virtual RTHS platform with a loading system model has been developed. Both numerical simulations on this platform and real tests are conducted using the RTHS approach. Results demonstrate that time delays can reduce the hunting stability of a high-speed train, and the improved two-stage adaptive time-delay compensation method outperforms other comparative methods. This research reveals the feasibility and efficacy of the RTHS method for hunting dampers of high-speed trains.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2024 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/4984025","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143121168","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":"Damage Detection in Bridge via Adversarial-Based Transfer Learning","authors":"Zhenhua Nie,&nbsp;Shenshen Xu,&nbsp;Kaijian Chen,&nbsp;Lianli Xu,&nbsp;Yizhou Lin,&nbsp;Hongwei Ma","doi":"10.1155/stc/5548218","DOIUrl":"https://doi.org/10.1155/stc/5548218","url":null,"abstract":"<div>\u0000 <p>A novel structural damage detection (SDD) method is proposed in this work, which is based on adversarial-based transfer learning to achieve the cross-domain information transfer of damage locations between numerical simulations and real bridge structures. Although the advancement of numerical modeling technology makes it accessible for relatively accurate finite element (FE) models, it is still difficult to meet the needs of practical engineering. The idea of adversarial training is introduced to enable the traditional feature extraction network to obtain the domain independent features between the numerical simulation and the real bridge structure. The dynamic response data from the numerical simulations are labeled with damage, while those from the real structure are unlabeled. To verify the effectiveness of the proposed method, we established a FE model of a simply support beam and regarded it as the benchmark model, and the target model with discrepancies from the benchmark model is obtained by quantitatively increasing the uncertainties. The results of the simulation show that the proposed method can overcome the discrepancy caused by uncertainty to a certain extent compared with the traditional method and obtain a high damage localization accuracy on the target model. In the laboratory experiment, the proposed method still achieves promising results. The primary contributions of this work are twofold: first, it delves deeper into the effectiveness of adversarial training for extracting domain-invariant features, which are crucial for structural damage identification. Second, it provides a quantitative assessment of the performance degradation of traditional methods due to modeling errors and uncertainty. Additionally, it demonstrates the significant performance enhancement achieved by the proposed method.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2024 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/5548218","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143121170","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":"Detection, Evaluation, and Reinforcement of Anchor Bolt Foundation Damage After Wind Turbine Tower Collapse","authors":"Weirong Lv,&nbsp;Dejia Gan,&nbsp;Shuai Yao,&nbsp;Jingjing Qi,&nbsp;Beirong Lu,&nbsp;Jiaqiang Wu","doi":"10.1155/stc/4756772","DOIUrl":"https://doi.org/10.1155/stc/4756772","url":null,"abstract":"<div>\u0000 <p>This paper presents an on-site anchor bolt tension-force versus deformation detection method, which enables the assessment and evaluation of the extent of damage in the anchored zone of anchor bolts buried deep within concrete. In addition, considering different toppling modes and their respective force mechanisms, the paper proposes using the entire cross-sectional plastic bending moment with a 0.4 impact coefficient as the actual effective bending moment for the anchor bolt foundation. This is employed to calculate the tensile forces in the anchor bolts and the local compressive bearing capacity of the concrete above the lower anchor plate. The computational results indicate that the plastic damage in the anchored zone of problematic anchor bolts is primarily caused by the crushing of concrete above the lower anchor plate rather than axial plastic deformation of the anchor bolts. Accordingly, targeted reinforcement schemes for repairing lower anchor plate deformations and replacing locally crushed concrete are proposed and successfully applied, providing practical reference for engineering applications.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2024 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/4756772","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143118782","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":"Three Optimization Methods for Preprocessing Dam Safety Monitoring Data Using Machine Learning","authors":"Zihan Jiang,&nbsp;Hao Gu,&nbsp;Yue Fang,&nbsp;Chenfei Shao,&nbsp;Xi Lu,&nbsp;Wenhan Cao,&nbsp;Jiayi Wang,&nbsp;Yan Wu,&nbsp;Mingyuan Zhu","doi":"10.1155/stc/4385464","DOIUrl":"https://doi.org/10.1155/stc/4385464","url":null,"abstract":"<div>\u0000 <p>The sensor-based dam health monitoring (DHM) systems of concrete-faced rockfill dam (CFRD) are easily affected by environmental factors, which inevitably causes sensor fault, and the measured value of its effect quantities is nonlinear and unstable. The application of machine learning in the preprocessing of dam safety monitoring data is very extensive, mainly including two parts: gross error elimination and missing data completion. In this paper, support vector regression (SVR), a typical machine learning algorithm, is chosen to accomplish these two tasks, while suggesting possible optimizations in different situations of hydraulic monitoring, including optimization of parameters in SVR using the population algorithm sparrow search algorithm (SSA); optimization of the pattern of gross error discriminant using the minimum covariance determinant (MCD) algorithm; and the hierarchical clustering on principal components (HCPC) algorithm to optimize the selection method of spatial measurement points when completing a segment of missing data. The results show that the optimized SVR method has greater accuracy in both gross error elimination and the completion of individual missing data or a segment of missing data for DHM systems, which is applicable to measured data of CFRD. These optimization methods can also be extended to other engineering applications.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2024 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/4385464","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143118589","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":"Generative Adversarial Networks for Improved Model Training in the Context of the Digital Twin","authors":"María Megía,&nbsp;Francisco Javier Melero,&nbsp;Manuel Chiachío,&nbsp;Juan Chiachío","doi":"10.1155/stc/9997872","DOIUrl":"https://doi.org/10.1155/stc/9997872","url":null,"abstract":"<div>\u0000 <p>Digital twins (DTs) have revolutionised digitalisation practices across various domains, including the Architecture, Engineering, Construction and Operations (AECO) sector. However, DTs often face challenges related to data scarcity, especially in AECO, where tests are costly and difficult to scale. Historical data in this domain are often limited, unstructured and lack interoperability standards. Data scarcity directly affects the accuracy and reliability of the DT models and their decision-making capabilities. To address these challenges, classical methods are used to produce synthetic data based on predefined statistical distributions, which are barely scalable to unpredictable scenarios and prone to overfitting. Alternately, this work presents a novel comprehensive approach that covers every aspect from synthetic data generation to training and testing of these data on the system’s models. This strategy not only delivers high-quality data that meets the model’s requirements in terms of diversity, complexity and class balance, but also provides the diagnostic and prognostic capabilities of the DT of the system through its trained models. State-of-the-art techniques including generative adversarial networks (GANs), specifically Wasserstein generative adversarial networks with gradient penalty (WGAN-GP), and convolutional neural networks (CNNs) are employed in this novel pervasive approach, participating in the same architecture for generative, diagnostic and prognostic purposes. GANs enable data augmentation and reconstruction, while CNNs excel in spatial pattern recognition tasks. The proposed framework is demonstrated through an experimental case study on damage diagnostics and prognostics of a laboratory-scale metallic tower, where synthetic datasets are generated to supplement limited health monitoring data. The results showcase the effectiveness of the generated data for damage detection, prognostics and operational decision-making within the DT context. The presented method contributes to overcoming data scarcity challenges and improving the accuracy of DT models in the AECO sector. The article concludes with discussions on the application of the results and their implications for decision-making within the DT framework.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2024 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/9997872","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142868969","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}