Structural Control & Health Monitoring最新文献

{"title":"Temperature Monitoring of Mass Concrete Structure Using Wireless Sensing System","authors":"Tengyi Wang,&nbsp;Dan Li,&nbsp;Jiajun Zhou,&nbsp;Jian Zhang","doi":"10.1155/stc/7847074","DOIUrl":"https://doi.org/10.1155/stc/7847074","url":null,"abstract":"<div>\u0000 <p>Rapid temperature changes during the early stages of mass concrete construction can cause thermal cracking, which negatively impacts structural integrity and longevity. Reliable temperature monitoring is essential for effective crack control. Traditional methods, such as manual inspections and wired structural health monitoring systems, are often hindered by high labor costs and maintenance challenges, limiting their effectiveness for large-scale applications. This paper presents the development of a wireless temperature sensing system designed to overcome these challenges. Both the hardware and software architectures of the wireless sensing unit are detailed. The system is characterized by easy deployment, low power consumption, and long-distance wireless communication, making it suitable for large-scale monitoring of concrete structures. To address data anomalies caused by wireless transmission failures, the sensing system includes robust data anomaly detection and recovery algorithms, ensuring reliable measurements. A prototype system was fabricated and field-tested on a massive concrete structure, validating the effectiveness of the sensing system. The experimental results demonstrate that the wireless temperature sensing system can reliably monitor the temperature distribution of mass concrete structures during construction, providing measurement data for preventing thermal cracking and ensuring structural integrity.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/7847074","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143770293","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":"Structural Damage Identification Based on Transfer Learning and Power Spectral Density","authors":"Youliang Fang,&nbsp;Chanpeng Li,&nbsp;Jiaxin Li","doi":"10.1155/stc/5224063","DOIUrl":"https://doi.org/10.1155/stc/5224063","url":null,"abstract":"<div>\u0000 <p>This paper proposes a novel method for structural damage identification that integrates the power spectral density (PSD) of structural acceleration responses with densely connected convolutional networks (DenseNet). The method transforms the training object of the DenseNet into a numerical matrix (PSD matrix) for structural damage identification. Leveraging transfer learning, the DenseNet models are initially trained on simulated data and further fine-tuned using experimental data to enhance robustness and generalization. Results demonstrate that frequency-domain signals processed by PSD significantly enhance model performance, achieving lower mean squared error (MSE), higher Pearson’s correlation coefficient (<i>R</i> value), and reduced mean absolute error (MAE) compared to time-domain signals. The effectiveness of this method was verified on a six-story frame structure. This study underscores the efficacy of transfer learning in bridging the gap between simulated and real-world data, thereby facilitating effective structural health monitoring and damage identification.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/5224063","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143770439","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":"Generalized Gaussian Distribution Refined Composite Multiscale Fluctuation Dispersion Entropy and Its Application in Fault Diagnosis of Switch Machine","authors":"Deqiang He,&nbsp;Jinxin Wu,&nbsp;Yingqian Sun,&nbsp;Zhenzhen Jin","doi":"10.1155/stc/1806458","DOIUrl":"https://doi.org/10.1155/stc/1806458","url":null,"abstract":"<div>\u0000 <p>The switch machine (SM) is an important device for turnout conversion, which is of great significance to ensure the safety of train operations. Refined composite multiscale dispersion entropy (RCMDE) is a formidable nonlinear characterization tool for time series signals, which has been applied to the fault diagnosis (FD) of switch machines. In fact, the lack of nonlinear mapping ability of RCMDE and the inability to evaluate the volatility of the SM signal affect its ability to extract features. To overcome its inherent drawbacks, a generalized Gaussian distribution refined composite multiscale fluctuation dispersion entropy (GGRCMFDE) is proposed to measure the complexity of the SM signal. In GGRCMFDE, first, the nonlinear mapping ability of the algorithm is improved by replacing the normal cumulative distribution function (NCDF) with the generalized Gaussian distribution (GGD). The fluctuation theory is introduced to evaluate the fluctuation of the signal to better adapt to the phenomenon of nonperiodic fluctuation of the signal when the SM fails. Through the above improvement, the feature extraction capability of the algorithm is comprehensively enhanced. Second, an FD method for the SM is used by combining the fault features extracted by GGRCMFDE with the support vector machine (SVM) for fault classification. Finally, the algorithm’s performance is guaranteed by improving dung beetle optimization (IDBO) algorithm, and the superiority of the diagnosis method is improved by using IDBO to optimize SVM; we name this method GGRCMFE–IDBO–SVM. It is verified by the actual operation scene experiment of the switch machines. The experiment shows that compared to the other algorithms, the FD impact of GGRCMFE–IDBO–SVM is significant, and a taller fault identification precision can be obtained.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/1806458","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143770072","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":"AT-LSTM-CUSUM Digital Intelligent Model for Seepage Safety Prediction of Concrete Dam","authors":"Xinyu Liang,&nbsp;Lizhi Zhang,&nbsp;Jiaqi Zhao","doi":"10.1155/stc/8518538","DOIUrl":"https://doi.org/10.1155/stc/8518538","url":null,"abstract":"<div>\u0000 <p>Seepage is one of the main causes of dam accidents, characterized by long latency periods and spatiotemporal randomness. In this study, an innovative combined algorithm model (AT-LSTM-CUSUM) is proposed to predict such leakage hazards. First, a long short-term memory (LSTM) network model based on an attention mechanism is established to focus on key influencing factors in predicting the time series data. Following the time series prediction, an improved Cumulative Sum (CUSUM) change-point monitoring algorithm is introduced. Within a sliding window period, a control function collects cumulative residuals, and a threshold test is performed to determine whether a potential hazard trend exists. Using monitoring data from a pressure measuring pipe in a concrete dam as the experimental subject, five related influencing factors were collected (upstream and downstream water levels, temperature, precipitation, and structural aging). These data were fed into the AT-LSTM model for iterative parameter tuning, yielding optimal prediction results. These results were compared with those of the LSTM, GRU, ARIMA, and Prophet models, validating the superior performance of the AT-LSTM model. In addition, by simulating the seepage hazard occurrence process, the change-point monitoring effectiveness of the improved CUSUM algorithm was tested. A parameter sensitivity analysis of the window period and threshold values revealed that the algorithm performed effectively in detecting seepage hazards. The innovative algorithm proposed in this paper exhibits strong early warning capabilities and holds significant value for dam safety monitoring and maintenance.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/8518538","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143762000","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":"Application of Intelligent Low-Cost Accelerometers for Bridge Monitoring With a Deep Learning Approach","authors":"Seyyedbehrad Emadi,&nbsp;Seyedmilad Komarizadehasl,&nbsp;Ye Xia","doi":"10.1155/stc/9835353","DOIUrl":"https://doi.org/10.1155/stc/9835353","url":null,"abstract":"<div>\u0000 <p>Despite the crucial role of structural health monitoring (SHM) in ensuring the integrity and safety of essential infrastructure, its adoption is often limited by the high costs of traditional sensors. This study introduces an innovative approach for creating intelligent, high-performing low-cost accelerometers using a deep learning framework rooted in long short–term memory (LSTM) neural networks. Initially, commercial sensors are temporarily installed alongside low-cost accelerometers on a bridge to facilitate the training process. Once the training is complete, the commercial sensors are removed, leaving the calibrated low-cost accelerometers permanently in place to perform continuous SHM tasks. In a case study, a bridge was equipped with an array of six low-cost and six commercial sensors. The efficacy of this innovative approach is corroborated through a comparative analysis of mode shapes and eigenfrequencies derived from both the low-cost and commercial sensors, as well as intelligent low-cost accelerometers.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/9835353","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143749324","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 Identification in Bridge Structures Based on a Novel Whale-Sand Cat Swarm Optimization Algorithm and an Improved Objective Function","authors":"Zhen Chen,&nbsp;Yikai Wang,&nbsp;Hui Wang,&nbsp;Shiming Liu,&nbsp;Tommy H. T. Chan","doi":"10.1155/stc/5587918","DOIUrl":"https://doi.org/10.1155/stc/5587918","url":null,"abstract":"<div>\u0000 <p>Structural damage identification (SDI) serves as an indirect approach that has the potential to meet real-time monitoring of structures. However, the identification accuracy and efficiency of some methods need to be improved, especially when there are some uncertain interfering factors or noise. This paper presents a new optimization algorithm and an improved objective function for inverse problems of SDI, offering an effective solution for bridge damage identification under uncertain noise interference and incomplete modal data. In this study, by hybridizing the whale optimization algorithm and the sand cat swarm optimization, a novel whale-sand cat swarm optimization (W-SCSO) method is proposed for SDI. The cubic chaotic mapping is introduced for initialization of the W-SCSO method, and then the lens opposition-based learning and the stochastic differential mutation are employed to enhance the search capability and convergence accuracy of the proposed algorithm. Besides, the mode shape curvature, the frequency change ratio, and the <i>L</i>_1/2 sparse regularization are used to improve the objective function. Four other existing state-of-the-art methods are used to verify the performance of the proposed W-SCSO method by the CEC2017 benchmark functions and a simply supported beam finite model. The comparative analysis highlights the feasibility and effectiveness of the proposed method in the considered cases. Moreover, an aluminum alloy simply supported beam was conducted for the SDI experiment to further prove the effectiveness of the improved method in practice. Simulation and experimental results show that the proposed method effectively locates and quantifies stiffness reduction in bridge structures, which maintains high accuracy in damage identification despite potential modal incompleteness and uncertain measurement noise interference.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/5587918","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143749515","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":"Study on Vibration Control of Wind Turbine With an Optimised Eddy Current Tuned Rolling Cylinder Damper","authors":"Zhenqing Liu,&nbsp;Chao Wang,&nbsp;Dongqin Zhang","doi":"10.1155/stc/6726023","DOIUrl":"https://doi.org/10.1155/stc/6726023","url":null,"abstract":"<div>\u0000 <p>The increasing scale and capacity of wind turbines, driven by advancements in wind power technology, present significant challenges in managing fatigue loads and vibrations. To address these challenges, we have designed an eddy current tuned rolling cylinder damper (ECTRCD) which incorporates eddy current–induced damping into the traditional tuned rolling cylinder damper (TRCD) and optimised the parameters including the radius ratio, mass ratio, frequency ratio and damping ratio. The optimal frequency ratio is observed between 0.9 and 1, with the damping ratio around 0.05 and the radius ration of 1/6. On the contrary, the optimal damping performance improves as the mass ratio increases. Additionally, the reduction ratio of the equivalent fatigue load is 17.7% by the ECTRCD with the optimal parameters (a radius ratio of 1/6, a mass ratio of 1.2%, a frequency ratio of 0.943 and a damping ratio of 0.059). Compared with the TRCD, the enhancement in this value is modest, with only a 1% improvement. Nevertheless, the displacement at the tower top in the side-to-side direction is significantly mitigated, particularly under high wind speeds. This finding underscores the potential of the ECTRCD as a promising alternative to conventional TRCDs, offering enhanced damping performance and improved structural stability.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/6726023","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143749517","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":"Screening for Anomalous Safety Condition Among Existing Buildings Using Explainable Machine Learning","authors":"Jie Liu,&nbsp;Guiwen Liu,&nbsp;Neng Wang,&nbsp;Yifei Jiang","doi":"10.1155/stc/6695396","DOIUrl":"https://doi.org/10.1155/stc/6695396","url":null,"abstract":"<div>\u0000 <p>To ensure a safe environment for occupants, evaluating the physical status and service performance of existing buildings is essential. However, large-scale building condition assessment usually relies on the expertise and judgment of inspectors, which can be costly and laborious due to unclear priorities, ambiguous procedures, and ineffective operations. To address these challenges, this study proposes an explainable machine learning-based screening model for the anomalous safety condition among existing buildings, narrowing down the scope of buildings requiring further and detailed inspection and monitoring. Initially, an imbalanced dataset of 18,090 survey reports of existing buildings of safe and unsafe labels is collected. Then, the synthetic minority oversampling technique (SMOTE) is conducted to balance the dataset. Subsequently, seven machine learning models are trained utilizing 10-fold cross-validation with grid search. Findings reveal that, based on the balanced dataset, the performance of ensemble learning models is significantly better than that of individual machine learning models. Specifically, the XGBoost model achieves the highest performance, with a macro-F1 of 98.49%, G-mean value of 98.49%, and accuracy of 98.49%. The final predictive model (the SMOTE-based XGBoost model) is explained using the SHapley Additive exPlanations (SHAP). Service year, structure, and location are the three most important features influencing building structural safety. This study represents a promising approach for automated screening of the anomalous safety condition among buildings, optimizing resource allocation, and enhancing the effectiveness in decision-making for construction and maintenance.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/6695396","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143741696","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 Multistrategy Fusion–Improved Black Widow Optimization Algorithm for Structural Damage Identification","authors":"Zhen Chen,&nbsp;Wanying Li,&nbsp;Xiaoshuai Liu,&nbsp;Yikai Wang,&nbsp;Tommy H. T. Chan","doi":"10.1155/stc/2939779","DOIUrl":"https://doi.org/10.1155/stc/2939779","url":null,"abstract":"<div>\u0000 <p>Structural damage identification based on metaheuristic algorithms is an important part of structural health monitoring with great potential. However, the metaheuristic intelligent algorithms probably have flaws of slow convergence speed and low calculation accuracy, which need to be improved to address engineering optimization problems. In this paper, the black widow optimization (BWO) algorithm is used for structural damage identification. In addition, a multistrategy fusion–improved BWO (IBWO) algorithm is proposed by introducing the tent chaotic mapping, the golden sine equation, the gazelle wandering equation, and the boundary treatments. First, in the population initialization stage, tent chaotic mapping is introduced to improve the quality of the initial solution. Second, the golden sine strategy is used to acquire the optimal solution quickly in local search. Then, the motion equation of the gazelle algorithm is employed to enhance the global search ability and avoid the algorithm falling into the local optimal solution. Finally, the boundary processing strategy is presented to reduce the calculation of solutions and improve the optimization efficiency. A novel damage identification objective function is redefined by combining the modal assurance criterion and the modal flexibility. Then, a two-story rigid frame structure is utilized for numerical simulations. Moreover, experimental studies with a simply supported beam were carried out to verify the performance of the proposed damage identification method. Simulation results and experimental studies demonstrate that, even with the interference of strong noise, the IBWO algorithm has a higher accuracy and efficiency in damage identification compared to the BWO algorithm.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/2939779","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143717035","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":"Cable Force Identification With Unknown and Variable Elastic Boundary Supports: Theory and Validation","authors":"Xing Fu,&nbsp;Si-Yuan Sun,&nbsp;Hong-Nan Li,&nbsp;Qing-Wei Li","doi":"10.1155/stc/6165260","DOIUrl":"https://doi.org/10.1155/stc/6165260","url":null,"abstract":"<div>\u0000 <p>The vibration method is widely used for identifying cable tension. However, the boundary conditions of cables in structures are often not ideally hinged, resulting in a significant error in identifying cable forces. To precisely determine the stress state of cables, this paper proposes a methodology for cable force identification with unknown and variable elastic boundary supports. First, an equivalent single-degree-of-freedom (SDOF) model of the cable with variable elastic boundary supports is established. A mathematical relationship between the frequencies of ideal hinged cables and those with elastic boundary supports is then established. Subsequently, the first-order frequency is modified by accounting for the mode shape values at the midpoint and both endpoints of the cable. Finally, a methodology for cable force identification with unknown and variable elastic boundary supports is proposed and validated through numerical simulations, experiments, and on-site tests. The results indicate that the proposed cable force identification method can adapt excellently to the variable elastic boundary supports without relying on known the boundary constraint stiffness. In numerical simulations, the identification errors of the proposed method are all less than 1%, while in experiments and on-site tests, the identification errors are within 5%, demonstrating its high accuracy and strong adaptability. The proposed method considers the complex boundary conditions of cables, eliminating the need to solve for unknown boundary constraint stiffness, indicating that it can adapt to the unknown and variable boundary stiffness of cables.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/6165260","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143717414","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}