Structural Control & Health Monitoring最新文献

{"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}

{"title":"Application of Acoustic Emission and Baseline-Based Approach for Early Fatigue-Damage Detection","authors":"Lu Cheng,&nbsp;Ze Chang,&nbsp;Roger Groves,&nbsp;Milan Veljkovic","doi":"10.1155/stc/3442236","DOIUrl":"https://doi.org/10.1155/stc/3442236","url":null,"abstract":"<div>\u0000 <p>Monitoring fatigue damage in mechanical connections is essential for maintaining the safety and structural integrity of offshore wind turbines (OWTs), particularly during the early stage of crack initiation. Recently, the C1 wedge connection (C1-WC) has emerged as a promising innovation for use in OWTs. Acoustic emission (AE) monitoring is a widely used real-time technique for detecting fatigue cracks. The space limitations of the lower segment holes in the C1-WC presents challenges for detecting surface cracks with conventional AE sensors. Thin Piezoelectric Wafer Active Sensors (PWAS), while small and lightweight, face limitations due to their poor signal-to-noise ratio. In this study, we propose a baseline-based approach to enhance the effectiveness of PWAS for accurate AE monitoring in confined spaces. A benchmark model correlating the damage state of specimens is created by breaking pencil leads. Multivariate feature vectors are extracted and then mapped to the Mahalanobis distance for damage identification. The proposed method is validated through testing on compact specimens and C1-WC specimens. To enhance the AE detection results, supplementary monitoring techniques, including digital image correlation, crack propagation gauges, and distributed optical fiber sensors, are employed. The experimental setup, signal acquisition, and detection efficiency of these techniques are briefly outlined. This study demonstrates that the proposed approach is highly effective in detecting early damage in C1-WC specimens using AE monitoring with PWAS.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/3442236","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689187","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":"Antiwhip Measures for High-Energy Piping in Nuclear Power Plants Using Lead Extrusion Impact Damping Devices: Tests and Simulations","authors":"Luwei Shi,&nbsp;Yiqian Lu,&nbsp;Lingyun Peng,&nbsp;Jingsheng Qiao,&nbsp;Ruhan Zhang,&nbsp;Tianwei Sun","doi":"10.1155/stc/8829452","DOIUrl":"https://doi.org/10.1155/stc/8829452","url":null,"abstract":"<div>\u0000 <p>In this study, the antiwhip capability of high-energy piping in nuclear power plants was investigated based on the basement structure of a conventional island in a nuclear power engineering project. A lead extrusion impact damping device was developed, and its mechanical performance was validated through uniaxial static loading tests. A 1:4 scaled-down test model was designed and fabricated using traditional energy-dissipating steel beams, lead extrusion impact damping, and concrete blocks as three types of antiwhip restraining devices. Impact test studies were conducted supplemented by destructive impact tests without antiwhip restraining devices. Finite element models were established using Ansys LS-DYNA simulation software, and simulations were conducted for these four scenarios. The antiwhip performances of different antiwhip measures were evaluated by comparing the test and finite element simulation results and considering factors such as the impact force, wall displacement, wall acceleration, and crack distribution and development. The results indicate that while traditional energy-dissipating steel beams continue to provide some antiwhip effectiveness, the lead extrusion impact damping solution exhibits a significantly improved performance with better control of the structural dynamic response. In contrast, the concrete block solution demonstrated a poorer performance, leading to severe damage in structures like those without antiwhip restraining devices.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/8829452","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689196","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":"Experimental Study on the Damping Ratio Evaluation of a Cable-Stayed Bridge Based on Damping Dissipation Function","authors":"Fengzong Gong,&nbsp;Cheng Pan,&nbsp;Ye Xia","doi":"10.1155/stc/5810450","DOIUrl":"https://doi.org/10.1155/stc/5810450","url":null,"abstract":"<div>\u0000 <p>Damping ratio is a fundamental dynamic parameter of the structure. Recent monitoring of cable-stayed bridges has shown that the damping ratio changes under different operating conditions. In general, the damping ratio of a structure can only be evaluated after the structure is built, and it is still challenging to theoretically calculate the damping ratio of a structure beforehand. To further understand the mechanism of the bridge damping ratio, this paper proposes an evaluation method based on the damping dissipation function. The effects of the initial and modal strain energy of the structure on the damping dissipation are considered. The damping dissipation function of substructures of a laboratory cable-stayed bridge model was tested, and the structural system damping ratio was evaluated. Furthermore, the theoretical discussion and experimental verification of the effect of support friction on the damping ratio were conducted. The results indicate that the damping ratio of the substructure varies with both the vibration amplitude and the initial stress level. Consideration of the initial strain energy during testing the substructure damping dissipation function can lead to more accurate results in the evaluation of the damping ratio of the structural system. In addition, support friction also significantly influences the damping ratio of the longitudinal drift and vertical bending modes of the structure.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/5810450","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629862","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":"Development and Validation of New Methodology for Automated Operational Modal Analysis Using Modal Domain Range","authors":"Fatih Yesevi Okur,&nbsp;Ahmet Can Altunişik,&nbsp;Ebru Kalkan Okur","doi":"10.1155/stc/6267884","DOIUrl":"https://doi.org/10.1155/stc/6267884","url":null,"abstract":"<div>\u0000 <p>The ability to conduct automated operational modal analysis is essential for enabling real-time structural health monitoring without human intervention. Such automation remains a significant challenge due to the complexity of processing large datasets and the necessity of setting multiple user-defined thresholds. This study introduces a novel methodology for automated modal identification that leverages the enhanced frequency domain decomposition method. The key innovation of the proposed approach is the concept of the modal domain range, a parameter calculated for each frequency of the structure to distinguish physical modes from noise and false modes. The modal domain range is derived using the correlation of mode shapes, assessed through the modal assurance criterion method. High values within this range indicate dominant structural frequencies, enabling the autonomous identification of the structure’s dynamic characteristics. To validate the proposed methodology, experimental data from the Z24 Bridge, a prestressed concrete structure, were analyzed. The dynamic parameters, including natural frequencies and mode shapes, were identified using the developed approach and compared with reference data from the literature. The results demonstrated that the methodology achieves remarkable precision. Moreover, the proposed method effectively reduces the impact of noise and environmental variations through a systematic filtering and grouping process. The findings highlight the robustness and adaptability of the methodology, demonstrating its capability for automated and accurate identification of modal parameters in civil engineering structures.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/6267884","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143602692","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 Novel Approach for Anomaly Detection in Vibration-Based Structural Health Monitoring Using Autoencoders in Deep Learning","authors":"Fatih Yesevi Okur,&nbsp;Ahmet Can Altunişik,&nbsp;Ebru Kalkan Okur","doi":"10.1155/stc/5602604","DOIUrl":"https://doi.org/10.1155/stc/5602604","url":null,"abstract":"<div>\u0000 <p>Structural health monitoring (SHM) has been widely employed in civil infrastructures for a number of years. Real-time monitoring of civil projects involves the utilization of diverse sensors. Nevertheless, accurately assessing the actual condition of a structure can pose challenges due to the existence of anomalies in the collected data. Abnormalities in this context often arise from a variety of factors, including extreme weather conditions, malfunctioning sensors, and structural impairments. The existing condition of anomaly detection is significantly impeded by this disparity. Online detection of anomalies in SHM data plays a crucial role in promptly assessing the status of structures and making informed decisions. In vibration-based SHM, enhanced frequency domain decomposition (EFDD) is one of the most used methods in the frequency domain. The signal output obtained from EFDD also includes the frequencies of the structures, which is a holistic evaluation. The findings of frequency measurements are influenced by the presence of structural damages. Extracting damage-sensitive characteristics from structural response has emerged as a complex task. Deep learning approaches have garnered growing interest due to their capacity to efficiently extract high-level abstract features from raw data. Within the scope of the study, a novel approach based on anomaly detection of changes in the signal output obtained using the EFDD was developed with autoencoders in deep learning. The performance of the novel approach was examined depending on different noise ratios (0%, 0.5%, 1%, 1.5%, and 2.0%) using the Z24 Bridge dataset. In the autoencoder training model, an autoencoder model containing a 4 Conv1D layer encoder–decoder as 128 × 64 × 64 × 128 was designed. By using the signal data of the first singular values obtained with the EFDD method, grouping was made with the labels “training data (1260 pieces),” “undamaged new data (250 pieces),” and “damaged new data (320 pieces).” In addition, the upper limit of the reconstruction error was calculated as 810 using the training data in the autoencoder model. The filtered reconstruction error values obtained were compared under different noise levels. At the end of the study, it was concluded that the novel approach works effectively under different noises and can be used in anomaly detection.</p>\u0000 </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/5602604","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143602628","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}