Pub Date : 2023-05-25DOI: 10.1177/14759217231176050
Kangzhen Yang, You-liang Ding, Huachen Jiang, Yun Zhang, Zhengbo Zou
Damage identification has always been one of the core functions of bridge structural health monitoring (SHM) systems. Damage identification techniques based on deep learning (DL) approaches have shown great promise recently. However, DL methods still need to be improved owing to their poor interpretability and generalization performance. The fundamental reason lies in the separation between physics-based mechanical principles and data-driven DL methods. To address this issue, this paper proposes a physics-inspired approach combining the data-driven method and the internal force redistribution effects to perform efficient damage identification. Firstly, the mechanical derivation of internal force redistribution is given based on a simplified three-span continuous bridge. Then, two types of typical damage scenarios including segment stiffness decrease and prestress loss are simulated to formulate the damage dataset with monitored field data noise added. Next, a modified Transformer model with multi-dimensional output is trained to obtain the complex dynamic spatiotemporal mapping among multiple measurement points from the intact structure as a benchmark model. Finally, the relationship between multiple damage patterns and the corresponding output regression residual distribution is studied, based on which the flexible combinations of the sensors are proposed as the test set to characterize the internal force redistribution due to damage. Validation on the extended dataset showed that this approach is effective to realize preliminary identification of damage patterns and resist interference from noise at the monitoring site.
{"title":"Deep learning-based bridge damage identification approach inspired by internal force redistribution effects","authors":"Kangzhen Yang, You-liang Ding, Huachen Jiang, Yun Zhang, Zhengbo Zou","doi":"10.1177/14759217231176050","DOIUrl":"https://doi.org/10.1177/14759217231176050","url":null,"abstract":"Damage identification has always been one of the core functions of bridge structural health monitoring (SHM) systems. Damage identification techniques based on deep learning (DL) approaches have shown great promise recently. However, DL methods still need to be improved owing to their poor interpretability and generalization performance. The fundamental reason lies in the separation between physics-based mechanical principles and data-driven DL methods. To address this issue, this paper proposes a physics-inspired approach combining the data-driven method and the internal force redistribution effects to perform efficient damage identification. Firstly, the mechanical derivation of internal force redistribution is given based on a simplified three-span continuous bridge. Then, two types of typical damage scenarios including segment stiffness decrease and prestress loss are simulated to formulate the damage dataset with monitored field data noise added. Next, a modified Transformer model with multi-dimensional output is trained to obtain the complex dynamic spatiotemporal mapping among multiple measurement points from the intact structure as a benchmark model. Finally, the relationship between multiple damage patterns and the corresponding output regression residual distribution is studied, based on which the flexible combinations of the sensors are proposed as the test set to characterize the internal force redistribution due to damage. Validation on the extended dataset showed that this approach is effective to realize preliminary identification of damage patterns and resist interference from noise at the monitoring site.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45656766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-25DOI: 10.1177/14759217231174697
Pan Gao, Jiepeng Liu, Xuanding Wang, Yubo Jiao, Wenchen Shan
Concrete-filled steel tubular (CFST) columns are frequently used as the main load-bearing components in engineering structures due to their excellent load-bearing capacity. However, the presence of steel tube makes it impossible to accurately detect the damage characteristics of concrete by only relying on traditional mechanical measurement methods. This article quantitatively investigates the concrete damage of circular CFST column during axial compression based on the acoustic emission (AE) technique. Through the cumulative AE parameters including amplitude, count, and energy, the axial compression process of the CFST column can be divided into five main stages (Stage I is divided into two substages) to represent the different damage degree. The damage characteristics of concrete at each stage were explained by combining AE results and mechanical phenomena. A sensitivity analysis of the axial compression process was carried out using the Historic Index ( HI) and Severity ( Sr) and found that the sudden rise in HI and Sr corresponded to the changes in the different loading stages. The Improved b ( Ib) value analysis calculated from the AE amplitudes reflects the evolution mechanism of the crack and can be used for the identification of the final failure moment of the specimen. Finally, a new method for processing and analyzing AE parameters was proposed, which effectively enhanced the dimensionality of real-time monitoring information on the damage of concrete filled in the steel tube.
{"title":"Damage evaluation and failure mechanism analysis of axially compressed circular concrete-filled steel tubular column via AE monitoring","authors":"Pan Gao, Jiepeng Liu, Xuanding Wang, Yubo Jiao, Wenchen Shan","doi":"10.1177/14759217231174697","DOIUrl":"https://doi.org/10.1177/14759217231174697","url":null,"abstract":"Concrete-filled steel tubular (CFST) columns are frequently used as the main load-bearing components in engineering structures due to their excellent load-bearing capacity. However, the presence of steel tube makes it impossible to accurately detect the damage characteristics of concrete by only relying on traditional mechanical measurement methods. This article quantitatively investigates the concrete damage of circular CFST column during axial compression based on the acoustic emission (AE) technique. Through the cumulative AE parameters including amplitude, count, and energy, the axial compression process of the CFST column can be divided into five main stages (Stage I is divided into two substages) to represent the different damage degree. The damage characteristics of concrete at each stage were explained by combining AE results and mechanical phenomena. A sensitivity analysis of the axial compression process was carried out using the Historic Index ( HI) and Severity ( Sr) and found that the sudden rise in HI and Sr corresponded to the changes in the different loading stages. The Improved b ( Ib) value analysis calculated from the AE amplitudes reflects the evolution mechanism of the crack and can be used for the identification of the final failure moment of the specimen. Finally, a new method for processing and analyzing AE parameters was proposed, which effectively enhanced the dimensionality of real-time monitoring information on the damage of concrete filled in the steel tube.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45722461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-23DOI: 10.1177/14759217231176968
Weiming Yin, Yefa Hu, Guoping Ding, Wen-bin Xu, Lei Feng, Xue-liang Chen, Xifei Cao
As the vital auxiliary machine of the coal-fired power plant, monitoring the real-time operating status of coal mills is critical to the secure and stable operation of the power plant. In this study, a new method of construction of the coal mill health indicator (HI) is proposed, and the operation condition monitoring approaches of the device are designed based on the HI value. Firstly, an improved coal mill dynamic model considering the joint influence of drying force, ventilation force, and grinding force is established, and a synchronous optimization approach of model structures and parameters based on the genetic algorithm is designed. Then the deviation between the model output and the actual value is computed by the designed distance measuring approach, and the typical fault characteristic factors are designed based on the relation between the dynamic model and the actual operating state. And the HI value is calculated by fusing the deviation with the characteristic factors. Finally, the HI value is applied to the process of operation condition evaluation, fault diagnosis, and trend prediction, and has obtained favorable application effects. The results of this research show that the established HI value can reflect the operating status of the coal mill promptly and accurately, and the monitoring method designed based on the values have satisfactory practicality.
{"title":"Health indicator construction and application of coal mill based on the dynamic model","authors":"Weiming Yin, Yefa Hu, Guoping Ding, Wen-bin Xu, Lei Feng, Xue-liang Chen, Xifei Cao","doi":"10.1177/14759217231176968","DOIUrl":"https://doi.org/10.1177/14759217231176968","url":null,"abstract":"As the vital auxiliary machine of the coal-fired power plant, monitoring the real-time operating status of coal mills is critical to the secure and stable operation of the power plant. In this study, a new method of construction of the coal mill health indicator (HI) is proposed, and the operation condition monitoring approaches of the device are designed based on the HI value. Firstly, an improved coal mill dynamic model considering the joint influence of drying force, ventilation force, and grinding force is established, and a synchronous optimization approach of model structures and parameters based on the genetic algorithm is designed. Then the deviation between the model output and the actual value is computed by the designed distance measuring approach, and the typical fault characteristic factors are designed based on the relation between the dynamic model and the actual operating state. And the HI value is calculated by fusing the deviation with the characteristic factors. Finally, the HI value is applied to the process of operation condition evaluation, fault diagnosis, and trend prediction, and has obtained favorable application effects. The results of this research show that the established HI value can reflect the operating status of the coal mill promptly and accurately, and the monitoring method designed based on the values have satisfactory practicality.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47413460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-21DOI: 10.1177/14759217231171696
Wenjun Wang, Chao Su, Guohui Han, Yijia Dong
Water leakage is a critical factor reflecting the structural safety of shield tunnels. Computer vision provides new opportunities to overcome the shortcomings of manual visual inspection and realize automatic detection of water leakage regions. In this study, we propose a leakage segmentation model with an encoder–decoder structure. The encoder adopts multi-branch convolutional attention for feature fusion, and the decoder adopts a lightweight design that only contains multi-layer perceptron. Standard convolution in multi-branch is decomposed to two depth-wise strip convolutions to realize lightweight design and extract strip-like features. Extensive ablation and comparative studies were conducted to test model performance. Test results show that our model achieves robust detection of water leakage under strong noise background, reaching an intersection over union of 90.75% with performance-computation trade-off. Consequently, the proposed method can be an effective alternative to the current visual inspection technologies, and provide a nearly automated inspection platform for shield tunnels.
{"title":"Efficient segmentation of water leakage in shield tunnel lining with convolutional neural network","authors":"Wenjun Wang, Chao Su, Guohui Han, Yijia Dong","doi":"10.1177/14759217231171696","DOIUrl":"https://doi.org/10.1177/14759217231171696","url":null,"abstract":"Water leakage is a critical factor reflecting the structural safety of shield tunnels. Computer vision provides new opportunities to overcome the shortcomings of manual visual inspection and realize automatic detection of water leakage regions. In this study, we propose a leakage segmentation model with an encoder–decoder structure. The encoder adopts multi-branch convolutional attention for feature fusion, and the decoder adopts a lightweight design that only contains multi-layer perceptron. Standard convolution in multi-branch is decomposed to two depth-wise strip convolutions to realize lightweight design and extract strip-like features. Extensive ablation and comparative studies were conducted to test model performance. Test results show that our model achieves robust detection of water leakage under strong noise background, reaching an intersection over union of 90.75% with performance-computation trade-off. Consequently, the proposed method can be an effective alternative to the current visual inspection technologies, and provide a nearly automated inspection platform for shield tunnels.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46060717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-19DOI: 10.1177/14759217231170316
Qi‐Ang Wang, Yang Dai, Zhan-guo Ma, Jun-Fang Wang, Jian‐Fu Lin, Y. Ni, W. Ren, Jian Jiang, Xuan Yang, Jia-Ru Yan
Central to structural health monitoring (SHM) is data modeling, manipulation, and interpretation on the basis of a sophisticated SHM system. Despite continuous evolution of SHM technology, the precise modeling and forecasting of SHM measurements under various uncertainties to extract structural condition-relevant knowledge remains a challenge. Aiming to resolve this problem, a novel application of a fully probabilistic and high-precision data modeling method was proposed in the context of an improved Sparse Bayesian Learning (iSBL) scheme. The proposed iSBL data modeling framework features the following merits. It can remove the need to specify the number of terms in the data-fitting function, and automatize sparsity of the Bayesian model based on the features of SHM monitoring data, which will enhance the generalization ability and then improve the data prediction accuracy. Embedded in a Bayesian framework which exhibits built-in protection against over-fitting problems, the proposed iSBL scheme has high robustness to data noise, especially for data forecasting. The model is verified to be effective on SHM vibration field monitoring data collected from a real-world large-scale cable-stayed bridge. The recorded acceleration data with two different vibration patterns, that is, stationary ambient vibration data and non-stationary decay vibration data, are investigated, returning accurate probabilistic predictions in both the time and frequency domains.
{"title":"Towards high-precision data modeling of SHM measurements using an improved sparse Bayesian learning scheme with strong generalization ability","authors":"Qi‐Ang Wang, Yang Dai, Zhan-guo Ma, Jun-Fang Wang, Jian‐Fu Lin, Y. Ni, W. Ren, Jian Jiang, Xuan Yang, Jia-Ru Yan","doi":"10.1177/14759217231170316","DOIUrl":"https://doi.org/10.1177/14759217231170316","url":null,"abstract":"Central to structural health monitoring (SHM) is data modeling, manipulation, and interpretation on the basis of a sophisticated SHM system. Despite continuous evolution of SHM technology, the precise modeling and forecasting of SHM measurements under various uncertainties to extract structural condition-relevant knowledge remains a challenge. Aiming to resolve this problem, a novel application of a fully probabilistic and high-precision data modeling method was proposed in the context of an improved Sparse Bayesian Learning (iSBL) scheme. The proposed iSBL data modeling framework features the following merits. It can remove the need to specify the number of terms in the data-fitting function, and automatize sparsity of the Bayesian model based on the features of SHM monitoring data, which will enhance the generalization ability and then improve the data prediction accuracy. Embedded in a Bayesian framework which exhibits built-in protection against over-fitting problems, the proposed iSBL scheme has high robustness to data noise, especially for data forecasting. The model is verified to be effective on SHM vibration field monitoring data collected from a real-world large-scale cable-stayed bridge. The recorded acceleration data with two different vibration patterns, that is, stationary ambient vibration data and non-stationary decay vibration data, are investigated, returning accurate probabilistic predictions in both the time and frequency domains.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42441966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-19DOI: 10.1177/14759217231172297
T. Abbott, F. Yuan
An integrated system is proposed to visualize subsurface barely visible impact damage (BVID) in composite structures using three-dimensional (3D) digital image correlation (3D DIC). This system uses a pair of digital cameras to record video frames in the field-of-view (FOV) of the structure’s surface, capturing the wavefield generated via chirp excitation in the near-ultrasonic frequency range. Significant pitfalls of previous efforts of damage imaging using two-dimensional DIC have been largely mitigated. First, 3D DIC enables capturing out-of-plane displacements, which are much larger in amplitude versus in-plane displacements that a single camera would be limited to sensing, thus increasing the signal-to-noise ratio. This enhancement in turn increases the sensitivity of the stereo-camera system. Second, a total wave energy (TWE) damage imaging condition is proposed to visualize the local damage region. The monogenic signal obtained via Reisz transform (RT) is employed to compute the instantaneous amplitude, with which the local wave energy can be calculated spatially over time. Since a high displacement amplitude and thus high wave energy will occur in the damage region due to the local resonance, the proposed TWE imaging condition can relax the Nyquist sampling requirement, unlike guided-wave-based structural health monitoring techniques which require fully reconstructing the wavefield and wave modes through sampling that satisfies the Nyquist criterion. As such, a much lower camera frame rate is adequate for the proposed system. Consequently, the maximum spatial resolution of the camera for a given FOV can be achieved at the expense of a reduced frame rate. With the maximized pixel resolution and reduced frame rate for employing the TWE imaging condition, composite structures can be inspected or monitored with a larger FOV. As a result, there is no longer any need to apply signal enhancement techniques, such as sample interleaving, image stitching, or averaging, to increase the effective performance of the camera. Rather than needing thousands of repeated videos for minimizing the incoherent noise, only a single stereo-video with a few seconds of sampling duration is necessary for damage imaging. The use of a powerful piezo-shaker also increases the wave signal amplitude and further enhances sensitivity without permanent adhesion. To demonstrate this stereo-camera concept with the TWE imaging condition, the system was used to image damage in two carbon fiber reinforced polymer composite honeycomb panels, which had been subjected to low-velocity impacts (2 J). For each panel, two excitation configurations were used to verify the robustness of the system. Initial damage maps produced for a 100 × 100-mm FOV using a three-second stereo-video show accurate damage imaging ability that is independent of excitation location and comparable to benchmark damage images computed from laser Doppler vibrometer data and those gathered from ultrasonic and X-
提出了一种基于三维数字图像相关(3D DIC)的复合材料结构表面下微可见冲击损伤可视化系统。该系统使用一对数码相机在结构表面的视场(FOV)中记录视频帧，捕获由近超声波频率范围内的啁啾激发产生的波场。以前使用二维DIC进行损伤成像的重大缺陷已经在很大程度上得到了缓解。首先，3D DIC能够捕获面外位移，其振幅比单相机只能感知的面内位移大得多，从而提高了信噪比。这种增强反过来又增加了立体相机系统的灵敏度。其次，提出了一种全波能损伤成像条件，用于局部损伤区域的可视化。利用Reisz变换(RT)得到的单基因信号计算瞬时幅值，利用瞬时幅值计算局部波能量随时间的空间分布。由于局部共振会在损伤区域产生高位移振幅和高波能量，因此所提出的TWE成像条件可以放宽Nyquist采样要求，而不像基于导波的结构健康监测技术需要通过满足Nyquist准则的采样来完全重建波场和波模态。因此，一个低得多的相机帧率是足够的，提出的系统。因此，在给定视场的最大空间分辨率的相机可以实现在降低帧率的代价。利用最大的像素分辨率和降低的帧率，采用TWE成像条件，可以在更大的视场下检查或监测复合结构。因此，不再需要应用信号增强技术，如样本交错、图像拼接或平均，以提高相机的有效性能。而不是需要成千上万的重复视频，以尽量减少非相干噪声，只有一个单一的立体视频与几秒钟的采样时间是必要的损伤成像。使用强大的压电激振器也增加了波信号幅度，并进一步提高了灵敏度，而不会永久粘附。为了在TWE成像条件下演示这种立体相机概念，该系统被用于对两块碳纤维增强聚合物复合材料蜂窝板的损伤进行成像，这些蜂窝板受到了低速撞击(2 J)。对于每个面板，采用两种激励配置来验证系统的鲁棒性。使用3秒立体视频对100 × 100 mm FOV生成的初始损伤图显示出与激励位置无关的精确损伤成像能力，可与激光多普勒测震仪数据计算的基准损伤图像以及超声波和x射线计算机断层扫描收集的图像相媲美。这种高效可靠的集成系统在复合材料飞机和其他关键结构的实时损伤检测中具有很大的潜力。
{"title":"Subsurface impact damage imaging for composite structures using 3D digital image correlation","authors":"T. Abbott, F. Yuan","doi":"10.1177/14759217231172297","DOIUrl":"https://doi.org/10.1177/14759217231172297","url":null,"abstract":"An integrated system is proposed to visualize subsurface barely visible impact damage (BVID) in composite structures using three-dimensional (3D) digital image correlation (3D DIC). This system uses a pair of digital cameras to record video frames in the field-of-view (FOV) of the structure’s surface, capturing the wavefield generated via chirp excitation in the near-ultrasonic frequency range. Significant pitfalls of previous efforts of damage imaging using two-dimensional DIC have been largely mitigated. First, 3D DIC enables capturing out-of-plane displacements, which are much larger in amplitude versus in-plane displacements that a single camera would be limited to sensing, thus increasing the signal-to-noise ratio. This enhancement in turn increases the sensitivity of the stereo-camera system. Second, a total wave energy (TWE) damage imaging condition is proposed to visualize the local damage region. The monogenic signal obtained via Reisz transform (RT) is employed to compute the instantaneous amplitude, with which the local wave energy can be calculated spatially over time. Since a high displacement amplitude and thus high wave energy will occur in the damage region due to the local resonance, the proposed TWE imaging condition can relax the Nyquist sampling requirement, unlike guided-wave-based structural health monitoring techniques which require fully reconstructing the wavefield and wave modes through sampling that satisfies the Nyquist criterion. As such, a much lower camera frame rate is adequate for the proposed system. Consequently, the maximum spatial resolution of the camera for a given FOV can be achieved at the expense of a reduced frame rate. With the maximized pixel resolution and reduced frame rate for employing the TWE imaging condition, composite structures can be inspected or monitored with a larger FOV. As a result, there is no longer any need to apply signal enhancement techniques, such as sample interleaving, image stitching, or averaging, to increase the effective performance of the camera. Rather than needing thousands of repeated videos for minimizing the incoherent noise, only a single stereo-video with a few seconds of sampling duration is necessary for damage imaging. The use of a powerful piezo-shaker also increases the wave signal amplitude and further enhances sensitivity without permanent adhesion. To demonstrate this stereo-camera concept with the TWE imaging condition, the system was used to image damage in two carbon fiber reinforced polymer composite honeycomb panels, which had been subjected to low-velocity impacts (2 J). For each panel, two excitation configurations were used to verify the robustness of the system. Initial damage maps produced for a 100 × 100-mm FOV using a three-second stereo-video show accurate damage imaging ability that is independent of excitation location and comparable to benchmark damage images computed from laser Doppler vibrometer data and those gathered from ultrasonic and X-","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48154236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-18DOI: 10.1177/14759217231170742
P. Negi, R. Kromanis, A. Dorée, K. Wijnberg
Inland navigation structures (INS) facilitate transportation of goods in rivers and canals. Transportation of goods over waterways is more energy efficient than on roads and railways. INS, similar to other civil structures, are aging and require frequent condition assessment and maintenance. Countries, in which INS are important to their economies, such as the Netherlands and the United States, allocate significant budgets for maintenance and renovation of exiting INS, as well as for building new structures. Timely maintenance and early detection of a change to material or geometric properties (i.e., damage) can be supported with the structural health monitoring (SHM), in which monitored data, such as load, structural response, environmental actions, are analyzed. Huge scientific efforts are realized in bridge SHM, but when it comes to SHM of INS, the efforts are significantly lower. Therefore, the SHM community has opportunities to develop new solutions for SHM of INS and convince asset owners of their benefits. This review article, first, articulates the need to keep INS safe to use and fit for purpose, and the challenges associated with it. Second, it defines and reviews sensors, sensing technologies, and approaches for SHM of INS. Then, INS and their components, including structures in ports, are identified, described, and illustrated, and their monitoring efforts are reviewed. Finally, the review article emphasizes the added value of SHM systems for INS, concludes on the current achievements, and proposes future trajectories for SHM of INS and ports.
{"title":"Structural health monitoring of inland navigation structures and ports: a review on developments and challenges","authors":"P. Negi, R. Kromanis, A. Dorée, K. Wijnberg","doi":"10.1177/14759217231170742","DOIUrl":"https://doi.org/10.1177/14759217231170742","url":null,"abstract":"Inland navigation structures (INS) facilitate transportation of goods in rivers and canals. Transportation of goods over waterways is more energy efficient than on roads and railways. INS, similar to other civil structures, are aging and require frequent condition assessment and maintenance. Countries, in which INS are important to their economies, such as the Netherlands and the United States, allocate significant budgets for maintenance and renovation of exiting INS, as well as for building new structures. Timely maintenance and early detection of a change to material or geometric properties (i.e., damage) can be supported with the structural health monitoring (SHM), in which monitored data, such as load, structural response, environmental actions, are analyzed. Huge scientific efforts are realized in bridge SHM, but when it comes to SHM of INS, the efforts are significantly lower. Therefore, the SHM community has opportunities to develop new solutions for SHM of INS and convince asset owners of their benefits. This review article, first, articulates the need to keep INS safe to use and fit for purpose, and the challenges associated with it. Second, it defines and reviews sensors, sensing technologies, and approaches for SHM of INS. Then, INS and their components, including structures in ports, are identified, described, and illustrated, and their monitoring efforts are reviewed. Finally, the review article emphasizes the added value of SHM systems for INS, concludes on the current achievements, and proposes future trajectories for SHM of INS and ports.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":"1 1","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65887277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-17DOI: 10.1177/14759217231173439
G. Uribe-Riestra, P. Ayuso-Faber, Miguel A. Rivero-Ayala, J. Cauich-Cupul, F. Gamboa, F. Avilés
A method based on changes of electrical resistance was used to evaluate non-visible damage inflicted to multiscale hierarchical composites subjected to monotonic and cyclic bending loads. The composites comprise glass fiber weaves modified by carbon nanotubes in a vinyl ester matrix. Damage sensing is achieved by placing an array of electrodes close to the surfaces of four-point bending specimens and is correlated to strain fields measured by digital image correlation (DIC). The top (compressive) surface exhibited lower electrical resistance changes than the bottom (tensile) one. Spatial measurements of electrical resistance allowed identification of the most severely damaged zones, which coincided with those pinpointed by DIC. DIC also indicated an important presence of irreversible interlaminar shear strains accumulating close to the supports and/or loading introduction elements, which coincided with the location of delamination. The electrical technique allowed not only the detection of the onset of damage in the form of initial fiber breakage and matrix cracking, but also the detection of damage progression under cyclic loading and low-velocity impact.
{"title":"Structural health monitoring of carbon nanotube-modified glass fiber-reinforced polymer composites by electrical resistance measurements and digital image correlation","authors":"G. Uribe-Riestra, P. Ayuso-Faber, Miguel A. Rivero-Ayala, J. Cauich-Cupul, F. Gamboa, F. Avilés","doi":"10.1177/14759217231173439","DOIUrl":"https://doi.org/10.1177/14759217231173439","url":null,"abstract":"A method based on changes of electrical resistance was used to evaluate non-visible damage inflicted to multiscale hierarchical composites subjected to monotonic and cyclic bending loads. The composites comprise glass fiber weaves modified by carbon nanotubes in a vinyl ester matrix. Damage sensing is achieved by placing an array of electrodes close to the surfaces of four-point bending specimens and is correlated to strain fields measured by digital image correlation (DIC). The top (compressive) surface exhibited lower electrical resistance changes than the bottom (tensile) one. Spatial measurements of electrical resistance allowed identification of the most severely damaged zones, which coincided with those pinpointed by DIC. DIC also indicated an important presence of irreversible interlaminar shear strains accumulating close to the supports and/or loading introduction elements, which coincided with the location of delamination. The electrical technique allowed not only the detection of the onset of damage in the form of initial fiber breakage and matrix cracking, but also the detection of damage progression under cyclic loading and low-velocity impact.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48423851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-17DOI: 10.1177/14759217231173873
Xing Gao, Wei Wang
Preload is important for the performance of bolted connections, especially for high strength bolt like slip-critical blind bolt (SCBB). There have been relatively few studies focused on detecting the looseness of blind bolts prior to this research. This article proposes a method based on the acoustoelastic effect to monitor the change in the preload in the bolt and detect the relaxation from the initial preload. The technique is suitable for such blind bolted connection because it only needs to connect with one side of bolted connection, unlike some other bolted connection monitoring methods. Considering that for SCBB, the traditional acoustoelastic technique cannot be applied because it needs the unstressed state of the bolt as baseline. The relationship between looseness of bolt load and change of travelling time is deduced. The measuring objective is then changed to the looseness of bolt load, instead of the bolt load itself. The practical processes of calibration, real-time monitoring and periodical detection are proposed, considering the application on real construction site. The tests on different configurations of SCBBs prove the reliability of the ultrasonic technique based on change in time-of-flight.
{"title":"Bolt load looseness measurement for slip-critical blind bolt by ultrasonic technique: a feasibility study with calibration and experimental verification","authors":"Xing Gao, Wei Wang","doi":"10.1177/14759217231173873","DOIUrl":"https://doi.org/10.1177/14759217231173873","url":null,"abstract":"Preload is important for the performance of bolted connections, especially for high strength bolt like slip-critical blind bolt (SCBB). There have been relatively few studies focused on detecting the looseness of blind bolts prior to this research. This article proposes a method based on the acoustoelastic effect to monitor the change in the preload in the bolt and detect the relaxation from the initial preload. The technique is suitable for such blind bolted connection because it only needs to connect with one side of bolted connection, unlike some other bolted connection monitoring methods. Considering that for SCBB, the traditional acoustoelastic technique cannot be applied because it needs the unstressed state of the bolt as baseline. The relationship between looseness of bolt load and change of travelling time is deduced. The measuring objective is then changed to the looseness of bolt load, instead of the bolt load itself. The practical processes of calibration, real-time monitoring and periodical detection are proposed, considering the application on real construction site. The tests on different configurations of SCBBs prove the reliability of the ultrasonic technique based on change in time-of-flight.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135861367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-16DOI: 10.1177/14759217231173314
Zhengfang Wang, Ming Lei, Junchang Wang, Bo Li, Jing Xu, Yuchen Jiang, Qingmei Sui, Y. Li
Anomaly detection of internal defects in underground engineering structures is critical. This paper proposes an unsupervised deep learning image-to-image translation method tailored for ground penetrating radar (GPR) images. The proposed model can translate real-world GPR images to simulated ones. In this manner, labeling real GPR images is not necessary, and only the target detection model trained on simulated GPR images is required to directly identify defects in real GPR images. The unsupervised deep learning network introduces geometry-consistency constraints into the CycleGAN, which largely prevents the problem of semantic distortion in translation. Validation of the proposed method was performed using GPR data collected in various scenarios using GPR of different center frequencies and manufacturers. Moreover, to verify its adaptability and feasibility for defect recognition, commonly used deep learning-based defect recognition methods, which were trained only on simulated GPR images, were used to detect the translated GPR images. The findings indicate that the type and location of internal defects in translated GPR images can be accurately identified using the proposed method.
{"title":"Unsupervised deep learning-based ground penetrating radar image translation for internal defect recognition of underground engineering structures","authors":"Zhengfang Wang, Ming Lei, Junchang Wang, Bo Li, Jing Xu, Yuchen Jiang, Qingmei Sui, Y. Li","doi":"10.1177/14759217231173314","DOIUrl":"https://doi.org/10.1177/14759217231173314","url":null,"abstract":"Anomaly detection of internal defects in underground engineering structures is critical. This paper proposes an unsupervised deep learning image-to-image translation method tailored for ground penetrating radar (GPR) images. The proposed model can translate real-world GPR images to simulated ones. In this manner, labeling real GPR images is not necessary, and only the target detection model trained on simulated GPR images is required to directly identify defects in real GPR images. The unsupervised deep learning network introduces geometry-consistency constraints into the CycleGAN, which largely prevents the problem of semantic distortion in translation. Validation of the proposed method was performed using GPR data collected in various scenarios using GPR of different center frequencies and manufacturers. Moreover, to verify its adaptability and feasibility for defect recognition, commonly used deep learning-based defect recognition methods, which were trained only on simulated GPR images, were used to detect the translated GPR images. The findings indicate that the type and location of internal defects in translated GPR images can be accurately identified using the proposed method.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47626936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：