Pub Date : 2024-08-28DOI: 10.1101/2023.08.04.552031
Charlie Lang, Ondrej Maxian, Alexander Anneken, Edwin Munro
Studies of PAR polarity have emphasized a paradigm in which mutually antagonistic PAR proteins form complementary polar domains in response to transient cues. A growing body of work suggests that the oligomeric scaffold PAR-3 can form unipolar asymmetries without mutual antagonism, but how it does so is largely unknown. Here we combine single molecule analysis and modeling to show how the interplay of two positive feedback loops promote dynamically stable unipolar PAR-3 asymmetries in early C. elegans embryos. First, the intrinsic dynamics of PAR-3 membrane binding and oligomerization encode negative feedback on PAR-3 dissociation. Second, membrane-bound PAR-3 promotes its own recruitment through a mechanism that requires the anterior polarity proteins CDC-42, PAR-6 and PKC-3. Using a kinetic model tightly constrained by our experimental measurements, we show that these two feedback loops are individually required and jointly sufficient to encode dynamically stable and locally inducible unipolar PAR-3 asymmetries in the absence of posterior inhibition. Given the central role of PAR-3, and the conservation of PAR-3 membrane-binding, oligomerization, and core interactions with PAR-6/aPKC, these results have widespread implications for PAR-mediated polarity in metazoa.
对 PAR 极性的研究强调了一种范式，即相互拮抗的 PAR 蛋白在瞬时线索的作用下形成互补的极性结构域。越来越多的研究表明，低聚物支架 PAR-3 可以在不相互拮抗的情况下形成单极性不对称，但它是如何做到这一点的却大多不为人知。在这里，我们结合单分子分析和建模，展示了两个正反馈环路的相互作用是如何在早期秀丽隐杆线虫胚胎中促进动态稳定的单极 PAR-3 不对称的。首先，PAR-3膜结合和寡聚化的内在动态编码了PAR-3解离的负反馈。其次，膜结合的 PAR-3 通过一种需要前极性蛋白 CDC-42、PAR-6 和 PKC-3 的机制促进其自身的招募。通过一个严格受限于实验测量结果的动力学模型，我们证明了这两个反馈环路是单独需要的，并且足以在没有后部抑制的情况下共同编码动态稳定和局部可诱导的单极 PAR-3 不对称。鉴于 PAR-3 的核心作用，以及 PAR-3 的膜结合、寡聚化和与 PAR-6/aPKC 的核心相互作用的保守性，这些结果对元古宙中 PAR 介导的极性具有广泛的影响。
{"title":"Oligomerization and positive feedback on membrane recruitment encode dynamically stable PAR-3 asymmetries in the <i>C. elegans</i> zygote.","authors":"Charlie Lang, Ondrej Maxian, Alexander Anneken, Edwin Munro","doi":"10.1101/2023.08.04.552031","DOIUrl":"10.1101/2023.08.04.552031","url":null,"abstract":"<p><p>Studies of PAR polarity have emphasized a paradigm in which mutually antagonistic PAR proteins form complementary polar domains in response to transient cues. A growing body of work suggests that the oligomeric scaffold PAR-3 can form unipolar asymmetries without mutual antagonism, but how it does so is largely unknown. Here we combine single molecule analysis and modeling to show how the interplay of two positive feedback loops promote dynamically stable unipolar PAR-3 asymmetries in early <i>C. elegans</i> embryos. First, the intrinsic dynamics of PAR-3 membrane binding and oligomerization encode negative feedback on PAR-3 dissociation. Second, membrane-bound PAR-3 promotes its own recruitment through a mechanism that requires the anterior polarity proteins CDC-42, PAR-6 and PKC-3. Using a kinetic model tightly constrained by our experimental measurements, we show that these two feedback loops are individually required and jointly sufficient to encode dynamically stable and locally inducible unipolar PAR-3 asymmetries in the absence of posterior inhibition. Given the central role of PAR-3, and the conservation of PAR-3 membrane-binding, oligomerization, and core interactions with PAR-6/aPKC, these results have widespread implications for PAR-mediated polarity in metazoa.</p>","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11383301/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76153836","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}
Pub Date : 2023-11-08DOI: 10.1177/14759217231206698
James Wilson, Graeme Manson, Paul Gardner, Robert J Barthorpe
This paper presents a demonstrative application of a forward model-driven approach to structural health monitoring (SHM), incorporating hierarchical validation methods. A key tenet of the approach is that an SHM system can be constructed that is capable of diagnosing damage at the full system level, without full system damage-state data having been used in its development; achieving this would be highly impactful as the system-level damage state data is generally not feasible to acquire (previous SHM methods such as data-driven SHM have been hindered by their dependence on these data). This is achieved by carrying out validation activities on the damage model at the subassembly level of the structure. The particular focus of the present paper is on damage detection and assessment, although the approach offers a natural basis for extension to other damage identification activities such as damage location and prognosis. The present study focuses on two of the key elements of the model-driven approach: validation of the predictive substructure models and their application in the assembled model. The ideas discussed are demonstrated in a case study based on a laboratory-scale truss bridge structure.
{"title":"Hierarchical verification and validation in a forward model-driven structural health monitoring strategy","authors":"James Wilson, Graeme Manson, Paul Gardner, Robert J Barthorpe","doi":"10.1177/14759217231206698","DOIUrl":"https://doi.org/10.1177/14759217231206698","url":null,"abstract":"This paper presents a demonstrative application of a forward model-driven approach to structural health monitoring (SHM), incorporating hierarchical validation methods. A key tenet of the approach is that an SHM system can be constructed that is capable of diagnosing damage at the full system level, without full system damage-state data having been used in its development; achieving this would be highly impactful as the system-level damage state data is generally not feasible to acquire (previous SHM methods such as data-driven SHM have been hindered by their dependence on these data). This is achieved by carrying out validation activities on the damage model at the subassembly level of the structure. The particular focus of the present paper is on damage detection and assessment, although the approach offers a natural basis for extension to other damage identification activities such as damage location and prognosis. The present study focuses on two of the key elements of the model-driven approach: validation of the predictive substructure models and their application in the assembled model. The ideas discussed are demonstrated in a case study based on a laboratory-scale truss bridge structure.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":"9 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135391057","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 : 2023-11-08DOI: 10.1177/14759217231207002
Yi Zeng, Tengsheng Chen, Feng Xiong, Kailai Deng, Yuanqing Xu
Rubber bearings are key components of base-isolated structures, and the monitoring of their damage states is an important task. Aging is a primary concern affecting the service life and isolation effect of rubber bearings. Therefore, this study combined an active sensing method and a data-driven approach to detect rubber aging. A shear stiffness, accelerated aging, and active sensing experiments were conducted on a scaled rubber specimen. As the aging level increased, the shear stiffness of the specimens gradually increased from 116.69 to 127.82 N/mm, but this change was not linear. Due to variations in the degree of aging, discrepancies may arise in the time and frequency domain characteristics of detection signals. However, establishing an empirical relationship between the degree of aging and the features of detection signals were highly challenging. A deep-learning-based data-driven method was used to predict the aging level and shear stiffness using detection signals. The deep learning model successfully detected the aging level, and the prediction accuracy on the validation and test sets reached 99.98%. For the deep learning model for aging level prediction, the optimal input vector length is 4096, the recommended number of layers is 3–5, and the recommended number of cells in each layer is 256–2048. Moreover, the deep learning model also detected the shear stiffness of the rubber specimen. The mean absolute error was 0.27 N/mm on the validation set and 0.28 N/mm on the test set. For the deep learning model for shear stiffness prediction, the optimal input vector length is 4096, and the optimal structure is seven layers with 2048 cells in each layer.
{"title":"Combination of active sensing method and data-driven approach for rubber aging detection","authors":"Yi Zeng, Tengsheng Chen, Feng Xiong, Kailai Deng, Yuanqing Xu","doi":"10.1177/14759217231207002","DOIUrl":"https://doi.org/10.1177/14759217231207002","url":null,"abstract":"Rubber bearings are key components of base-isolated structures, and the monitoring of their damage states is an important task. Aging is a primary concern affecting the service life and isolation effect of rubber bearings. Therefore, this study combined an active sensing method and a data-driven approach to detect rubber aging. A shear stiffness, accelerated aging, and active sensing experiments were conducted on a scaled rubber specimen. As the aging level increased, the shear stiffness of the specimens gradually increased from 116.69 to 127.82 N/mm, but this change was not linear. Due to variations in the degree of aging, discrepancies may arise in the time and frequency domain characteristics of detection signals. However, establishing an empirical relationship between the degree of aging and the features of detection signals were highly challenging. A deep-learning-based data-driven method was used to predict the aging level and shear stiffness using detection signals. The deep learning model successfully detected the aging level, and the prediction accuracy on the validation and test sets reached 99.98%. For the deep learning model for aging level prediction, the optimal input vector length is 4096, the recommended number of layers is 3–5, and the recommended number of cells in each layer is 256–2048. Moreover, the deep learning model also detected the shear stiffness of the rubber specimen. The mean absolute error was 0.27 N/mm on the validation set and 0.28 N/mm on the test set. For the deep learning model for shear stiffness prediction, the optimal input vector length is 4096, and the optimal structure is seven layers with 2048 cells in each layer.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":"37 S163","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135341917","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}
Deep transfer learning is an effective method for unsupervised fault diagnosis of rolling bearings. In some works, the pseudo-label of target domain prediction is used to improve the ability of target domain prediction in transfer learning. However, its validity depends on the quality of pseudo-label generated by the network itself, which is easy to cause the misclassification of the samples. Aiming to this, a dual sample screening (DSS) method based on the information of predicted label changes is proposed in the article, and it is applied to the fault diagnosis of rolling bearings with variable working conditions. DSS combines pre-screening and real-time screening and uses the continuous output of prediction label change information in the training process to improve the network training. It owes to eliminating part of the target domain samples with prediction errors in the stage of network training with pseudo-label. The proposed method improves the stability of the pseudo-label involved in the training and alleviates the negative effects caused by the pseudo-label. The experimental results on Paderborn University dataset show that, compare with the deep transfer learning fault diagnosis method based on pseudo-label cross-entropy, the average diagnostic accuracy of the six transfer tasks using DSS is increased by 5.97%, which effectively improves the fault diagnosis accuracy of rolling bearings.
{"title":"An unsupervised transfer learning approach for rolling bearing fault diagnosis based on dual pseudo-label screening","authors":"Chunran Huo, Weiyang Xu, Quansheng Jiang, Yehu Shen, Qixin Zhu, Qingkui Zhang","doi":"10.1177/14759217231206579","DOIUrl":"https://doi.org/10.1177/14759217231206579","url":null,"abstract":"Deep transfer learning is an effective method for unsupervised fault diagnosis of rolling bearings. In some works, the pseudo-label of target domain prediction is used to improve the ability of target domain prediction in transfer learning. However, its validity depends on the quality of pseudo-label generated by the network itself, which is easy to cause the misclassification of the samples. Aiming to this, a dual sample screening (DSS) method based on the information of predicted label changes is proposed in the article, and it is applied to the fault diagnosis of rolling bearings with variable working conditions. DSS combines pre-screening and real-time screening and uses the continuous output of prediction label change information in the training process to improve the network training. It owes to eliminating part of the target domain samples with prediction errors in the stage of network training with pseudo-label. The proposed method improves the stability of the pseudo-label involved in the training and alleviates the negative effects caused by the pseudo-label. The experimental results on Paderborn University dataset show that, compare with the deep transfer learning fault diagnosis method based on pseudo-label cross-entropy, the average diagnostic accuracy of the six transfer tasks using DSS is increased by 5.97%, which effectively improves the fault diagnosis accuracy of rolling bearings.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":"45 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135390406","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 : 2023-11-08DOI: 10.1177/14759217231206857
Ruonan Ou, Linqing Luo, Kenichi Soga
Material cracking is one of the key mechanisms contributing to structural failure. Distributed fiber optic sensing (DFOS) can measure the strain profile along optical fiber distributively. However, the conventional strain measurement using a Brillouin-DFOS system (Brillouin optical time-domain analysis/reflectometry (BOTDA/R)) has a decimeter-order spatial resolution, making it difficult to measure the highly localized strain generated by a sub-millimeter crack. This paper introduces a crack analysis method based on decomposing the Brillouin scattering spectrum to overcome the spatial resolution induced crack measurement limitation of the BOTDA/R system. The method uses the non-negative least squares algorithm to back-calculate the strain profile within the spatial resolution around each measurement point. The performance of this method is verified by a four point bending test of a brittle slag cement-cement-bentonite beam. The crack width estimation error is improved to ±0.005 mm for a crack as narrow as 0.76 mm.
{"title":"Distributed fiber optic strain sensing for crack detection with Brillouin shift spectrum back analysis","authors":"Ruonan Ou, Linqing Luo, Kenichi Soga","doi":"10.1177/14759217231206857","DOIUrl":"https://doi.org/10.1177/14759217231206857","url":null,"abstract":"Material cracking is one of the key mechanisms contributing to structural failure. Distributed fiber optic sensing (DFOS) can measure the strain profile along optical fiber distributively. However, the conventional strain measurement using a Brillouin-DFOS system (Brillouin optical time-domain analysis/reflectometry (BOTDA/R)) has a decimeter-order spatial resolution, making it difficult to measure the highly localized strain generated by a sub-millimeter crack. This paper introduces a crack analysis method based on decomposing the Brillouin scattering spectrum to overcome the spatial resolution induced crack measurement limitation of the BOTDA/R system. The method uses the non-negative least squares algorithm to back-calculate the strain profile within the spatial resolution around each measurement point. The performance of this method is verified by a four point bending test of a brittle slag cement-cement-bentonite beam. The crack width estimation error is improved to ±0.005 mm for a crack as narrow as 0.76 mm.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":"34 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135342912","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 : 2023-11-07DOI: 10.1177/14759217231204242
Helena Rocha, Paulo Antunes, Ugo Lafont, João P. Nunes
A process and Structural Health Monitoring system was implemented on a Composite Overwrapped Pressure Vessel (COPV) for hydrogen storage at 350 bar to be used in a fuel-cell system of an Unmanned Aerial Vehicle. This work reports the embedment strategy of optical fibre Bragg grating (FBG) sensors to monitor the full life cycle of the vessel, consisting of an aluminium liner and a wound carbon fibre reinforced polymer composite overwrap. A FBG sensing array, bonded on the aluminium liner circumferential section, was covered with a localised unidirectional prepreg composite tape, enabling composite winding and curing monitoring. The sensing array strategy allowed to detect and locate Barely Visible Impact Damage resulting from drop-weight impact tests, based on the ratio of the residual strain amplitude between FBG sensor pairs. Errors as small as 17 mm and up to 56 mm were determined between the predicted and ‘real’ impact locations. To simulate the real-life operational pressure charging and discharging cycles, the COPV was subjected to cycling testing at different pressure ranges. The FBG sensors were able to monitor a total of 20 980 pressure cycles, revealing a linear response to the applied pressure, and remained operational after COPV failure. Furthermore, the FBG sensing array was able to detect the residual plastic strain caused in the aluminium liner by the autofrettage process that the COPV was subjected to prior to pressure cycling, at 600 bar for 2 min, to improve its fatigue performance. This manuscript also reports the COPV structural design by Finite Element Modelling (FEM), its manufacturing process and burst pressure testing for the FEM analysis validation. A small difference of 0.7% was found between the simulated and experimental determined burst pressure of 1061 ± 26 bar.
{"title":"Processing and structural health monitoring of a composite overwrapped pressure vessel for hydrogen storage","authors":"Helena Rocha, Paulo Antunes, Ugo Lafont, João P. Nunes","doi":"10.1177/14759217231204242","DOIUrl":"https://doi.org/10.1177/14759217231204242","url":null,"abstract":"A process and Structural Health Monitoring system was implemented on a Composite Overwrapped Pressure Vessel (COPV) for hydrogen storage at 350 bar to be used in a fuel-cell system of an Unmanned Aerial Vehicle. This work reports the embedment strategy of optical fibre Bragg grating (FBG) sensors to monitor the full life cycle of the vessel, consisting of an aluminium liner and a wound carbon fibre reinforced polymer composite overwrap. A FBG sensing array, bonded on the aluminium liner circumferential section, was covered with a localised unidirectional prepreg composite tape, enabling composite winding and curing monitoring. The sensing array strategy allowed to detect and locate Barely Visible Impact Damage resulting from drop-weight impact tests, based on the ratio of the residual strain amplitude between FBG sensor pairs. Errors as small as 17 mm and up to 56 mm were determined between the predicted and ‘real’ impact locations. To simulate the real-life operational pressure charging and discharging cycles, the COPV was subjected to cycling testing at different pressure ranges. The FBG sensors were able to monitor a total of 20 980 pressure cycles, revealing a linear response to the applied pressure, and remained operational after COPV failure. Furthermore, the FBG sensing array was able to detect the residual plastic strain caused in the aluminium liner by the autofrettage process that the COPV was subjected to prior to pressure cycling, at 600 bar for 2 min, to improve its fatigue performance. This manuscript also reports the COPV structural design by Finite Element Modelling (FEM), its manufacturing process and burst pressure testing for the FEM analysis validation. A small difference of 0.7% was found between the simulated and experimental determined burst pressure of 1061 ± 26 bar.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":"81 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135540110","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 : 2023-11-07DOI: 10.1177/14759217231203022
Ahmed Aseem, Ching Tai Ng
This study utilizes the linear and nonlinear features of guided waves (GWs) for detecting and evaluating heat damage in reinforced concrete (RC) beams. The RC beams with embedded sensors attached at rebar ends are experimentally studied using longitudinal GW at 200 kHz after heating the specimens in a furnace from 100°C to 300°C. For the studies investigating the effect of heat damage on the RC beams beyond 300°C, the rebar ends are exposed outside the concrete so that the longitudinal transducers can be attached there. These specimens are then experimentally studied using GW with an excitation frequency of 100 kHz. In this study, the RC beams are prepared as fully bonded and debonded specimens. The experimental study shows that heat damage in the RC beams causes debonding between rebar and concrete enabling GW signal to generate second harmonics. The experimental study also discussed the linear features of GW, which shows that the amplitude of the GW signal increases with elevated temperatures in the RC beams. To distinguish material nonlinearity and contact nonlinearity, two types of nonlinear parameters are defined in this study. The nonlinear parameter due to the contact acoustic nonlinearity effect in the RC beams is defined as β, whereas the nonlinear parameter due to material nonlinearity is defined as β m . The study shows that β m is negligible in comparison to β at relevant heated temperatures. With the increase in temperature, the nonlinear parameter β is significantly increased at elevated temperatures. The peak amplitude of the nonlinear parameter β is observed at the maximum heated temperature 800°C for both bonded and debonded specimens.
{"title":"Detection and evaluation of heat damage in reinforced concrete beams using linear and nonlinear guided waves","authors":"Ahmed Aseem, Ching Tai Ng","doi":"10.1177/14759217231203022","DOIUrl":"https://doi.org/10.1177/14759217231203022","url":null,"abstract":"This study utilizes the linear and nonlinear features of guided waves (GWs) for detecting and evaluating heat damage in reinforced concrete (RC) beams. The RC beams with embedded sensors attached at rebar ends are experimentally studied using longitudinal GW at 200 kHz after heating the specimens in a furnace from 100°C to 300°C. For the studies investigating the effect of heat damage on the RC beams beyond 300°C, the rebar ends are exposed outside the concrete so that the longitudinal transducers can be attached there. These specimens are then experimentally studied using GW with an excitation frequency of 100 kHz. In this study, the RC beams are prepared as fully bonded and debonded specimens. The experimental study shows that heat damage in the RC beams causes debonding between rebar and concrete enabling GW signal to generate second harmonics. The experimental study also discussed the linear features of GW, which shows that the amplitude of the GW signal increases with elevated temperatures in the RC beams. To distinguish material nonlinearity and contact nonlinearity, two types of nonlinear parameters are defined in this study. The nonlinear parameter due to the contact acoustic nonlinearity effect in the RC beams is defined as β, whereas the nonlinear parameter due to material nonlinearity is defined as β m . The study shows that β m is negligible in comparison to β at relevant heated temperatures. With the increase in temperature, the nonlinear parameter β is significantly increased at elevated temperatures. The peak amplitude of the nonlinear parameter β is observed at the maximum heated temperature 800°C for both bonded and debonded specimens.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":"79 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135539975","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}
An accurate prediction of the future service state of long-span bridges is crucial for the structural reliability evaluation, maintenance planning, and further life-cycle cost analysis. By extending the average conditional exceedance rate (ACER) statistical model and applying input–output data collected through a structural health monitoring (SHM) system, this paper proposes a novel methodology for predicting the future service state of long-span bridges. The advantages lie in the consideration of the main excitation load as the structural input and the strain response of the bridge as the output. Therefore, a mapping relationship between the extreme excitation load and extreme strain could be established, and the future service state of long-span bridges could be predicted. The proposed method comprises three steps: (1) extraction of the ambient temperature-induced strain and vehicle-induced strain from the measured strain series through the SHM system using the baseline estimation and denoising with sparsity (BEADS) method, (2) establishing statistical models of the extreme values of different excitations (input) and structural strains (output) using a cascade of conditioning approximations and the ACER to obtain the tail trend of the data and extrapolating it, and (3) establishing a functional relationship between the input and output extreme values based on the same conditions of the regression period at the target prediction level, after which the future service state of long-span bridges can be predicted. The proposed method is applied to a case study of the Jinchao Bridge in Guangdong Province, China, and the results are expected to provide a scientific reference for the design of new bridges and in the maintenance of existing ones in service.
{"title":"Extreme state prediction of long-span bridges using extended ACER method","authors":"Liping Zhang, Liming Zhou, Jianqing Bu, Fei Xu, Bin Wei, Zhaofeng Xu, Cunbao Zhao, Yiqiang Li, Wei Chai, Shuanglin Guo, Yongding Tian","doi":"10.1177/14759217231206531","DOIUrl":"https://doi.org/10.1177/14759217231206531","url":null,"abstract":"An accurate prediction of the future service state of long-span bridges is crucial for the structural reliability evaluation, maintenance planning, and further life-cycle cost analysis. By extending the average conditional exceedance rate (ACER) statistical model and applying input–output data collected through a structural health monitoring (SHM) system, this paper proposes a novel methodology for predicting the future service state of long-span bridges. The advantages lie in the consideration of the main excitation load as the structural input and the strain response of the bridge as the output. Therefore, a mapping relationship between the extreme excitation load and extreme strain could be established, and the future service state of long-span bridges could be predicted. The proposed method comprises three steps: (1) extraction of the ambient temperature-induced strain and vehicle-induced strain from the measured strain series through the SHM system using the baseline estimation and denoising with sparsity (BEADS) method, (2) establishing statistical models of the extreme values of different excitations (input) and structural strains (output) using a cascade of conditioning approximations and the ACER to obtain the tail trend of the data and extrapolating it, and (3) establishing a functional relationship between the input and output extreme values based on the same conditions of the regression period at the target prediction level, after which the future service state of long-span bridges can be predicted. The proposed method is applied to a case study of the Jinchao Bridge in Guangdong Province, China, and the results are expected to provide a scientific reference for the design of new bridges and in the maintenance of existing ones in service.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":"53 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135539434","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 : 2023-11-07DOI: 10.1177/14759217231202544
Xiangdong He, Xuan Zhu
Engineered wood or mass timber has gained increasing popularity in building construction, and layered engineered wood is a major category of mass timber design since it enables manufacturing structural members with a wide range of geometry. Thus, there is a potential rising demand for structural health monitoring on engineered wood-based structural members and buildings. This study investigates the feasibility of using an important and practical acoustic emission (AE) method for damage localization, specifically two-dimensional (2D) AE source localization, in a representative layered engineered wood sample, namely laminated veneer lumber (LVL) plate. While 2D AE source localization is generally straightforward in isotropic materials, the problem becomes challenging for anisotropic materials with angle-dependent wave velocities. It is even more complicated if heterogeneity involves, which turns out to be the case for layered engineered wood. In this study, we rely on the AE feature of difference in time of arrival (dTOA) and develop three methods to address the challenges of 2D AE source localization raised by anisotropy and heterogeneity in an LVL plate. The benchmark velocity profile method (VPM) is first implemented in an LVL plate. With knowledge of the angle-dependent velocity, the source location predictions by the VPM are generally erroneous even with predicted source location outside of the region of interest. Furthermore, the general regression neural network (GRNN) is developed using different combinations of dTOA components, resulting in improved prediction performance. Third, the Gaussian process regression (GPR) is developed by maximizing the marginal likelihood of the training dataset. Moreover, to lessen the computation burden, the lower bound of the logarithm likelihood of the whole models is derived and decomposed through Jensen’s inequality and Bayes’ theorem, providing the theoretical background for training models with different combinations of dTOAs individually.
{"title":"Two-dimensional acoustic emission source localization on layered engineered wood by machine learning: a case study of laminated veneer lumber plate structure","authors":"Xiangdong He, Xuan Zhu","doi":"10.1177/14759217231202544","DOIUrl":"https://doi.org/10.1177/14759217231202544","url":null,"abstract":"Engineered wood or mass timber has gained increasing popularity in building construction, and layered engineered wood is a major category of mass timber design since it enables manufacturing structural members with a wide range of geometry. Thus, there is a potential rising demand for structural health monitoring on engineered wood-based structural members and buildings. This study investigates the feasibility of using an important and practical acoustic emission (AE) method for damage localization, specifically two-dimensional (2D) AE source localization, in a representative layered engineered wood sample, namely laminated veneer lumber (LVL) plate. While 2D AE source localization is generally straightforward in isotropic materials, the problem becomes challenging for anisotropic materials with angle-dependent wave velocities. It is even more complicated if heterogeneity involves, which turns out to be the case for layered engineered wood. In this study, we rely on the AE feature of difference in time of arrival (dTOA) and develop three methods to address the challenges of 2D AE source localization raised by anisotropy and heterogeneity in an LVL plate. The benchmark velocity profile method (VPM) is first implemented in an LVL plate. With knowledge of the angle-dependent velocity, the source location predictions by the VPM are generally erroneous even with predicted source location outside of the region of interest. Furthermore, the general regression neural network (GRNN) is developed using different combinations of dTOA components, resulting in improved prediction performance. Third, the Gaussian process regression (GPR) is developed by maximizing the marginal likelihood of the training dataset. Moreover, to lessen the computation burden, the lower bound of the logarithm likelihood of the whole models is derived and decomposed through Jensen’s inequality and Bayes’ theorem, providing the theoretical background for training models with different combinations of dTOAs individually.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":"58 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135539707","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}
The health indicator (HI) plays a crucial role in the condition monitoring of the rolling bearing. However, most existing HIs exhibit significant fluctuations when the speed changes. To address the issue, this paper proposes a new HI namely reweighted fault impact strength (RFIS)-HI. First, sub-signals are obtained through a frequency division strategy, and corresponding resampled signals are derived using order tracking. Second, the average impact peak in the time domain is acquired to measure the impact of the signal. According to fault characteristic order (FCO), the ratio of FCOs summation to noise amplitude in the frequency domain is obtained to measure periodicity. Then, the FISgram is constructed for selecting the optimal frequency band. To better quantify the degradation degree of the bearing, different weights are assigned and optimized for constructing RFIS. Finally, the influence of rotational speed on RFIS is eliminated by utilizing prior knowledge. Taking the first 10% of the dataset as baseline data, RFIS-HI is constructed through relative similarity. In this paper, a bearing dataset under time-varying speed conditions and an XJTU-SY dataset are used for verification. Results show that the proposed HI can achieve better trendability, scale similarity, and stability.
{"title":"RFIS-HI: a new health indicator for quantitative condition monitoring of the bearing under variable speed conditions","authors":"Weipeng Ma, Yaoxiang Yu, Liang Guo, Mengui Qian, Hongli Gao","doi":"10.1177/14759217231203244","DOIUrl":"https://doi.org/10.1177/14759217231203244","url":null,"abstract":"The health indicator (HI) plays a crucial role in the condition monitoring of the rolling bearing. However, most existing HIs exhibit significant fluctuations when the speed changes. To address the issue, this paper proposes a new HI namely reweighted fault impact strength (RFIS)-HI. First, sub-signals are obtained through a frequency division strategy, and corresponding resampled signals are derived using order tracking. Second, the average impact peak in the time domain is acquired to measure the impact of the signal. According to fault characteristic order (FCO), the ratio of FCOs summation to noise amplitude in the frequency domain is obtained to measure periodicity. Then, the FISgram is constructed for selecting the optimal frequency band. To better quantify the degradation degree of the bearing, different weights are assigned and optimized for constructing RFIS. Finally, the influence of rotational speed on RFIS is eliminated by utilizing prior knowledge. Taking the first 10% of the dataset as baseline data, RFIS-HI is constructed through relative similarity. In this paper, a bearing dataset under time-varying speed conditions and an XJTU-SY dataset are used for verification. Results show that the proposed HI can achieve better trendability, scale similarity, and stability.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":"82 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135540104","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}
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