L. Fazzi, N. Dias, M. Hołyńska, A. Tighe, R. Rampini, R. Groves
This research demonstrates the promising abilities of a tilted Fibre Bragg Grating (TFBG) sensor for monitoring the status of a silicone adhesive during a simulated space environment exposure. The silicone is used as adhesive between two thin cover glasses and the TFBG is embedded into the polymer such that it is fully enclosed. Then, the sample is exposed to standard space environment conditions in a vacuum chamber simulated by creating a high vacuum (1.3×10-6 mbar) and thermal cycles between -120 ℃ to 190 ℃. The TFBG spectra recorded during the exposure were demodulated to obtain the wavelength shifts of the Bragg and Ghost peaks and the envelope area of the upper and lower cladding modes resonances peaks. This will allow the thermomechanical and the refractive index (RI) variations of the silicone to be measured during the testing. In particular, the silicone RI depends on the material chemical and physical state and its thermal history, and the TFBG envelope area is sensitive to these RI changes. Hence, the envelope area of the TFBG spectrum can be used to obtain information on the evolution of the silicone adhesive during the test. The resulting trend of the selected peak wavelengths variation and envelope area were used to detect a variation of the degradation state of the material.
{"title":"MONITORING OF THERMAL AGEING CYCLES OF A SILICONE ADHESIVE IN A SIMULATED SPACE ENVIRONMENT USING EMBEDDED TFBG SENSORS","authors":"L. Fazzi, N. Dias, M. Hołyńska, A. Tighe, R. Rampini, R. Groves","doi":"10.12783/shm2021/36351","DOIUrl":"https://doi.org/10.12783/shm2021/36351","url":null,"abstract":"This research demonstrates the promising abilities of a tilted Fibre Bragg Grating (TFBG) sensor for monitoring the status of a silicone adhesive during a simulated space environment exposure. The silicone is used as adhesive between two thin cover glasses and the TFBG is embedded into the polymer such that it is fully enclosed. Then, the sample is exposed to standard space environment conditions in a vacuum chamber simulated by creating a high vacuum (1.3×10-6 mbar) and thermal cycles between -120 ℃ to 190 ℃. The TFBG spectra recorded during the exposure were demodulated to obtain the wavelength shifts of the Bragg and Ghost peaks and the envelope area of the upper and lower cladding modes resonances peaks. This will allow the thermomechanical and the refractive index (RI) variations of the silicone to be measured during the testing. In particular, the silicone RI depends on the material chemical and physical state and its thermal history, and the TFBG envelope area is sensitive to these RI changes. Hence, the envelope area of the TFBG spectrum can be used to obtain information on the evolution of the silicone adhesive during the test. The resulting trend of the selected peak wavelengths variation and envelope area were used to detect a variation of the degradation state of the material.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132517692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The article investigates multiple debonding in a glass fibre reinforced polymer structure (GFRPS) using nondestructive testing (NDT) based on ultrasonic guided waves (UGW) propagation. The piezoelectric transducers (PZT) attached to the material excite the UGW and the registered time signals are analyzed. The debonding are in various depths of the GFRPS. It was assessed using NDT-based tools. The presence of debonding and wave scattering based on the depth and location in GFRPS is studied. This is followed by using signal processing methods to visualize and analyze the different characteristics of the ultrasonic waves before and after the debonding. Thus an experimental-based approach to identify the debonding inside the GFRPS and its influence is studied.
{"title":"DEBONDING ANALYSIS OF COMPOSITE MATERIAL USING ULTRASONIC WAVE-BASED NDT METHODS","authors":"K. Balasubramaniam, T. Wandowski, P. Malinowski","doi":"10.12783/shm2021/36313","DOIUrl":"https://doi.org/10.12783/shm2021/36313","url":null,"abstract":"The article investigates multiple debonding in a glass fibre reinforced polymer structure (GFRPS) using nondestructive testing (NDT) based on ultrasonic guided waves (UGW) propagation. The piezoelectric transducers (PZT) attached to the material excite the UGW and the registered time signals are analyzed. The debonding are in various depths of the GFRPS. It was assessed using NDT-based tools. The presence of debonding and wave scattering based on the depth and location in GFRPS is studied. This is followed by using signal processing methods to visualize and analyze the different characteristics of the ultrasonic waves before and after the debonding. Thus an experimental-based approach to identify the debonding inside the GFRPS and its influence is studied.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114516298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, in order to efficiently distinguish the influence of both interface debonding defect and the mesoscale structure of concrete core on the stress wave field and the response of an embedded Piezoelectric-lead-zirconate-titanate (PZT) sensor in rectangular concrete filled steel tube (RCFST) members, a two dimensional (2D) mesoscale numerical concrete with homogenization approach considering the random distribution of circle, ellipse and irregular polygon aggregates is proposed firstly. Then, mesoscale homogenization simulation on stress wave fields within the cross-sections of RCFST members with and without interface debonding defects are carried out, respectively. The effect of both mesoscale structure of concrete core and the interface debonding defect on the stress wave field of each member is discussed. Therefore, the time-domain response of the embedded PZT sensor in the RCFST members coupled with PZT patches under sweep frequency excitation signal is determined and compared when both mesoscale models and their homogenization models are used. The sensitivity of the wavelet packet energy of the embedded PZT sensor response on the variation of both mesoscale structure of concrete core and the dimension of interface debonding defects is investigated. The detectability of interface debonding using stress wave measurement is illustrated efficiently with the proposed mesoscale homogenization modelling approach even the mesoscale structure of the concrete core is considered.
{"title":"MESOSCALE HOMOGENIZATION NUMERICAL STUDY ON THE SIGNIFICANCE OF CONCRETE MESOSCALE STRUCTURE ON WAVE PROPAGATION OF RECTANGULAR RCFSTS WITH DEBONDING","authors":"Jiang Wang, Bing-Lei Xu, Hongbing Chen, H. Ge","doi":"10.12783/shm2021/36319","DOIUrl":"https://doi.org/10.12783/shm2021/36319","url":null,"abstract":"In this paper, in order to efficiently distinguish the influence of both interface debonding defect and the mesoscale structure of concrete core on the stress wave field and the response of an embedded Piezoelectric-lead-zirconate-titanate (PZT) sensor in rectangular concrete filled steel tube (RCFST) members, a two dimensional (2D) mesoscale numerical concrete with homogenization approach considering the random distribution of circle, ellipse and irregular polygon aggregates is proposed firstly. Then, mesoscale homogenization simulation on stress wave fields within the cross-sections of RCFST members with and without interface debonding defects are carried out, respectively. The effect of both mesoscale structure of concrete core and the interface debonding defect on the stress wave field of each member is discussed. Therefore, the time-domain response of the embedded PZT sensor in the RCFST members coupled with PZT patches under sweep frequency excitation signal is determined and compared when both mesoscale models and their homogenization models are used. The sensitivity of the wavelet packet energy of the embedded PZT sensor response on the variation of both mesoscale structure of concrete core and the dimension of interface debonding defects is investigated. The detectability of interface debonding using stress wave measurement is illustrated efficiently with the proposed mesoscale homogenization modelling approach even the mesoscale structure of the concrete core is considered.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125033914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this study, we proposed a methodology for a technique to simultaneously estimate the mechanical parameters of vehicles and bridges and road surface roughness from vehicle vibration. The MCMC (Markov chain Monte Carlo) method was used to search for parameters from vehicle vibrations generated by numerical simulation. The results obtained are estimable even in the presence of bridge stiffness reduction, which suggests the possibility of bridge damage detection using vehicle vibration.
在本研究中，我们提出了一种从车辆振动中同时估计车辆和桥梁力学参数和路面粗糙度的技术方法。利用MCMC (Markov chain Monte Carlo)方法从数值模拟产生的车辆振动中搜索参数。即使在桥梁刚度降低的情况下，得到的结果也是可估计的，这表明利用车辆振动进行桥梁损伤检测是可能的。
{"title":"NUMERICAL STUDIES ON BRIDGE INSPECTION USING DATA OBTAINED FROM SENSORS ON VEHICLE","authors":"Kyosuke Yamamoto, Sachiyo Fujiwara, Kento Tsukada, Ryota Shin, Yukihiko Okada","doi":"10.12783/shm2021/36324","DOIUrl":"https://doi.org/10.12783/shm2021/36324","url":null,"abstract":"In this study, we proposed a methodology for a technique to simultaneously estimate the mechanical parameters of vehicles and bridges and road surface roughness from vehicle vibration. The MCMC (Markov chain Monte Carlo) method was used to search for parameters from vehicle vibrations generated by numerical simulation. The results obtained are estimable even in the presence of bridge stiffness reduction, which suggests the possibility of bridge damage detection using vehicle vibration.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125399183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Long-span prestressed steel structures, known for its light weight and well performance, are world-wide used and developed nowadays. The constructional errors are challenges to long-span prestressed structures with considerations of the constructional precisions, prestressing degrees, and global stability respectively. The 3D laser scanning technique is applied in the structural health monitoring which is used in the long-span prestressed structures as well. However, a gap exists between measurement point clouds and structural assessments of long-span prestressed steel structures due to the complexity and volume of scanning data. The research targets at the real-time global stability assessments of long-span prestressed steel structures, including characterization of constructional errors from in-situ measurements, establishment of probabilistic model for constructional errors’ sensitivity study, and real-time constructional errors’ analysis. This work emphasizes current research progress on constructional errors’ characterization and data analysis from the in-situ measurements. The in-situ measurement data obtained from two projects of long-span prestressed steel structures charged by the researchers. The constructional errors are smartly recognized from the geometric deviations in comparison with nominal BIM models. The recognized data are then characterized from the convolutional neural network algorithm and statistically analyzed as well. The statistical data is used for the constructional-error sensitivity study where failure probabilities and collapse modes will be carefully evaluated. The research bridges the structural health information and structural stability assessments of long-span prestressed steel structures. In turn, it lays a solid foundation of real-time instant global stability assessments of long-span prestressed steel structures in a long term.
{"title":"CHARACTERIZATION AND ANALYSIS OF CONSTRUCTIONAL ERRORS OF LONG-SPAN PRESTRESSED STEEL STRUCTURES BASED ON IN-SITU MEASUREMENTS","authors":"A. Zhang, Jie Wang, Xi Zhao, Yanxia. Zhang","doi":"10.12783/shm2021/36260","DOIUrl":"https://doi.org/10.12783/shm2021/36260","url":null,"abstract":"Long-span prestressed steel structures, known for its light weight and well performance, are world-wide used and developed nowadays. The constructional errors are challenges to long-span prestressed structures with considerations of the constructional precisions, prestressing degrees, and global stability respectively. The 3D laser scanning technique is applied in the structural health monitoring which is used in the long-span prestressed structures as well. However, a gap exists between measurement point clouds and structural assessments of long-span prestressed steel structures due to the complexity and volume of scanning data. The research targets at the real-time global stability assessments of long-span prestressed steel structures, including characterization of constructional errors from in-situ measurements, establishment of probabilistic model for constructional errors’ sensitivity study, and real-time constructional errors’ analysis. This work emphasizes current research progress on constructional errors’ characterization and data analysis from the in-situ measurements. The in-situ measurement data obtained from two projects of long-span prestressed steel structures charged by the researchers. The constructional errors are smartly recognized from the geometric deviations in comparison with nominal BIM models. The recognized data are then characterized from the convolutional neural network algorithm and statistically analyzed as well. The statistical data is used for the constructional-error sensitivity study where failure probabilities and collapse modes will be carefully evaluated. The research bridges the structural health information and structural stability assessments of long-span prestressed steel structures. In turn, it lays a solid foundation of real-time instant global stability assessments of long-span prestressed steel structures in a long term.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127808257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ke Yu, Jingying Yang, Jun Li, Jiangpeng Shu, A. Strauss, K. Zandi
Cracks in reinforced concrete (RC) structures can be detrimental if they grow beyond the limits, so it is important to include their influences in the structural health monitoring method. For example, the impact of shear cracks on the structural performance of RC beams remains unknown. To fully understand the impact of cracks, judging by crack patterns alone is insufficient. This study focuses on the initial shear cracks and investigates whether shear cracks in different locations reduce the load-bearing capacity of beams at different levels. Four sets of beams that exhibited different types of initial shear cracks were cast, including (i) reference beams with no cracks, (ii) beams with inclined cracks in the bending span, (iii) beams with shear cracks in the shear span, and (iv) beams with pre-defined notches in the shear span. The initial cracks were carefully documented, and all the specimens were tested in four-point bending tests. The test results of beams with initial cracks or notches were compared with reference beams. The comparison indicated that the initial cracks in the bending span had limited influence on the load-bearing capacity, while the cracks in the shear span significantly reduced the ultimate load of the beams. Also, the cracks in the shear span were likely to further propagate to become the dominant cracks.
{"title":"SHEAR TESTS OF REINFORCED CONCRETE BEAMS WITH THE INFLUENCE OF INITIAL SHEAR CRACKS","authors":"Ke Yu, Jingying Yang, Jun Li, Jiangpeng Shu, A. Strauss, K. Zandi","doi":"10.12783/shm2021/36251","DOIUrl":"https://doi.org/10.12783/shm2021/36251","url":null,"abstract":"Cracks in reinforced concrete (RC) structures can be detrimental if they grow beyond the limits, so it is important to include their influences in the structural health monitoring method. For example, the impact of shear cracks on the structural performance of RC beams remains unknown. To fully understand the impact of cracks, judging by crack patterns alone is insufficient. This study focuses on the initial shear cracks and investigates whether shear cracks in different locations reduce the load-bearing capacity of beams at different levels. Four sets of beams that exhibited different types of initial shear cracks were cast, including (i) reference beams with no cracks, (ii) beams with inclined cracks in the bending span, (iii) beams with shear cracks in the shear span, and (iv) beams with pre-defined notches in the shear span. The initial cracks were carefully documented, and all the specimens were tested in four-point bending tests. The test results of beams with initial cracks or notches were compared with reference beams. The comparison indicated that the initial cracks in the bending span had limited influence on the load-bearing capacity, while the cracks in the shear span significantly reduced the ultimate load of the beams. Also, the cracks in the shear span were likely to further propagate to become the dominant cracks.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130866106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sam Choppala, Poojhita Vurturbadarinath, M. Chierichetti, Fatemeh Davoudi Khaki
Current maintenance intervals of mechanical systems are scheduled a priori based on the life of the system, resulting in expensive maintenance scheduling, and often undermining the safety of passengers. This problem is particularly relevant in the development of autonomous vehicles, especially in the concept of urban air mobility. The actual usage of the vehicle will be used to predict stresses in the structure and therefore to define maintenance scheduling. Supervised regression machine learning algorithms are used to map a reduced set of data coming from real-time measurements of a structure into a detailed/high-fidelity finite element analysis (FEA) model of the same system, therefore creating a surrogate of the finite element model. The paper will present applications of the approach to a one-dimensional beam structure, modeled with finite element methods. Based on the response of the beam measured at a few reference locations, the surrogate finite element approach determines the entire response of the beam at all spatial locations (displacements, velocities, accelerations, stresses, strains) using neural networks. The FEA-based machine learning approach estimates the stress distribution over the entire system during operations, thus improving the ability to define ad-hoc, safe and efficient maintenance procedures. The effect of type of input features and output and their relationship on the performance of the neural network is discussed, as well as the effect of the beam boundary conditions on network performance.
{"title":"APPLICATIONS OF SURROGATE FINITE ELEMENT MACHINE LEARNING APPROACH FOR STRUCTURAL MONITORING","authors":"Sam Choppala, Poojhita Vurturbadarinath, M. Chierichetti, Fatemeh Davoudi Khaki","doi":"10.12783/shm2021/36284","DOIUrl":"https://doi.org/10.12783/shm2021/36284","url":null,"abstract":"Current maintenance intervals of mechanical systems are scheduled a priori based on the life of the system, resulting in expensive maintenance scheduling, and often undermining the safety of passengers. This problem is particularly relevant in the development of autonomous vehicles, especially in the concept of urban air mobility. The actual usage of the vehicle will be used to predict stresses in the structure and therefore to define maintenance scheduling. Supervised regression machine learning algorithms are used to map a reduced set of data coming from real-time measurements of a structure into a detailed/high-fidelity finite element analysis (FEA) model of the same system, therefore creating a surrogate of the finite element model. The paper will present applications of the approach to a one-dimensional beam structure, modeled with finite element methods. Based on the response of the beam measured at a few reference locations, the surrogate finite element approach determines the entire response of the beam at all spatial locations (displacements, velocities, accelerations, stresses, strains) using neural networks. The FEA-based machine learning approach estimates the stress distribution over the entire system during operations, thus improving the ability to define ad-hoc, safe and efficient maintenance procedures. The effect of type of input features and output and their relationship on the performance of the neural network is discussed, as well as the effect of the beam boundary conditions on network performance.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123557188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Poole, P. Gardner, N. Dervilis, L. Bull, K. Worden
The practical application of structural health monitoring (SHM) is often limited by the unavailability of labelled data. Transfer learning - specifically in the form of domain adaptation – gives rise to the possibility of leveraging information from a population of physical or numerical structures, by inferring a mapping that aligns the feature spaces. There are a number of approaches to domain adaptation that minimise some distribution discrepancy metric. However, it is found that under high initial distribution discrepancy, these methods may be prone to performance degradation. In this paper, guided normalisation is proposed as a solution to the initial distribution discrepancy problem. Several case studies demonstrate how normalisation can itself perform powerful adaptation and facilitate further adaptation, with more sophisticated domain adaptation methods.
{"title":"ON NORMALISATION FOR DOMAIN ADAPTATION IN POPULATION-BASED STRUCTURAL HEALTH MONITORING","authors":"J. Poole, P. Gardner, N. Dervilis, L. Bull, K. Worden","doi":"10.12783/shm2021/36280","DOIUrl":"https://doi.org/10.12783/shm2021/36280","url":null,"abstract":"The practical application of structural health monitoring (SHM) is often limited by the unavailability of labelled data. Transfer learning - specifically in the form of domain adaptation – gives rise to the possibility of leveraging information from a population of physical or numerical structures, by inferring a mapping that aligns the feature spaces. There are a number of approaches to domain adaptation that minimise some distribution discrepancy metric. However, it is found that under high initial distribution discrepancy, these methods may be prone to performance degradation. In this paper, guided normalisation is proposed as a solution to the initial distribution discrepancy problem. Several case studies demonstrate how normalisation can itself perform powerful adaptation and facilitate further adaptation, with more sophisticated domain adaptation methods.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128412426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Machine learning algorithms have been extensively used to implement structural health monitoring (SHM) systems to detect the occurrence of damage within a structure. To obtain the most effective data for SHM decision making, it is desirable to perform sensor placement optimisation (SPO), with a particular focus on damage identification. However, comparatively little attention has been paid to systematic assessment criteria appropriate to the design of a sensor system for SHM. This paper focusses on studying the evaluation criteria at different stages of a sensor-system design process, ranging from the measurement of linear associations to the detailed evaluation of the overall probability of correct classification. The effects of the investigated criteria are demonstrated using a physics-based model with uncertain parameters related to material proprieties. Predictions of the dynamic response of the structure in different states of interest are used to derive features.
{"title":"ASSESSMENT CRITERIA FOR OPTIMAL SENSOR PLACEMENT FOR A STRUCTURAL HEALTH MONITORING SYSTEM","authors":"Tingna Wang, D. Wagg, K. Worden, R. Barthorpe","doi":"10.12783/shm2021/36279","DOIUrl":"https://doi.org/10.12783/shm2021/36279","url":null,"abstract":"Machine learning algorithms have been extensively used to implement structural health monitoring (SHM) systems to detect the occurrence of damage within a structure. To obtain the most effective data for SHM decision making, it is desirable to perform sensor placement optimisation (SPO), with a particular focus on damage identification. However, comparatively little attention has been paid to systematic assessment criteria appropriate to the design of a sensor system for SHM. This paper focusses on studying the evaluation criteria at different stages of a sensor-system design process, ranging from the measurement of linear associations to the detailed evaluation of the overall probability of correct classification. The effects of the investigated criteria are demonstrated using a physics-based model with uncertain parameters related to material proprieties. Predictions of the dynamic response of the structure in different states of interest are used to derive features.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128924943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Railway tracks are used as mass transportation system for transporting large number of people and goods from place-to-place to keep the economy running smoothly. Hence it is inevitable to keep the tracks healthy for safe and on-time movement of trains. Traintracks are complex systems that contain ballast, sleepers, fasteners and rails. Therefore, monitoring only one/two elements (e.g., ballast/train-track) will not provide enough information to understand the overall performance of the railway tracks. To tackle such issue, herein a sensor fusion i.e., accelerometers, fiber-optic sensors, strategy is adopted and sensors are placed at different locations of a real rail-track. In order to measure the vibration signal four accelerometers are employed, first one is placed on the rail (between two sleepers), second one is installed on the rail but above the sleeper, third one is exactly on the sleeper, and last one is on the precast railway trough. In a first step, the investigation has focused into accelerometers data only. The tests are performed for the following loading conditions: (i) shaking the track via an APS400 type shaker, (ii) hitting the track by an impact hammer, and (iii) by passing a real train on the track. The time-series data are analyzed and the frequencies and spectrums are estimated via the use of fast Fourier transform (FFT). The changes of frequencies of the tested rail-track at different locations due to the various loading conditions are observed. In a later step, an autoregressive type time-series model has been developed and validated where the initially obtained results show good agreement with the measured data. The current findings will assist to monitor the rail-track for any further changes.
{"title":"MONITORING OF RAIL-TRACKS BASED ON MEASURED ACCELERATION DATA","authors":"M. S. Miah, W. Lienhart","doi":"10.12783/shm2021/36244","DOIUrl":"https://doi.org/10.12783/shm2021/36244","url":null,"abstract":"Railway tracks are used as mass transportation system for transporting large number of people and goods from place-to-place to keep the economy running smoothly. Hence it is inevitable to keep the tracks healthy for safe and on-time movement of trains. Traintracks are complex systems that contain ballast, sleepers, fasteners and rails. Therefore, monitoring only one/two elements (e.g., ballast/train-track) will not provide enough information to understand the overall performance of the railway tracks. To tackle such issue, herein a sensor fusion i.e., accelerometers, fiber-optic sensors, strategy is adopted and sensors are placed at different locations of a real rail-track. In order to measure the vibration signal four accelerometers are employed, first one is placed on the rail (between two sleepers), second one is installed on the rail but above the sleeper, third one is exactly on the sleeper, and last one is on the precast railway trough. In a first step, the investigation has focused into accelerometers data only. The tests are performed for the following loading conditions: (i) shaking the track via an APS400 type shaker, (ii) hitting the track by an impact hammer, and (iii) by passing a real train on the track. The time-series data are analyzed and the frequencies and spectrums are estimated via the use of fast Fourier transform (FFT). The changes of frequencies of the tested rail-track at different locations due to the various loading conditions are observed. In a later step, an autoregressive type time-series model has been developed and validated where the initially obtained results show good agreement with the measured data. The current findings will assist to monitor the rail-track for any further changes.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115309589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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