With the rapid development of China’s chemical industry in coastal areas, the transportation of hazardous chemicals has become increasingly busy. Due to the complexity of marine environment, there are a large number of safety hazards during the transportation of hazardous chemicals. Based on accident statistics, the main factors affecting the transportation safety of hazardous chemicals is analyzed, including strong wind and reduced adhesion coefficient caused by rain and snow. Further, a vehicle stability analysis model considering these factors is established to calculate the critical wind speed of sideslip. Finally, the speed of the hazardous chemical vehicle is used as the safety evaluation index, and the safety critical speed surface is given. This research has important reference value for ensuring the transportation safety of hazardous chemicals and the operation of cross-sea bridges.
{"title":"SAFETY ASSESSMENT METHOD FOR VEHICLE TRANSPORTATION OF HAZARDOUS CHEMICALS ON CROSS-SEA BRIDGES","authors":"Jian Guo, Kai Ma, C. Luo","doi":"10.12783/shm2021/36249","DOIUrl":"https://doi.org/10.12783/shm2021/36249","url":null,"abstract":"With the rapid development of China’s chemical industry in coastal areas, the transportation of hazardous chemicals has become increasingly busy. Due to the complexity of marine environment, there are a large number of safety hazards during the transportation of hazardous chemicals. Based on accident statistics, the main factors affecting the transportation safety of hazardous chemicals is analyzed, including strong wind and reduced adhesion coefficient caused by rain and snow. Further, a vehicle stability analysis model considering these factors is established to calculate the critical wind speed of sideslip. Finally, the speed of the hazardous chemical vehicle is used as the safety evaluation index, and the safety critical speed surface is given. This research has important reference value for ensuring the transportation safety of hazardous chemicals and the operation of cross-sea bridges.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"50 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":"124587002","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}
This paper proposes a CNN-based crack detection method that can recognize and extract cracks from photos of concrete structures. The algorithm consists of two subsequent procedures, classification, and segmentation, achieved by two convolutional neural networks respectively. First, full images are divided into patches and classified as positive and negative. Then, those sub-images classified as positive are further processed by the image segmentation procedure to obtain the pixel level geometry of the cracks. For the classification part, the performance of transfer learning models based on pre-trained VGG16, Inception V3, MobileNet and DenseNet169 is compared with different classifier. Finally, the CNN based on MobileNet was trained with 30,000 training images and reached 97% testing accuracy and 0.96 F1 score on testing image. For the segmentation part, different neural networks based on the elegant U-net architecture are built and tested. The models are trained with 3840 crack images and annotated ground truth and compared quantitatively and qualitatively. The model with the best performance reached 88% sensitivity on test data set. The combination of the classification and segmentation neural networks achieves an image-based crack detection method with high efficiency and accuracy. The algorithm can process any full image size as input. Compared with most machine learning based crack detection algorithms using sub-image classification, a relatively larger patch size is used in this paper and in this way the classification is more robust and accurate. On the other hand, the negative areas in the full image will not be concerned in the segmentation procedure and this fact not only saves a lot of computational power but also significantly increases the accuracy compared to the segmentation performed on full images.
{"title":"AN IMAGE-BASED CONCRETE CRACK DETECTION METHOD USING CONVOLUTIONAL NEURAL NETWORKS","authors":"Xing Luo, Jiadong Guo, K. Zandi","doi":"10.12783/shm2021/36325","DOIUrl":"https://doi.org/10.12783/shm2021/36325","url":null,"abstract":"This paper proposes a CNN-based crack detection method that can recognize and extract cracks from photos of concrete structures. The algorithm consists of two subsequent procedures, classification, and segmentation, achieved by two convolutional neural networks respectively. First, full images are divided into patches and classified as positive and negative. Then, those sub-images classified as positive are further processed by the image segmentation procedure to obtain the pixel level geometry of the cracks. For the classification part, the performance of transfer learning models based on pre-trained VGG16, Inception V3, MobileNet and DenseNet169 is compared with different classifier. Finally, the CNN based on MobileNet was trained with 30,000 training images and reached 97% testing accuracy and 0.96 F1 score on testing image. For the segmentation part, different neural networks based on the elegant U-net architecture are built and tested. The models are trained with 3840 crack images and annotated ground truth and compared quantitatively and qualitatively. The model with the best performance reached 88% sensitivity on test data set. The combination of the classification and segmentation neural networks achieves an image-based crack detection method with high efficiency and accuracy. The algorithm can process any full image size as input. Compared with most machine learning based crack detection algorithms using sub-image classification, a relatively larger patch size is used in this paper and in this way the classification is more robust and accurate. On the other hand, the negative areas in the full image will not be concerned in the segmentation procedure and this fact not only saves a lot of computational power but also significantly increases the accuracy compared to the segmentation performed on full images.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"50 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":"126954570","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}
An integrated techno-economic cost-benefit analysis is presented to analyze the impact of damage detection-based SHM on an Airbus A320-based reference aircraft, instrumented with ultrasonic and fiber-optic sensors. The operational performance of aircraft with and without SHM is compared in terms of inspection effort, dispatch reliability, payload capacity, service limit, SHM equipment weight and performance, as well as total operating cost. Finally, the net present value of SHM is calculated. While SHM can be profitable for airlines, the achievable benefit depends on the SHM system performance and the economic environment of the airline.
{"title":"COST AND BENEFIT OF SHM IN COMMERCIAL AVIATION","authors":"D. Steinweg, M. Hornung","doi":"10.12783/shm2021/36239","DOIUrl":"https://doi.org/10.12783/shm2021/36239","url":null,"abstract":"An integrated techno-economic cost-benefit analysis is presented to analyze the impact of damage detection-based SHM on an Airbus A320-based reference aircraft, instrumented with ultrasonic and fiber-optic sensors. The operational performance of aircraft with and without SHM is compared in terms of inspection effort, dispatch reliability, payload capacity, service limit, SHM equipment weight and performance, as well as total operating cost. Finally, the net present value of SHM is calculated. While SHM can be profitable for airlines, the achievable benefit depends on the SHM system performance and the economic environment of the airline.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"14 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":"121466147","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}
Time synchronization in communication networks is a common issue: in a sensor network it means that the order of data samples becomes uncertain, which can make it unusable. Dedicated signals and schemes for synchronization of sensor networks has hence been a well-researched topic for decades. Here we bring in an approach to synchronization which uses the sensory data. Drawing inspiration from sensor time synchronization using environmental noise, we consider synchronizing sensory nodes for structural health monitoring–if the physical quantity the sensors measure is correlated, propagating as a wave, or oscillating in regular fashion, it is intuitively clear how to put it to use. We discuss when structural health monitoring signals can aid synchronization; we also connect this synchronization scheme to the idea of using physical human-made structures as reservoirs for reservoir computing, formulating synchronization as a reservoir computing task.
{"title":"NATURAL SYNCHRONIZATION OF WIRELESS SENSOR NETWORKS FOR STRUCTURAL HEALTH MONITORING","authors":"H. Šiljak, B. Basu","doi":"10.12783/shm2021/36278","DOIUrl":"https://doi.org/10.12783/shm2021/36278","url":null,"abstract":"Time synchronization in communication networks is a common issue: in a sensor network it means that the order of data samples becomes uncertain, which can make it unusable. Dedicated signals and schemes for synchronization of sensor networks has hence been a well-researched topic for decades. Here we bring in an approach to synchronization which uses the sensory data. Drawing inspiration from sensor time synchronization using environmental noise, we consider synchronizing sensory nodes for structural health monitoring–if the physical quantity the sensors measure is correlated, propagating as a wave, or oscillating in regular fashion, it is intuitively clear how to put it to use. We discuss when structural health monitoring signals can aid synchronization; we also connect this synchronization scheme to the idea of using physical human-made structures as reservoirs for reservoir computing, formulating synchronization as a reservoir computing task.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"11 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":"127712171","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}
This paper presents a Bluetooth Low Energy sensing node, part of a wireless sensor network dedicated to the deployment of a cyber-physical system for the structural health monitoring of reinforced concretes throughout their life. This fully wireless sensing node is designed to measure temperature and relative humidity, and wirelessly transmit the collected data in its network, as well as to be energy autonomous. For that, it is battery-free, able to cold-start, and wirelessly and remotely powered -and controlledover several meters by communicating nodes (other part of the network, assuring the connection to the digital world) via a radiative electromagnetic power transfer system.
{"title":"BATTERY-FREE BLUETOOTH LOW ENERGY SENSING NODES FOR STRUCTURAL HEALTH MONITORING OF CONCRETES","authors":"G. Loubet, A. Sidibe, A. Takacs, D. Dragomirescu","doi":"10.12783/shm2021/36247","DOIUrl":"https://doi.org/10.12783/shm2021/36247","url":null,"abstract":"This paper presents a Bluetooth Low Energy sensing node, part of a wireless sensor network dedicated to the deployment of a cyber-physical system for the structural health monitoring of reinforced concretes throughout their life. This fully wireless sensing node is designed to measure temperature and relative humidity, and wirelessly transmit the collected data in its network, as well as to be energy autonomous. For that, it is battery-free, able to cold-start, and wirelessly and remotely powered -and controlledover several meters by communicating nodes (other part of the network, assuring the connection to the digital world) via a radiative electromagnetic power transfer system.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"26 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":"127923462","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}
Structural health monitoring (SHM) of additively manufactured polymer parts is challenging due to the very strong attenuation of the surface waves. To excite the part surface at a very wide frequency band in a very short time, Multiple Width Pulse Excitation (MWPE) signal was introduced. MPWE was used to excite the surface of the structure for the implementation of the Surface Response to Excitation (SuRE) method. A cross-shaped polymer part was fabricated additively for the identification of the hidden geometry of the infill. The part had four extensions with identical geometry but different internal designs. Two of the extensions had cross infills and the other two had square infills. For each type of infill, one extension had 1 mm and the other extension had 2 mm thick skin. The part was excited at the middle with WMPE excitation and the dynamic response was monitored at the end of each extension. The Short-Time Fast Fourier Transform (STFFT) was used for the analysis of the signal in the time-frequency domain. The two dimentional sum of the squares of the differences (2DSSD) was used for the classification of the signal. Compressive force and type of infill was identified accurately for all the test cases.
由于表面波的衰减非常强，增材制造聚合物部件的结构健康监测(SHM)具有挑战性。为了在极短的时间内对零件表面进行极宽频带的激励，引入了多宽脉冲激励(MWPE)信号。利用MPWE对结构表面进行激励，实现表面激励响应(surface Response to Excitation, SuRE)方法。为了识别填充物的隐藏几何形状，采用增材制造了十字形聚合物零件。该部件有四个具有相同几何形状但内部设计不同的扩展部分。其中两个扩展部分是交叉填充，另外两个是方形填充。对于每种类型的填充物，一个延伸有1毫米厚，另一个延伸有2毫米厚的皮肤。采用WMPE励磁法对中间部分进行激励，并在每次延伸结束时监测其动态响应。采用短时快速傅立叶变换(STFFT)对信号进行时频分析。采用二维差分平方和(2DSSD)对信号进行分类。所有试验用例的压缩力和填充物类型都得到了准确的识别。
{"title":"NEW EXCITATION (MULTIPLE WIDTH PULSE EXCITATION (MWPE)) METHOD FOR SHM SYSTEMS—PART 1: VISUALIZATION OF TIME- FREQUENCY DOMAIN CHARACTERISTICS","authors":"I. Tansel, Alireza Modir","doi":"10.12783/shm2021/36341","DOIUrl":"https://doi.org/10.12783/shm2021/36341","url":null,"abstract":"Structural health monitoring (SHM) of additively manufactured polymer parts is challenging due to the very strong attenuation of the surface waves. To excite the part surface at a very wide frequency band in a very short time, Multiple Width Pulse Excitation (MWPE) signal was introduced. MPWE was used to excite the surface of the structure for the implementation of the Surface Response to Excitation (SuRE) method. A cross-shaped polymer part was fabricated additively for the identification of the hidden geometry of the infill. The part had four extensions with identical geometry but different internal designs. Two of the extensions had cross infills and the other two had square infills. For each type of infill, one extension had 1 mm and the other extension had 2 mm thick skin. The part was excited at the middle with WMPE excitation and the dynamic response was monitored at the end of each extension. The Short-Time Fast Fourier Transform (STFFT) was used for the analysis of the signal in the time-frequency domain. The two dimentional sum of the squares of the differences (2DSSD) was used for the classification of the signal. Compressive force and type of infill was identified accurately for all the test cases.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"194 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":"132280352","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}
3D remote sensing technologies have improved dramatically over the past five years and methods such as laser scanning and photogrammetry are now capable of reliably resolving geometric details on the order of one millimeter or less. This has significant impacts for the structural health monitoring community, as it has expanded the range of mechanics-driven problems that these methods can be employed on. In this work, we explore how 3D geometric measurements extracted from photogrammetric point clouds can be leveraged for structural analysis and measurement of structural deformations without physically contacting the target structure. Here we present a non-destructive evaluation technique for extracting and quantifying structural deformations as applied to a load test on a highway bridge in Delaware. The challenging nature of 3D point cloud data means that statistical methods must be employed to adequately evaluate the deformation field of the bridge. Overall, the results show a direct pathway from 3D imaging to fundamental mechanical analysis with measurements that capture the true deformation values typically within one standard deviation. These results are promising given that the mid-span deformation of the bridge for the given load test is on the scale of only a few millimeters. Future work for this method will also investigate using these results for updating finite element models.
{"title":"FULL-SCALE DEFORMATION FIELD MEASUREMENTS VIA PHOTOGRAMMETRIC REMOTE SENSING","authors":"W. Graves, D. Lattanzi","doi":"10.12783/shm2021/36298","DOIUrl":"https://doi.org/10.12783/shm2021/36298","url":null,"abstract":"3D remote sensing technologies have improved dramatically over the past five years and methods such as laser scanning and photogrammetry are now capable of reliably resolving geometric details on the order of one millimeter or less. This has significant impacts for the structural health monitoring community, as it has expanded the range of mechanics-driven problems that these methods can be employed on. In this work, we explore how 3D geometric measurements extracted from photogrammetric point clouds can be leveraged for structural analysis and measurement of structural deformations without physically contacting the target structure. Here we present a non-destructive evaluation technique for extracting and quantifying structural deformations as applied to a load test on a highway bridge in Delaware. The challenging nature of 3D point cloud data means that statistical methods must be employed to adequately evaluate the deformation field of the bridge. Overall, the results show a direct pathway from 3D imaging to fundamental mechanical analysis with measurements that capture the true deformation values typically within one standard deviation. These results are promising given that the mid-span deformation of the bridge for the given load test is on the scale of only a few millimeters. Future work for this method will also investigate using these results for updating finite element models.","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":"133762356","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}
Paul Sieber, K. Agathos, R. Soman, Wieslaw OSTACHOWICZWIESLAW OSTACHOWICZ, E. Chatzi
Data from guided wave propagation in structures, produced by piezoelectric elements, can offer valuable information regarding the possible existence of flaws. Numerical models can be used to complement the attained data for refining the potential for flaw characterization. Unfortunately, evaluation of these models remains computationally expensive, especially for small defects, due to the short wavelength required for detection and, the in turn fine discretization in time and space. This renders real–time simulation infeasible, rendering GW–approaches less attractive for inverse problem formulations, where the forward problem needs to be solved several times. We propose an accelerated computation method, which exploits the properties of guided waves interacting with defects, where an extra band of waves is created, whose phase is differentiated, depending on the location of the flaw (e.g. notch) within the medium. To expedite the actual simulation for the inverse problem, the system is parametrized in terms of the location of the flaw and, in an offline phase, is repeatedly solved to produce snapshots of the system’s response. The snapshots are used to create a physics–informed interpolation of the solution of the wave propagation problem for different flaw locations. The gained information is then used in an inverse setting for localising the defect using an evolution strategy as a means to stochastic, derivative-free numerical optimization. The method is demonstrated in simulations of a 2D slice of a thin plate.
{"title":"A PARAMETRIZED REDUCED ORDER MODEL FOR RAPID EVALUATION OF FLAWS IN GUIDED WAVE TESTING","authors":"Paul Sieber, K. Agathos, R. Soman, Wieslaw OSTACHOWICZWIESLAW OSTACHOWICZ, E. Chatzi","doi":"10.12783/shm2021/36315","DOIUrl":"https://doi.org/10.12783/shm2021/36315","url":null,"abstract":"Data from guided wave propagation in structures, produced by piezoelectric elements, can offer valuable information regarding the possible existence of flaws. Numerical models can be used to complement the attained data for refining the potential for flaw characterization. Unfortunately, evaluation of these models remains computationally expensive, especially for small defects, due to the short wavelength required for detection and, the in turn fine discretization in time and space. This renders real–time simulation infeasible, rendering GW–approaches less attractive for inverse problem formulations, where the forward problem needs to be solved several times. We propose an accelerated computation method, which exploits the properties of guided waves interacting with defects, where an extra band of waves is created, whose phase is differentiated, depending on the location of the flaw (e.g. notch) within the medium. To expedite the actual simulation for the inverse problem, the system is parametrized in terms of the location of the flaw and, in an offline phase, is repeatedly solved to produce snapshots of the system’s response. The snapshots are used to create a physics–informed interpolation of the solution of the wave propagation problem for different flaw locations. The gained information is then used in an inverse setting for localising the defect using an evolution strategy as a means to stochastic, derivative-free numerical optimization. The method is demonstrated in simulations of a 2D slice of a thin plate.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"179 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":"133641917","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 recent years the Digital Twin (DT) paradigm has been studied as a futuristic tool for the next generation of infrastructures. Due to the interdisciplinary nature of the design, construction, monitoring, and maintenance of the infrastructures and the cooperation of several stakeholders throughout their lifetime, it is indispensable to introduce a comprehensive platform for the digital representation of infrastructures. Although the DT emphasizes the role of digital modeling and data analysis, there is a gap between physical modeling and data-driven tools. The newly introduced Physics Informed Neural Networks (PINNs) are capable of not only filling this gap but also representing a unified real-time platform for different users from various fields. These algorithms suggest an agile environment for users to introduce different criteria from the design stage to the health monitoring period. The PINN integrates both physical modeling and data analysis in a unique algorithm, helping them interact simultaneously and providing real-time, reliable responses. By means of the PINN, the DT can learn and update the model from various data sources with a unique platform, which plays an essential role in the rapid flow of information and transparency of data-based calculations. The dynamic ambiance of the PINN enables the users to interact with the modeling procedure and track the analysis. In this study, the details of the proposed platform for the integration of the PINNs in the DT are addressed for monitoring the bridges. Extensive numerical studies are provided for various scenarios of sensor equipment, including sensor type, data accuracy, and installation pattern. The performance of the proposed platform is evaluated for predicting subsequent responses to ensure the reliability of the responses in future decision makings.
{"title":"A PHYSICS INFORMED NEURAL NETWORK INTEGRATED DIGITAL TWIN FOR MONITORING OF THE BRIDGES","authors":"Sarvin Moradi, S. E. Azam, M. Mofid","doi":"10.12783/shm2021/36326","DOIUrl":"https://doi.org/10.12783/shm2021/36326","url":null,"abstract":"In recent years the Digital Twin (DT) paradigm has been studied as a futuristic tool for the next generation of infrastructures. Due to the interdisciplinary nature of the design, construction, monitoring, and maintenance of the infrastructures and the cooperation of several stakeholders throughout their lifetime, it is indispensable to introduce a comprehensive platform for the digital representation of infrastructures. Although the DT emphasizes the role of digital modeling and data analysis, there is a gap between physical modeling and data-driven tools. The newly introduced Physics Informed Neural Networks (PINNs) are capable of not only filling this gap but also representing a unified real-time platform for different users from various fields. These algorithms suggest an agile environment for users to introduce different criteria from the design stage to the health monitoring period. The PINN integrates both physical modeling and data analysis in a unique algorithm, helping them interact simultaneously and providing real-time, reliable responses. By means of the PINN, the DT can learn and update the model from various data sources with a unique platform, which plays an essential role in the rapid flow of information and transparency of data-based calculations. The dynamic ambiance of the PINN enables the users to interact with the modeling procedure and track the analysis. In this study, the details of the proposed platform for the integration of the PINNs in the DT are addressed for monitoring the bridges. Extensive numerical studies are provided for various scenarios of sensor equipment, including sensor type, data accuracy, and installation pattern. The performance of the proposed platform is evaluated for predicting subsequent responses to ensure the reliability of the responses in future decision makings.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"41 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":"116546773","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}
H. Alnuaimi, U. Amjad, Sehyuk Park, P. Russo, V. Lopresto, T. Kundu
The newly developed non-linear ultrasonic (NLU) technique known as the Sideband Peak Count - Index (SPC-I) has demonstrated that it can detect and monitor the non-linearity generated by defects in a wide range of materials such as metals, composites, and concrete. The general approach of applying the SPC-I technique is by using a single sweep wideband excitation signal that is propagated through the specimen and a single signal is received which is then analyzed. This general approach has proven to be effective in giving a big picture measure of the nonlinearity of the material. However, it can be further tuned and improved by exciting a sweep signal using multiple excitation signals. As a result, multiple signals are received and analyzed. These multiple sweep signals have the benefit of not being contaminated (dispersion effects) by multiple wave modes propagating at the same time compared to exciting a wide band single sweep signal. Additionally, by using these multiple sweep signals the effects of frequency modulation of wave modes and higher harmonics are easier to detect. By analyzing the received signals multiple frequency ranges can be discovered that are sensitive to different failure modes or types of defects. These frequency ranges of interest are then used to detect damage initiation and progression in the composite plate specimens. Two sets of composite plate specimens with two types of fiber reinforcements (Glass and Basalt) are investigated in this study. The specimens are impacted with a dart impact machine at increasing impact energies. By focusing on a frequency range that is sensitive to the damage in the composite plate specimens. The NLU SPC-I technique can robustly detect and monitor the impact induced damages in composite plates.
{"title":"ROBUST DETECTION OF DAMAGE IN COMPOSITE PLATES USING THE NONLINEAR SPC-I ULTRASONIC TECHNIQUE","authors":"H. Alnuaimi, U. Amjad, Sehyuk Park, P. Russo, V. Lopresto, T. Kundu","doi":"10.12783/shm2021/36361","DOIUrl":"https://doi.org/10.12783/shm2021/36361","url":null,"abstract":"The newly developed non-linear ultrasonic (NLU) technique known as the Sideband Peak Count - Index (SPC-I) has demonstrated that it can detect and monitor the non-linearity generated by defects in a wide range of materials such as metals, composites, and concrete. The general approach of applying the SPC-I technique is by using a single sweep wideband excitation signal that is propagated through the specimen and a single signal is received which is then analyzed. This general approach has proven to be effective in giving a big picture measure of the nonlinearity of the material. However, it can be further tuned and improved by exciting a sweep signal using multiple excitation signals. As a result, multiple signals are received and analyzed. These multiple sweep signals have the benefit of not being contaminated (dispersion effects) by multiple wave modes propagating at the same time compared to exciting a wide band single sweep signal. Additionally, by using these multiple sweep signals the effects of frequency modulation of wave modes and higher harmonics are easier to detect. By analyzing the received signals multiple frequency ranges can be discovered that are sensitive to different failure modes or types of defects. These frequency ranges of interest are then used to detect damage initiation and progression in the composite plate specimens. Two sets of composite plate specimens with two types of fiber reinforcements (Glass and Basalt) are investigated in this study. The specimens are impacted with a dart impact machine at increasing impact energies. By focusing on a frequency range that is sensitive to the damage in the composite plate specimens. The NLU SPC-I technique can robustly detect and monitor the impact induced damages in composite plates.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"32 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":"131014056","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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