Contact acoustic nonlinearity (CAN) is generated when oscillating crack faces open and close while a wave passes through it. However, reliably assessing the nonlinear effect due to micro-scale defects is challenging, especially in concrete structures, due to their large size, high attenuation and low signal-to-noise ratio. However, concrete facilities vibrate due to ambient excitations such as vehicle movement, wind, and water flow. These ambient vibrations can be utilised in amplifying CAN. For example, a vehicle can be moved at a particular velocity over a bridge to amplify a particular natural mode of vibration. This paper illustrates a method of enhancing contact acoustic nonlinearity with the help of ambient vibrations of the structure. A finite element (FE) model of a concrete beam with a thin crack is developed. The base of the beam was oscillated at 100 Hz. Simultaneously, a 200 kHz ultrasonic excitation was applied on the beam to monitor its propagation through the crack. The closing and opening of the crack generate the nonlinear behavior of the ultrasonic pulse. A considerable increment of nonlinearity was observed demonstrating the efficacy of the proposed method. The time windows for the nonlinear zone have been identified. A laboratory experiment has been performed to demonstrate the proposed method in reinforced concrete beams. This investigation demonstrates that CAN can be utilised in monitoring concrete structures when ambient vibrations are taken into account.
{"title":"Enhancement of Contact Acoustic Nonlinearity Effect in a Concrete Beam using Ambient Vibrations","authors":"Yikuan Wang, Abhijit Mukherjee","doi":"10.1115/1.4064374","DOIUrl":"https://doi.org/10.1115/1.4064374","url":null,"abstract":"Contact acoustic nonlinearity (CAN) is generated when oscillating crack faces open and close while a wave passes through it. However, reliably assessing the nonlinear effect due to micro-scale defects is challenging, especially in concrete structures, due to their large size, high attenuation and low signal-to-noise ratio. However, concrete facilities vibrate due to ambient excitations such as vehicle movement, wind, and water flow. These ambient vibrations can be utilised in amplifying CAN. For example, a vehicle can be moved at a particular velocity over a bridge to amplify a particular natural mode of vibration. This paper illustrates a method of enhancing contact acoustic nonlinearity with the help of ambient vibrations of the structure. A finite element (FE) model of a concrete beam with a thin crack is developed. The base of the beam was oscillated at 100 Hz. Simultaneously, a 200 kHz ultrasonic excitation was applied on the beam to monitor its propagation through the crack. The closing and opening of the crack generate the nonlinear behavior of the ultrasonic pulse. A considerable increment of nonlinearity was observed demonstrating the efficacy of the proposed method. The time windows for the nonlinear zone have been identified. A laboratory experiment has been performed to demonstrate the proposed method in reinforced concrete beams. This investigation demonstrates that CAN can be utilised in monitoring concrete structures when ambient vibrations are taken into account.","PeriodicalId":52294,"journal":{"name":"Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems","volume":"141 1‐2","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139149380","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 size of bearing outer ring spalling failures has a significant impact on the vibration and service life of rotating machinery. It is necessary to judge the size of the outer ring fault size. Most of the vibration analyses identify the bearing fault size only in terms of the shock interval. The decreasing impact of the shock on the vibration signal will be related to the identification accuracy of the shock interval. This study aims to identify some feasible vibration signal processing methods for the identification of outer ring spalling sizes of ball bearings based on a modified energy value. The method involves the influence of impact forces on the measured vibration characteristics. According to the simulation analysis, the mapping relationship between the vibration signals with different fault sizes and the modified energy value is obtained. Then, the size of the spalling failure size of the ball bearing outer ring is determined. Compared to existing methods, the proposed method is less affected by impact forces. Simulation and experiment results have verified the accuracy of this fault size identification.
{"title":"Identification of spalling fault size of ball bearing based on modified energy value","authors":"Ke Zhang, Ru Zhang, Zinan Wang, X. Bai, H. Shi","doi":"10.1115/1.4064186","DOIUrl":"https://doi.org/10.1115/1.4064186","url":null,"abstract":"\u0000 The size of bearing outer ring spalling failures has a significant impact on the vibration and service life of rotating machinery. It is necessary to judge the size of the outer ring fault size. Most of the vibration analyses identify the bearing fault size only in terms of the shock interval. The decreasing impact of the shock on the vibration signal will be related to the identification accuracy of the shock interval. This study aims to identify some feasible vibration signal processing methods for the identification of outer ring spalling sizes of ball bearings based on a modified energy value. The method involves the influence of impact forces on the measured vibration characteristics. According to the simulation analysis, the mapping relationship between the vibration signals with different fault sizes and the modified energy value is obtained. Then, the size of the spalling failure size of the ball bearing outer ring is determined. Compared to existing methods, the proposed method is less affected by impact forces. Simulation and experiment results have verified the accuracy of this fault size identification.","PeriodicalId":52294,"journal":{"name":"Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems","volume":"27 11","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138604209","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}
Steven Hespeler, Hamidreza Nemati, Nihar Masurkar, Fernando Alvidrez, Hamidreza Marvi, Ehsan Dehghan Niri
Abstract This journal paper explores the application of Deep Learning (DL)-based Time-Series Classification (TSC) algorithms in ultrasonic testing for pipeline inspection. The utility of Electromagnetic Acoustic Transducers (EMAT) as a non-contact ultrasonic testing technique for compact robotic platforms is emphasized, prioritizing computational efficiency in defect detection over pinpoint accuracy. To address limited sample availability, the study conducts benchmarking of four methods to enable comparative evaluation of classification times. The core of the DL-based TSC approach involves training DL models using varied proportions (60%, 80%, and 100%) of the available training dataset. This investigation demonstrates the adaptability of DL-enabled anomaly detection with shifting data sizes, showcasing the AI-driven process's robustness in identifying pipeline irregularities. The outcomes underscore the pivotal role of artificial intelligence (AI) in facilitating semi-accurate but swift anomaly detection, thereby streamlining subsequent focused inspections on pipeline areas of concern. By synergistically integrating EMAT technology and DL-driven TSC, this research contributes to enhancing the precision and near real-time inspection capabilities of pipeline assessment. This investigation collectively highlights the potential of DL networks to revolutionize pipeline inspection by rapidly and accurately analyzing ultrasound waveform data.
{"title":"Deep Learning based Time-Series Classification for Robotic Inspection of Pipe Condition using Non-Contact Ultrasonic Testing","authors":"Steven Hespeler, Hamidreza Nemati, Nihar Masurkar, Fernando Alvidrez, Hamidreza Marvi, Ehsan Dehghan Niri","doi":"10.1115/1.4063694","DOIUrl":"https://doi.org/10.1115/1.4063694","url":null,"abstract":"Abstract This journal paper explores the application of Deep Learning (DL)-based Time-Series Classification (TSC) algorithms in ultrasonic testing for pipeline inspection. The utility of Electromagnetic Acoustic Transducers (EMAT) as a non-contact ultrasonic testing technique for compact robotic platforms is emphasized, prioritizing computational efficiency in defect detection over pinpoint accuracy. To address limited sample availability, the study conducts benchmarking of four methods to enable comparative evaluation of classification times. The core of the DL-based TSC approach involves training DL models using varied proportions (60%, 80%, and 100%) of the available training dataset. This investigation demonstrates the adaptability of DL-enabled anomaly detection with shifting data sizes, showcasing the AI-driven process's robustness in identifying pipeline irregularities. The outcomes underscore the pivotal role of artificial intelligence (AI) in facilitating semi-accurate but swift anomaly detection, thereby streamlining subsequent focused inspections on pipeline areas of concern. By synergistically integrating EMAT technology and DL-driven TSC, this research contributes to enhancing the precision and near real-time inspection capabilities of pipeline assessment. This investigation collectively highlights the potential of DL networks to revolutionize pipeline inspection by rapidly and accurately analyzing ultrasound waveform data.","PeriodicalId":52294,"journal":{"name":"Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems","volume":" 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135293371","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}
Mohamed Aninou, Adil El Baroudi, Jean Yves Le Pommellec
Abstract A novel analytical investigation of longitudinal wave propagation in an elastic cylinder embedded in a viscoelastic fluid is proposed. The Maxwell model is used to describe the viscoelastic fluid behavior. With appropriate boundary conditions, a complex dispersion equation of longitudinal wave has been established. The aim of this paper is to study the effect of the fluid rheological properties on the longitudinal wave characteristics (attenuation and velocity). It is shown that the attenuation is the sum of a viscous and non viscous component. The viscosity induced attenuation is predominant at low frequencies. On the other hand, the effect of the liquid amount and elastic cylinder radius on the attenuation and velocity are studied. A critical normalized liquid thickness is highlighted. Beyond this critical value, the influence of the outer boundary condition can be neglected. At last, among other interesting phenomena, it is highlighted that if the Deborah number increases, the attenuation decreases. This variation characterize a stiffening of the viscoelastic medium. In addition, the obtained results show that the viscosity measurement should be performed at low frequencies using small elastic cylinder radius. Accordingly, these investigations are novel and can be applied in geophysics, food industry, medicine, non-destructive testing of materials, design and development of fluid sensors.
{"title":"Longitudinal wave propagation in an elastic cylinder embedded in a viscoelastic fluid","authors":"Mohamed Aninou, Adil El Baroudi, Jean Yves Le Pommellec","doi":"10.1115/1.4064012","DOIUrl":"https://doi.org/10.1115/1.4064012","url":null,"abstract":"Abstract A novel analytical investigation of longitudinal wave propagation in an elastic cylinder embedded in a viscoelastic fluid is proposed. The Maxwell model is used to describe the viscoelastic fluid behavior. With appropriate boundary conditions, a complex dispersion equation of longitudinal wave has been established. The aim of this paper is to study the effect of the fluid rheological properties on the longitudinal wave characteristics (attenuation and velocity). It is shown that the attenuation is the sum of a viscous and non viscous component. The viscosity induced attenuation is predominant at low frequencies. On the other hand, the effect of the liquid amount and elastic cylinder radius on the attenuation and velocity are studied. A critical normalized liquid thickness is highlighted. Beyond this critical value, the influence of the outer boundary condition can be neglected. At last, among other interesting phenomena, it is highlighted that if the Deborah number increases, the attenuation decreases. This variation characterize a stiffening of the viscoelastic medium. In addition, the obtained results show that the viscosity measurement should be performed at low frequencies using small elastic cylinder radius. Accordingly, these investigations are novel and can be applied in geophysics, food industry, medicine, non-destructive testing of materials, design and development of fluid sensors.","PeriodicalId":52294,"journal":{"name":"Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems","volume":"169 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135480205","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}
Abstract This article addresses the classification of fatigue crack length using artificial intelligence (AI) applied to acoustic emission (AE) signals. The AE signals were collected during fatigue of two specimen types. One specimen type had a 1-mm hole for crack initiation. The other specimen type had a 150-micron wide slit of various lengths. Fatigue testing was performed under stress-intensity-factor control to moderate crack advancement. The slit specimen produced AE signals only from crack advancement at the slit tips whereas the 1-mm hole specimens produced AE signals both from crack tip advancement and crack rubbing or clapping. The AE signals were captured with a piezoelectric wafer active sensor (PWAS) array connected to MISTRAS instrumentation and AEwin software. The collected AE signals were preprocessed using time-of-flight filtering and denoising. Choi Williams transform converted time-domain AE-signals into spectrograms. To apply machine learning, the spectrogram images were used as input data for the training, validation, and testing of a GoogLeNet convolutional neural network (CNN). The CNN was trained to sort the AE signals into crack-length classes. CNN performance enhancements, including synthetic data generation and class balancing were developed. A three-class example with crack lengths of (i) 10-12 mm; (ii) 12-14 mm; and (iii) 14-16 mm is provided. Our AI approach was able to classify the AE signals into these three classes with 91% accuracy thus proving that the AE signals contain sufficient information for crack estimation using an AI-enabled approach.
{"title":"AI-enabled crack-length estimation from acoustic emission signal signatures","authors":"Shane Ennis, Victor Giurgiutiu","doi":"10.1115/1.4064011","DOIUrl":"https://doi.org/10.1115/1.4064011","url":null,"abstract":"Abstract This article addresses the classification of fatigue crack length using artificial intelligence (AI) applied to acoustic emission (AE) signals. The AE signals were collected during fatigue of two specimen types. One specimen type had a 1-mm hole for crack initiation. The other specimen type had a 150-micron wide slit of various lengths. Fatigue testing was performed under stress-intensity-factor control to moderate crack advancement. The slit specimen produced AE signals only from crack advancement at the slit tips whereas the 1-mm hole specimens produced AE signals both from crack tip advancement and crack rubbing or clapping. The AE signals were captured with a piezoelectric wafer active sensor (PWAS) array connected to MISTRAS instrumentation and AEwin software. The collected AE signals were preprocessed using time-of-flight filtering and denoising. Choi Williams transform converted time-domain AE-signals into spectrograms. To apply machine learning, the spectrogram images were used as input data for the training, validation, and testing of a GoogLeNet convolutional neural network (CNN). The CNN was trained to sort the AE signals into crack-length classes. CNN performance enhancements, including synthetic data generation and class balancing were developed. A three-class example with crack lengths of (i) 10-12 mm; (ii) 12-14 mm; and (iii) 14-16 mm is provided. Our AI approach was able to classify the AE signals into these three classes with 91% accuracy thus proving that the AE signals contain sufficient information for crack estimation using an AI-enabled approach.","PeriodicalId":52294,"journal":{"name":"Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems","volume":"349 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":"135475557","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}
Mahdi Ghafarzadeh, Mohammad Tavakoli Kejani, Mehdi Karimi, Amirreza Asadi
Abstract Utilizing accurate, nondestructive testing methods to improve quality control and reduce manufacturing errors has gained prominence in light of industry development in various fields. Industrial computed tomography (CT) scanning carries considerable weight among all conventional methods because of their unique features, such as providing a three-dimensional specimen model. Due to the prevalence of metals with high linear attenuation coefficients in industrial applications, beam hardening and scatter artifacts are two of the most prevalent artifacts in any reconstructed volume. Other notable artifacts include those with a nonideal focal spot and conical beam radiation. These artifacts may manifest as a distortion of gray value peaks, systematic discrepancies, blurring-like cupping, and streaking in reconstructed images, degrading volume reconstruction quality. In this paper, the effect of these artifacts is illustrated and mitigated by adopting our proposed method, a combination of conventional and contemporary techniques, including the use of a pretrained convolutional neural network (CNN). Five tests are replicated in different geometric parameters to perform a geometric configuration analysis, indicating how effective the proposed approach is at encountering different geometric situations. The results demonstrate that the proposed method has substantially achieved its goal of improving the accuracy of dimensional metrology performed on our phantom.
{"title":"CT Artifact Reduction Employing A Convolutional Neural Network Within the Context of Dimensional Metrology","authors":"Mahdi Ghafarzadeh, Mohammad Tavakoli Kejani, Mehdi Karimi, Amirreza Asadi","doi":"10.1115/1.4063805","DOIUrl":"https://doi.org/10.1115/1.4063805","url":null,"abstract":"Abstract Utilizing accurate, nondestructive testing methods to improve quality control and reduce manufacturing errors has gained prominence in light of industry development in various fields. Industrial computed tomography (CT) scanning carries considerable weight among all conventional methods because of their unique features, such as providing a three-dimensional specimen model. Due to the prevalence of metals with high linear attenuation coefficients in industrial applications, beam hardening and scatter artifacts are two of the most prevalent artifacts in any reconstructed volume. Other notable artifacts include those with a nonideal focal spot and conical beam radiation. These artifacts may manifest as a distortion of gray value peaks, systematic discrepancies, blurring-like cupping, and streaking in reconstructed images, degrading volume reconstruction quality. In this paper, the effect of these artifacts is illustrated and mitigated by adopting our proposed method, a combination of conventional and contemporary techniques, including the use of a pretrained convolutional neural network (CNN). Five tests are replicated in different geometric parameters to perform a geometric configuration analysis, indicating how effective the proposed approach is at encountering different geometric situations. The results demonstrate that the proposed method has substantially achieved its goal of improving the accuracy of dimensional metrology performed on our phantom.","PeriodicalId":52294,"journal":{"name":"Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems","volume":"24 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135769506","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}
Abstract In this study, the influence of the orientation of embedded piezoelectric ceramic lead zirconium titanate (PZT) on the mechanical performance of hybrid fiber-reinforced (polypropylene and glass fiber) concrete beams was evaluated. The performance of concrete was evaluated under self-weight, followed by assessing the mechanical property using the electromechanical impedance (EMI) technique after optimization of M30 grade concrete with polypropylene fiber and glass fiber. PZT patches are embedded at different orientations, i.e., 0 deg, 45 deg, and 90 deg, with the axis of the structure for monitoring the hydration of the RC beam. The change in stiffness due to heat hydration in the concrete structure after 5, 7, 14, 21, and 28 days was observed by curing hybrid concrete beams and examining them after 5, 7, 14, 21, and 28 days. On the fifth day, beams were simply supported and allowed to deflect under their weight, and measurements of heat hydration in terms of conductance at frequencies ranging between 1 and 1000 kHz were done. Similarly, days 7, 14, 21, and 28 were done. Day 5 was considered the baseline. It is noted that the PZT sensor placed at an angle of 45 deg is the least effective in recording the incremental changes in hydration that occurred in the concrete beam. The highest quality results were obtained at 90 deg, which is further demonstrated by statistically quantifying the changes using the root-mean-square deviation (RMSD) percentage method and proves to be the most optimized orientation to obtain the stiffness of the hybrid reinforced beam in terms of conductance.
{"title":"The Efficacy of EMI Monitoring of Embedded PZT Sensors in Different Orientations for Hybrid Fibre Reinforced Concrete Structures Hydration","authors":"None Shivangi, Priyanka Singh, Bashar S Mohammed","doi":"10.1115/1.4063845","DOIUrl":"https://doi.org/10.1115/1.4063845","url":null,"abstract":"Abstract In this study, the influence of the orientation of embedded piezoelectric ceramic lead zirconium titanate (PZT) on the mechanical performance of hybrid fiber-reinforced (polypropylene and glass fiber) concrete beams was evaluated. The performance of concrete was evaluated under self-weight, followed by assessing the mechanical property using the electromechanical impedance (EMI) technique after optimization of M30 grade concrete with polypropylene fiber and glass fiber. PZT patches are embedded at different orientations, i.e., 0 deg, 45 deg, and 90 deg, with the axis of the structure for monitoring the hydration of the RC beam. The change in stiffness due to heat hydration in the concrete structure after 5, 7, 14, 21, and 28 days was observed by curing hybrid concrete beams and examining them after 5, 7, 14, 21, and 28 days. On the fifth day, beams were simply supported and allowed to deflect under their weight, and measurements of heat hydration in terms of conductance at frequencies ranging between 1 and 1000 kHz were done. Similarly, days 7, 14, 21, and 28 were done. Day 5 was considered the baseline. It is noted that the PZT sensor placed at an angle of 45 deg is the least effective in recording the incremental changes in hydration that occurred in the concrete beam. The highest quality results were obtained at 90 deg, which is further demonstrated by statistically quantifying the changes using the root-mean-square deviation (RMSD) percentage method and proves to be the most optimized orientation to obtain the stiffness of the hybrid reinforced beam in terms of conductance.","PeriodicalId":52294,"journal":{"name":"Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems","volume":"26 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136233923","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}
Abstract Three pretensioned adjacent concrete box beam bridges were studied with a structural health monitoring (SHM) paradigm based on strain measurements and finite element static analysis. An accurate model for one bridge and an approximate model for the other two were created using ANSYS software. The analysis was used to calculate the strains generated by six concentrated loads that mimic the presence of a truck. Pristine and damage scenarios were implemented, and the associated numerical strains were compared to the experimental strains measured with proprietary wireless sensors during a truck test. As the results from the approximate models revealed that the approximations did not capture the field response of the bridge, the accurate model applied to one bridge was extended to the other two. The comparison between numerical and experimental results revealed the presence of non-critical anomalies related to strain distribution across adjacent beams. Such issues were confirmed with the examination of the historical strains streamed for several months to a repository, using simple data processing strategies. The intellectual contribution of the work resides in the combination of finite element analysis and SHM paradigm on three existing bridges with very similar structural characteristics. This combination showed the limitations of approximated modeling and the possibilities to unfold critical and non-critical issues with SHM.
{"title":"Diagnostics of Concrete Box Beam Bridges using Wireless Sensors and Finite Element Analysis","authors":"Piervincenzo Rizzo, Alireza Enshaeian, Behzad Ghahremani","doi":"10.1115/1.4063804","DOIUrl":"https://doi.org/10.1115/1.4063804","url":null,"abstract":"Abstract Three pretensioned adjacent concrete box beam bridges were studied with a structural health monitoring (SHM) paradigm based on strain measurements and finite element static analysis. An accurate model for one bridge and an approximate model for the other two were created using ANSYS software. The analysis was used to calculate the strains generated by six concentrated loads that mimic the presence of a truck. Pristine and damage scenarios were implemented, and the associated numerical strains were compared to the experimental strains measured with proprietary wireless sensors during a truck test. As the results from the approximate models revealed that the approximations did not capture the field response of the bridge, the accurate model applied to one bridge was extended to the other two. The comparison between numerical and experimental results revealed the presence of non-critical anomalies related to strain distribution across adjacent beams. Such issues were confirmed with the examination of the historical strains streamed for several months to a repository, using simple data processing strategies. The intellectual contribution of the work resides in the combination of finite element analysis and SHM paradigm on three existing bridges with very similar structural characteristics. This combination showed the limitations of approximated modeling and the possibilities to unfold critical and non-critical issues with SHM.","PeriodicalId":52294,"journal":{"name":"Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135993956","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}
Hamad Alnuaimi, Umar Amjad, Novonil Sen, Tribikram Kundu
Abstract The effectiveness of the signal energy-based acoustic source localization approach in practical applications has yet to be established. This is addressed herein by conducting an experimental study on a 500 mm × 500 mm carbon fiber-reinforced polymer plate and generating artificial acoustic events in the plate. Upon acquiring the propagating wave signals at several well-scattered sensors, the signal energy-based approach is applied, and the accuracy of the source localization results is noted. Seven experiments are performed with varying source locations, sensor-plate bonding, and excitation types. This approach has performed well for five experiments with source localization error below 15 mm. The remaining two experiments where the acoustic sources are relatively close to the plate edges compared to the other experiments have, however, produced large localization errors, indicating a scope of improvement in the approach to encompass all situations.
基于信号能量的声源定位方法在实际应用中的有效性尚未得到证实。本文通过在500 mm × 500 mm碳纤维增强聚合物板上进行实验研究，并在板中产生人工声事件来解决这一问题。通过对几个散射良好的传感器上的传播波信号进行采集，采用基于信号能量的方法，对源定位结果的精度进行了验证。七个实验进行了不同的源位置，传感器板键合，和激励类型。该方法在5次源定位误差小于15 mm的实验中取得了良好的效果。然而，与其他实验相比，声源相对靠近板边缘的其余两个实验产生了较大的定位误差，这表明该方法在涵盖所有情况方面还有很大的改进余地。
{"title":"A Note on Performance Assessment of Signal Energy-Based Acoustic Source Localization in a Carbon Fiber-Reinforced Polymer Plate","authors":"Hamad Alnuaimi, Umar Amjad, Novonil Sen, Tribikram Kundu","doi":"10.1115/1.4063698","DOIUrl":"https://doi.org/10.1115/1.4063698","url":null,"abstract":"Abstract The effectiveness of the signal energy-based acoustic source localization approach in practical applications has yet to be established. This is addressed herein by conducting an experimental study on a 500 mm × 500 mm carbon fiber-reinforced polymer plate and generating artificial acoustic events in the plate. Upon acquiring the propagating wave signals at several well-scattered sensors, the signal energy-based approach is applied, and the accuracy of the source localization results is noted. Seven experiments are performed with varying source locations, sensor-plate bonding, and excitation types. This approach has performed well for five experiments with source localization error below 15 mm. The remaining two experiments where the acoustic sources are relatively close to the plate edges compared to the other experiments have, however, produced large localization errors, indicating a scope of improvement in the approach to encompass all situations.","PeriodicalId":52294,"journal":{"name":"Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135351574","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}
A framework to generate simulated X-ray computed tomography (XCT) data of ground truth (denoted here as ‘GT’) flaws was developed for evaluation of flaw detection algorithms using image comparison metrics. The flaws are mimicking some of those found in additively manufactured parts. The simulated flaw structure gives a GT data set with which to quantitatively evaluate, by calculating exact errors, the results of flaw detection algorithms applied to simulated XCT images. The simulated data avoid time-consuming manual voxel labeling steps needed for many physical data sets to generate GT images. The voxelated pore meshes that exactly match GT images avoid approximations due to converting continuum pore meshes to voxelated GT images. Spherical pores of varying sizes were randomly distributed near the surface and interior of a cylindrical part. XCT simulation was carried out on the structure at three different signal-to-noise levels by changing the number of frames integrated for each projection. Two different local thresholding algorithms (a commercial code and the Bernsen method) and a global thresholding algorithm (Otsu) were used to segment images using varying sets of algorithm parameters. The segmentation results were evaluated with various image evaluation metrics, which showed different behaviors for the three algorithms regarding “closeness” to the GT data. An approach to optimize the thresholding parameters is demonstrated for the commercial flaw detection algorithm based on the semantic evaluation metrics. A framework to evaluate pore sizing error and binary probability of detection was further demonstrated to compare the optimization results.
{"title":"Evaluation of flaw detection algorithm using simulated X-ray computed tomography of ground truth data","authors":"F. Kim, A. Pintar, J. H. Scott, E. Garboczi","doi":"10.1115/1.4063170","DOIUrl":"https://doi.org/10.1115/1.4063170","url":null,"abstract":"\u0000 A framework to generate simulated X-ray computed tomography (XCT) data of ground truth (denoted here as ‘GT’) flaws was developed for evaluation of flaw detection algorithms using image comparison metrics. The flaws are mimicking some of those found in additively manufactured parts. The simulated flaw structure gives a GT data set with which to quantitatively evaluate, by calculating exact errors, the results of flaw detection algorithms applied to simulated XCT images. The simulated data avoid time-consuming manual voxel labeling steps needed for many physical data sets to generate GT images. The voxelated pore meshes that exactly match GT images avoid approximations due to converting continuum pore meshes to voxelated GT images. Spherical pores of varying sizes were randomly distributed near the surface and interior of a cylindrical part. XCT simulation was carried out on the structure at three different signal-to-noise levels by changing the number of frames integrated for each projection. Two different local thresholding algorithms (a commercial code and the Bernsen method) and a global thresholding algorithm (Otsu) were used to segment images using varying sets of algorithm parameters. The segmentation results were evaluated with various image evaluation metrics, which showed different behaviors for the three algorithms regarding “closeness” to the GT data. An approach to optimize the thresholding parameters is demonstrated for the commercial flaw detection algorithm based on the semantic evaluation metrics. A framework to evaluate pore sizing error and binary probability of detection was further demonstrated to compare the optimization results.","PeriodicalId":52294,"journal":{"name":"Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems","volume":"54 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84817731","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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