e-Journal of Nondestructive Testing最新文献

Development of a Bayesian Framework for Kinematic Data Fusion 为运动学数据融合开发贝叶斯框架

e-Journal of Nondestructive Testing Pub Date : 2024-07-01 DOI: 10.58286/29643

Alessandro Lotti, Stefano Zorzi, D. Tonelli, Enrico Tubaldi, Daniele Zonta

{"title":"Development of a Bayesian Framework for Kinematic Data Fusion","authors":"Alessandro Lotti, Stefano Zorzi, D. Tonelli, Enrico Tubaldi, Daniele Zonta","doi":"10.58286/29643","DOIUrl":"https://doi.org/10.58286/29643","url":null,"abstract":"\u0000Structural health monitoring (SHM) is widely used for assessing the condition of bridges at risk. Traditional SHM techniques rely on point-wise information provided by individual sensors placed at strategic locations. However, a more comprehensive assessment of the bridge state can be achieved through data fusion, integrating information from different sensors.\u0000\u0000This article presents a Bayesian framework data fusion method that combines information from various measurements to improve the knowledge of the structural deformation state. The proposed framework identifies key deformation parameters by exploiting a simplified model that describes the system deformation state and uses an extensive set of data, including prisms, extensometers, tiltmeters, and beyond. Moreover, this approach provides a continuous knowledge of the deformation state, and reduces the uncertainties associated with individual sensor measurements. The framework developed is initially applied to a simulated case study of a simply supported beam, and then to the Colle Isarco viaduct, a highway bridge equipped with an extensive monitoring system.\u0000\u0000\u0000","PeriodicalId":482749,"journal":{"name":"e-Journal of Nondestructive Testing","volume":"20 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141848598","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}

引用次数: 0

Experimental Verification on Deep Learning based Monitoring Algorithms for Early Detection of Damage in Buried Pipelines 基于深度学习的监测算法在早期探测埋地管道损坏方面的实验验证

e-Journal of Nondestructive Testing Pub Date : 2024-07-01 DOI: 10.58286/29876

Sun-Ho Lee, Choon-su Park, D. Yoon

{"title":"Experimental Verification on Deep Learning based Monitoring Algorithms for Early Detection of Damage in Buried Pipelines","authors":"Sun-Ho Lee, Choon-su Park, D. Yoon","doi":"10.58286/29876","DOIUrl":"https://doi.org/10.58286/29876","url":null,"abstract":"\u0000Recent increases in buried pipeline damage accidents due to third-party interference have significantly heightened attention towards buried pipeline monitoring. Especially, as the sudden damage can lead to large-scale leakage, there is a necessity for preemptive response and maintenance. However, the application of a structural health monitoring approach is difficult, since the extensive network of buried pipelines, stretching over thousands of kilometers, exhibits diverse noise environments and propagation characteristics. As a result, challenges within the buried pipeline system frequently lead to damages being overlooked. In this study, introduces a deep learning-based pipeline damage monitoring algorithm, specifically designed to early detection of accidents caused by third-party interference. This algorithm integrates a CNN-based anomaly detection model, advanced signal processing for data preprocessing, and TDoA-based source localization. The training and test data set are the acquisition under completely independent conditions, which has been experimentally validate for applicability across various environments for buried pipelines. Moreover, both the training and test dataset acquisition were performed using accelerometers on in-service buried pipelines, each with diameters of 1,100 mm, 1,200 mm, and 2,200 mm, extending over lengths ranging from approximately 200 to 500 meters. Despite the independent conditions of the datasets, our study yielded over 95% accuracy in early detection, with the results being in good agreement with the actual excavate locations.\u0000","PeriodicalId":482749,"journal":{"name":"e-Journal of Nondestructive Testing","volume":"21 81","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141843318","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}

引用次数: 0

Predicting the Dynamic Behaviour of a Concrete Dam using Statistical and Machine Learning Models 利用统计和机器学习模型预测混凝土大坝的动态行为

e-Journal of Nondestructive Testing Pub Date : 2024-07-01 DOI: 10.58286/29856

Sérgio Pereira, Juan Mata, Filipe Magalhães, J. Gomes, Álvaro Cunha

{"title":"Predicting the Dynamic Behaviour of a Concrete Dam using Statistical and Machine Learning Models","authors":"Sérgio Pereira, Juan Mata, Filipe Magalhães, J. Gomes, Álvaro Cunha","doi":"10.58286/29856","DOIUrl":"https://doi.org/10.58286/29856","url":null,"abstract":"\u0000Operational Modal Analysis is a reliable methodology for the assessment of civil engineering structures, allowing for the accurate definition of their dynamic behavior. Additionally, since it does not require the use of artificial excitation, it becomes a cost-effective choice for the performance of singular tests, as well as a consistent option for the long-term continuous monitoring of structures.\u0000\u0000Nevertheless, the modal characteristics of structures are affected by environmental and operational conditions, concealing the variations that could emerge due to abnormal behavior. With respect to concrete dams, factors such as temperature and the level of water in the reservoir exert a pronounced influence in the evolution of natural frequencies, increasing data variability and camouflaging the behavior that would be discerned under stable conditions.\u0000\u0000In this context, the current study seeks to examine the ability of statistical and machine learning tools to mitigate the effects of external conditions on the modal properties of concrete dams, specifically natural frequencies. To achieve this objective, the efficiency of methods incorporating measurements of variables impacting the structure, such as Multiple Linear Regressions and Neural Networks, is compared to that of tools not needing these inputs, as is the case of Principal Components Analysis and the Minimum Mean Square Error estimator. \u0000\u0000Experimental data obtained during the continuous dynamic monitoring of a concrete dam in Portugal is used as a case study.\u0000\u0000\u0000","PeriodicalId":482749,"journal":{"name":"e-Journal of Nondestructive Testing","volume":"65 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141847739","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}

引用次数: 0

Simulation-based Optimal Sensor Placement in Tunnel Structures considering Uncertain in-situ Conditions and Multiple Loading Scenarios 基于仿真的隧道结构中传感器优化布置（考虑不确定的原位条件和多种加载情况

e-Journal of Nondestructive Testing Pub Date : 2024-07-01 DOI: 10.58286/29650

Nicola Gottardi, Steffen Freitag, Gunther Meschke

{"title":"Simulation-based Optimal Sensor Placement in Tunnel Structures considering Uncertain in-situ Conditions and Multiple Loading Scenarios","authors":"Nicola Gottardi, Steffen Freitag, Gunther Meschke","doi":"10.58286/29650","DOIUrl":"https://doi.org/10.58286/29650","url":null,"abstract":"\u0000The expansion of the underground infrastructure and the necessity to maintain the full functionality of the existing tunnels highlights the role of structural health monitoring in keeping track of the behaviour of the structure over time. \u0000In the study, the focus is on segmental tunnel lining for deep and long tunnels. The aim of this research is to investigate the best position for sensor placement based on the potential loading scenarios that might insist on the tunnel structure. Since for the evaluation of the lining safety we are interested in the maximum stresses reached in the structure, which might lead to durability critical cracking or crushing of concrete, a method is presented to suggest optimal positions for strain gauges. Due to uncertain geological conditions, multiple loading configurations acting on the tunnel structure are simulated using a finite element model. The results are employed to better identify the sensor locations, on the basis of where damage is prone to occur and where maximum information about the stress state in the structure can be inferred. \u0000The results obtained by the framework are discussed in light of a practical engineering perspective and the advantages as well as the limitations of the proposed approach are expounded.\u0000","PeriodicalId":482749,"journal":{"name":"e-Journal of Nondestructive Testing","volume":"3 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141838662","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}

引用次数: 0

Acoustic emission monitoring during elastic bending of cross laminated timber-steel composite beams 交叉层压木材-钢材复合梁弹性弯曲过程中的声发射监测

e-Journal of Nondestructive Testing Pub Date : 2024-07-01 DOI: 10.58286/29578

Gerd Manthei, Dennis Bohn, Noah Böhm, Bertram Kühn, Achim Vogelsberg

{"title":"Acoustic emission monitoring during elastic bending of cross laminated timber-steel composite beams","authors":"Gerd Manthei, Dennis Bohn, Noah Böhm, Bertram Kühn, Achim Vogelsberg","doi":"10.58286/29578","DOIUrl":"https://doi.org/10.58286/29578","url":null,"abstract":"\u0000Reinforcement of cross laminated timber (CLT) floors by a composite design with steel girders can provide an innovative and sustainable alternative for highly stressable floor systems made of steel and concrete for spans exceeding 8 m. To make this construction method economical and resource efficient, a significant contribution of the CLT panel to the composite stiffness is necessary. One key aspect of the composite design is the formation of the shear-resistant connection between CLT panel and steel girder.\u0000The effect of both composites on the bending stiffness of CLT-steel composite beams was investigated in large-scale 4-point bending tests with spans of 8.1 m and 10.6 m. To monitor the damage of the specimen using acoustic emission (AE), a network with 16 AE sensors were attached to the surface of the specimen in area of largest deflection and highest load. For this purpose broadband AE sensors with a measurement frequency of up to 200 kHz were used.\u0000The ultimate failure of the specimen occurred at a maximum test load of approximately 197 kN. The highest deflection at this force was about 192 mm. During the tests, which lasted about 50 minutes, more than 8,500 AE events were detected. In this contribution, the relationship between the mechanical quantities and the AE activity is shown.\u0000\u0000","PeriodicalId":482749,"journal":{"name":"e-Journal of Nondestructive Testing","volume":"9 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141853903","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}

引用次数: 0

An Evaluation Framework for Deep Learning-Based Anomaly Detection in Structural Health Monitoring. 基于深度学习的结构健康监测异常检测评估框架。

e-Journal of Nondestructive Testing Pub Date : 2024-07-01 DOI: 10.58286/29573

Yacine Bel-Hadj, W. Weijtjens, C. Devriendt

{"title":"An Evaluation Framework for Deep Learning-Based Anomaly Detection in Structural Health Monitoring.","authors":"Yacine Bel-Hadj, W. Weijtjens, C. Devriendt","doi":"10.58286/29573","DOIUrl":"https://doi.org/10.58286/29573","url":null,"abstract":"\u0000The task of evaluating deep learning algorithms in the context of Structural Health Monitoring (SHM) for damage detection is made particularly challenging by the limited availability of empirical data from damaged structures. Making it impossible to assert whether the trained algorithm would be able to pick up changes due to (unseen) damage. This study puts forth a methodology that employs synthesized anomalies to advance our understanding of the specific conditions under which deep learning algorithms for anomaly detection are succesfull and when they prove to be insensitive to damage. \u0000\u0000\u0000\u0000This research aims to develop a comprehensive deep learning model that utilizes raw data as its input, negating the need for specialized preprocessing or the development of anomaly indices that are constrained to specific types of anomalies. Central to our methodology is the introduction of simulated damage into the data set through various manipulations. This evaluation method could be generalized across diverse sensor types, such as accelerometer data (ACCs) and Fiber Bragg Sensors (FBGs). \u0000\u0000\u0000\u0000As a case study, we focus on accelerometer data, utilizing Power Spectral Density (PSD) as the input for a deep learning-based anomaly detection algorithm. The study employs both attenuation and the addition of harmonics at varying levels and frequencies to mimic anomalies, thereby investigation the model's areas of sensitivity. To empirically validate the approach, an 8-degree-of-freedom simulated system is used, and environmental effect is modelled by a linear stiffness reduction across all degrees of freedom (DOFs). Structural damage is then simulated by altering the stiffness of a specific DOF. \u0000\u0000\u0000\u0000Our results demonstrate a robust correlation between the model’s success in identifying these synthesized anomalies and its capability to detect actual structural damage. This correlation serves as a valuable guide for hyperparameter optimization. \u0000","PeriodicalId":482749,"journal":{"name":"e-Journal of Nondestructive Testing","volume":"80 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141843878","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}

引用次数: 0

Advancing X-ray Inspection with Deep Learning De-noising Technology 利用深度学习去噪技术推进 X 射线检测工作

e-Journal of Nondestructive Testing Pub Date : 2024-05-01 DOI: 10.58286/29550

Sara Ziliani

引用次数: 0

Non-destructive larval infestation detection in pear fruits using deep learning and x-ray CT generated radiographs 利用深度学习和 X 射线 CT 生成的射线照片对梨果中的幼虫虫害进行无损检测

e-Journal of Nondestructive Testing Pub Date : 2024-03-01 DOI: 10.58286/29252

Jiaqi He, Simon Verlinde, Tim Belien, A. Alhmedi, Pieter Verboven, Bart M. Nicolai

引用次数: 0

Comparison of Different Quantum Computing Devices for Optimization of Computed Tomography Data Acquisition 不同量子计算设备在优化计算机断层扫描数据采集方面的比较

e-Journal of Nondestructive Testing Pub Date : 2024-03-01 DOI: 10.58286/29236

Dimitri Prjamkov, Kilian Dremel, Thomas Lang, Simon Semmler, Mareike Weule, Markus Firsching, S. Kasperl, Theobald O.J. Fuchs