دانلود و نمایش مقالات مرتبط با Structural health monitoring::صفحه 1
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نتیجه جستجو - Structural health monitoring

تعداد مقالات یافته شده: 18
ردیف عنوان نوع
1 Neural network-based seismic response prediction model for building structures using artificial earthquakes
مدل پیش بینی لرزه ای مبتنی بر شبکه عصبی برای سازه های ساختمان با استفاده از زلزله های مصنوعی-2020
In this paper, a new model for predicting seismic responses of buildings based on the correlation of ground motion (GM) and the structure is presented by simulating numerous artificial earthquakes (AEQs). In the model, neural network (NN) configurations representing the relationships between GM characteristics and seismic responses of a structure are developed to predict responses of the structure with only GM data measured by monitoring system in future seismic events. To extract the GM characteristics, multiple AEQs corresponding to the design response spectrum are generated based on probabilistic vibration theory, instead of using historical earthquakes. In the presented NN configurations, GM characteristics including mean and predominant period, significant duration, and peak ground acceleration are established as the input layer and the maximum interstory drift ratio and maximum displacement are established as the output layer. In addition, a new parameter called resonance area is proposed to represent the relationship between a GM and a target structure in the frequency domain and utilized in the NN input layer. By employing the new parameter, dynamic characteristics of the structure are considered in the response estimation of the model with related to GM. The model is applied to seismic response prediction for four multi-degrees-of-freedom (MDOF) structures with different natural periods using 2700 AEQs. The validities of the presented NN models are confirmed by investigating the performance of response prediction. The effectiveness of the resonance area parameter in the NN for predicting the seismic responses is assessed and discussed. Furthermore, the effects of the constitution of NNs and computational costs of those NNs on estimation were investigated. Finally, the presented model is employed for prediction of seismic responses for a structural model of a planar reinforced concrete building structure.
Keywords: Structural health monitoring | Seismic response prediction | Neural network | Artificial earthquake
مقاله انگلیسی
2 A generalizable deep learning framework for localizing and characterizing acoustic emission sources in riveted metallic panels
یک چارچوب یادگیری عمیق قابل تعمیم برای بومی سازی و توصیف منابع انتشار صوتی در صفحه های فلزی پرچین-2019
This paper introduces a deep learning-based framework to localize and characterize acoustic emission (AE) sources in plate-like structures that have complex geometric features, such as doublers and rivet connections. Specifically, stacked autoencoders are pre-trained and utilized in a two-step approach that first localizes AE sources and then characterizes them. To achieve these tasks with only one AE sensor, the paper leverages the reverberation patterns, multimodal characteristics, and dispersive behavior of AE waveforms. The considered waveforms include AE sources near rivet connections, on the surface of the plate-like structure, and on its edges. After identifying AE sources that occur near rivet connections, the proposed framework classifies them into four source-to-rivet distance categories. In addition, the paper investigates the sensitivity of localization results to the number of sensors and compares their localization accuracy with the triangulation method as well as machine learning algorithms, including support vector machine (SVM) and shallow neural network. Moreover, the generalization of the deep learning approach is evaluated for typical scenarios in which the training and testing conditions are not identical. To train and test the performance of the proposed approach, Hsu-Nielsen pencil lead break tests were carried out on two identical aluminum panels with a riveted stiffener. The results demonstrate the effectiveness of the deep learning-based framework for singlesensor, AE-based structural health monitoring of plate-like structures
Keywords: Acoustic emission | Deep learning | Edge reflection | Reverberation patterns | Plate-like structures | Pattern recognition | Stacked autoencoders | uided ultrasonic waves | Machine learning | Structural health monitoring
مقاله انگلیسی
3 Probabilistic active learning: An online framework for structural health monitoring
یادگیری فعال احتمالی: یک چارچوب آنلاین برای نظارت بر سلامت ساختاری-2019
A novel, probabilistic framework for the classification, investigation and labelling of data is suggested as an online strategy for Structural Health Monitoring (SHM). A critical issue for data-based SHM is a lack of descriptive labels (for measured data), which correspond to the condition of the monitored system. For many applications, these labels are costly and/or impractical to obtain, and as a result, conventional supervised learning is not feasible. This fact forces a dependence on outlier analysis, or one-class classifiers, in practical applications, as a means of damage detection. The model suggested in this work, however, allows for the definition of a multi-class classifier, to aid both damage detection and identification, while using a limited number of the most informative labelled data. The algorithm is applied to three datasets in the online setting; the Z24 bridge data, a machining (acoustic emission) dataset, and measurements from ground vibration aircraft tests. In the experiments, active learning is shown to improve the online classification performance for damage detection and classification.
Keywords: Damage detection | Pattern recognition | Semi-supervised learning |Structural health monitoring
مقاله انگلیسی
4 Data interpretation framework integrating machine learning and pattern recognition for self-powered data-driven damage identification with harvested energy variations
چارچوب تفسیر داده ها ادغام یادگیری ماشین و شناخت الگو برای شناسایی آسیب خود محور داده با تغییرات انرژی برداشت شده-2019
Data mining methods have been widely used for structural health monitoring (SHM) and damage identification for analysis of continuous signals. Nonetheless, the applicability and effectiveness of these techniques cannot be guaranteed when dealing with discrete binary and incomplete/missing signals (i.e., not continuous in time). In this paper a novel data interpretation framework for SHM with noisy and incomplete signals, using a through-substrate self-powered sensing technology, is presented within the context of artificial intelligence (AI). AI methods, namely, machine learning and pattern recognition, were integrated within the data interpretation framework developed for use in a practical engineering problem: data-driven SHM of platelike structures. Finite element simulations on an aircraft stabilizer wing and experimental vibration tests on a dynamically loaded plate were conducted to validate the proposed framework. Machine learning algorithms, including support vector machine, k-nearest neighbor, and artificial neural networks, were integrated within the developed learning framework for performance assessment of the monitored structures. Different levels of harvested energy were considered to evaluate the robustness of the SHM system with respect to such variations. Results demonstrate that the SHM methodology employing the proposed machine learning-based data interpretation framework is efficient and robust for damage detection with incomplete and sparse/missing binary signals, overcoming the notable issue of energy availability for smart damage identification platforms being used in structural/infrastructure and aerospace health monitoring. The present study aims to advance data mining and interpretation techniques in the SHM domain, promoting the practical application of machine learning and pattern recognition with incomplete and missing/sparse signals in smart cities and smart infrastructure monitoring.
Keywords: Structural health monitoring | Machine learning | Low-rank matrix completion | Pattern recognition | Self-powered sensors | Plate-like structures | Incomplete signals | Energy harvesting
مقاله انگلیسی
5 Data mining methodology employing artificial intelligence and a probabilistic approach for energy-efficient structural health monitoring with noisy and delayed signals
روش داده کاوی با استفاده از هوش مصنوعی و یک رویکرد احتمالی برای نظارت بر سلامت ساختاری کارآمد با انرژی با سیگنال های پر سر و صدا و تأخیر-2019
Numerous methods have been developed in the context of expert and intelligent systems for structural health monitoring (SHM) with wireless sensor networks (WSNs). However, these techniques have been proven to be efficient when dealing with continuous signals, and the applicability of such expert sys- tems with discrete noisy signals has not yet been explored. This study presents an intelligent data min- ing methodology as part of an expert system developed for SHM with noisy and delayed signals, which are generated by a through-substrate self-powered sensor network. The noted sensor network has been demonstrated as an effective means for minimizing energy consumption in WSNs for SHM. Experimen- tal vibration tests were conducted on a cantilever plate to evaluate the developed expert system for SHM. The proposed data mining method is based on the integration of pattern recognition, an innova- tive probabilistic approach, and machine learning. The novelty of the proposed system for SHM with data interpretation methodology lies in the integration of the noted intelligent techniques on discrete, binary, noisy, and delayed patterns of signals collected from self-powered sensing technology in the applica- tion to a practical engineering problem, i.e., data-driven energy-efficient SHM. Results confirm that the proposed data mining method employing a probabilistic approach can be effectively used to reconstruct delayed and missing signals, thereby addressing the important issue of energy availability for intelligent SHM systems being used for damage identification in civil and aerospace structures. The applicability and effectiveness of the expert system with the data mining approach in detecting damage with noisy sig- nals was demonstrated for plate-like structures with an accuracy of 97%. The present study successfully contributes to advance data mining and signal processing techniques in the SHM domain, indicating a practical application of expert and intelligent systems applied to damage detection in SHM platforms. Findings from this research pave a way for development of the data analysis techniques that can be em- ployed for interpreting noisy and incomplete signals collected from various expert systems such as those being used in intelligent infrastructure monitoring systems and smart cities
Keywords: Structural health monitoring | Data mining | Artificial intelligence | Probabilistic approach | Signal time delay
مقاله انگلیسی
6 Advanced damage detection technique by integration of unsupervised clustering into acoustic emission
تکنیک پیشرفته تشخیص آسیب با ادغام خوشه های بدون نظارت در انتشار آکوستیک-2019
The use of acoustic emission (AE) technique for damage diagnostic is typically challenging due to difficulties associated with discrimination of events that occur during different stages of damage that take place in a material or a structure. In this study, an unsupervised kernel fuzzy c-means pattern recognition analysis and the principal component method were utilized to categorize various damage stages in plain and steel fiber reinforced concrete specimens monitored by AE technique. Enhancement of the discrimination and characterization of damage mechanisms were achieved by processing time and frequency domain data. Both domains (time and frequency) were taken into account to propose new descriptors for crack classification purposes. A cluster of AE data in three classes of Kernel Fuzzy c-means (KFCM) was obtained. The clustered data was subsequently correlated with each particular damage stage for identifying the peak frequency range corresponding to the respective damage stages. Moreover, a novel quantitative technique called Spatial Intelligent b-value (SIb) Analysis was proposed to quantify damage for each stage.
Keywords: Acoustic emission | Torsional loading | Structural health monitoring | Unsupervised pattern recognition | Damage detection | Non-destructive testing
مقاله انگلیسی
7 A generalizable deep learning framework for localizing and characterizing acoustic emission sources in riveted metallic panels
یک چارچوب یادگیری عمیق قابل تعمیم برای محلی سازی و توصیف منابع انتشار صوتی در پانل های فلزی پرچین-2019
This paper introduces a deep learning-based framework to localize and characterize acoustic emission (AE) sources in plate-like structures that have complex geometric features, such as doublers and rivet connections. Specifically, stacked autoencoders are pre-trained and utilized in a two-step approach that first localizes AE sources and then characterizes them. To achieve these tasks with only one AE sensor, the paper leverages the reverberation patterns, multimodal characteristics, and dispersive behavior of AE waveforms. The considered waveforms include AE sources near rivet connections, on the surface of the plate-like structure, and on its edges. After identifying AE sources that occur near rivet connections, the proposed framework classifies them into four source-to-rivet distance categories. In addition, the paper investigates the sensitivity of localization results to the number of sensors and compares their localization accuracy with the triangulation method as well as machine learning algorithms, including support vector machine (SVM) and shallow neural network. Moreover, the generalization of the deep learning approach is evaluated for typical scenarios in which the training and testing conditions are not identical. To train and test the performance of the proposed approach, Hsu-Nielsen pencil lead break tests were carried out on two identical aluminum panels with a riveted stiffener. The results demonstrate the effectiveness of the deep learning-based framework for singlesensor, AE-based structural health monitoring of plate-like structures.
Keywords: Acoustic emission | Deep learning | Edge reflection | Reverberation patterns | Plate-like structures | Pattern recognition | Stacked autoencoders | Guided ultrasonic waves | Machine learning | Structural health monitoring
مقاله انگلیسی
8 An algorithmic framework for reconstruction of time-delayed and incomplete binary signals from an energy-lean structural health monitoring system
یک چارچوب الگوریتمیک برای بازسازی سیگنالهای باینری با تأخیر زمان و ناقص از یک سیستم نظارت بر سلامت ساختاری کم انرژی-2019
Recent advances in energy harvesting technologies have led to the development of self-powered structural health monitoring (SHM) techniques that are power-efficient. Energy-aware data transmission protocols, on the other hand, have evolved due to the emergence of self-powered sensing. The pulse switching architecture is among such protocols employing ultrasonic pulses for event reporting through the substrate material. However, the noted protocol raises the necessity for new types of signal/data interpretation methods for SHM purposes. This is because a system using such technology demands dealing with power budgets for sensing and communication of binary signals that leads to unique time delay constraints. This study presents a novel computational approach to reconstruct delayed and incomplete binary signals provided by a through-substrate ultrasonic self-powered sensor network for SHM of plate-like structures. An algorithmic framework incorporating low-rank matrix completion, a data fusion model, and a statistical approach is proposed for damage identification. Performance and effectiveness of the proposed method for the case of dynamically loaded plates was evaluated using finite element simulations and experimental vibration tests. Results demonstrate that the energy-lean damage identification methodology employing the proposed algorithmic framework enables dependable detection of damage using reconstructed time-delayed binary signals.
Keywords: Structural health monitoring | Matrix completion | Pattern recognition | Self-powered sensor network | Time-delayed binary signals
مقاله انگلیسی
9 Acoustic emission monitoring of containment structures during posttensioning
نظارت بر انتشار صوتی ساختارهای مهار شده در زمان پس از فشار خون-2019
This paper introduces a method based on acoustic emission (AE) to monitor the onset of delamination in posttensioned concrete containment structures. The method is based on clustering AE occurring during post-tensioning and/or re-tensioning such structures. In particular, the investigation is focused on AE of a large-scale, curved concrete wall subject to monotonically increasing prestressing forces. This specimen is a representative of typical cylindrical concrete structures, such as water storage tanks, silos, bins, and nuclear containment structures. To analyze AE data, this paper uses both time-driven and hit-driven features extracted from AE. To this end, a novel approach is proposed to analyze and visualize hit-driven features. To detect and localize such defects, the proposed approach identifies an optimal number of clusters in AE data and interprets each cluster based on the physical mechanism that generates it. Such interpretations are compared with the state of stresses and modified Mohr–Coulomb failure criteria. The results show that the AE events are due to three categories of source mechanisms, micro shear cracking, micro tensile cracking, and macro delamination cracking. To validate the results, comparisons are made with through-thickness expansion measurements of the wall. The results demonstrate that the proposed approach can detect delamination defects and enable decision makers to take remedial and preventive actions.
Keywords: Acoustic emission | K-means clustering | Delamination | Containment structures | Pattern recognition | Concrete structures | Post-tensioning | Structural health monitoring
مقاله انگلیسی
10 Data interpretation framework integrating machine learning and pattern recognition for self-powered data-driven damage identification with harvested energy variations
چارچوب تفسیر داده ها ادغام یادگیری ماشین و شناخت الگوی برای شناسایی آسیب خود محور داده با تغییرات انرژی برداشت شده-2019
Data mining methods have been widely used for structural health monitoring (SHM) and damage identification for analysis of continuous signals. Nonetheless, the applicability and effectiveness of these techniques cannot be guaranteed when dealing with discrete binary and incomplete/missing signals (i.e., not continuous in time). In this paper a novel data interpretation framework for SHM with noisy and incomplete signals, using a through-substrate self-powered sensing technology, is presented within the context of artificial intelligence (AI). AI methods, namely, machine learning and pattern recognition, were integrated within the data interpretation framework developed for use in a practical engineering problem: data-driven SHM of platelike structures. Finite element simulations on an aircraft stabilizer wing and experimental vibration tests on a dynamically loaded plate were conducted to validate the proposed framework. Machine learning algorithms, including support vector machine, k-nearest neighbor, and artificial neural networks, were integrated within the developed learning framework for performance assessment of the monitored structures. Different levels of harvested energy were considered to evaluate the robustness of the SHM system with respect to such variations. Results demonstrate that the SHM methodology employing the proposed machine learning-based data interpretation framework is efficient and robust for damage detection with incomplete and sparse/missing binary signals, overcoming the notable issue of energy availability for smart damage identification platforms being used in structural/infrastructure and aerospace health monitoring. The present study aims to advance data mining and interpretation techniques in the SHM domain, promoting the practical application of machine learning and pattern recognition with incomplete and missing/sparse signals in smart cities and smart infrastructure monitoring.
Keywords: Structural health monitoring | Machine learning | Low-rank matrix completion | Pattern recognition | Self-powered sensors | Plate-like structures | Incomplete signals | Energy harvesting
مقاله انگلیسی
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