Mechanical properties and electrical resistivity of multiwall carbon nanotubes incorporated into high calcium fly ash geopolymer
Mechanical properties and electrical resistivity of multiwall carbon nanotubes incorporated into high calcium fly ash geopolymer-2021
High calcium fly ash (HCF) is a pozzolan material and is available in large quantity in Thailand due to the existence of coal-based electrical power plants. It is used as a supplemental material to partially replace cement content in concrete as a movement toward concrete sustainability. In order to lift the sustainability level, a cementitious material without Portland cement called ‘geopolymer’ was introduced. Geopolymer can be produced from raw materials containing high alumina and silica, for example fly ash, blast furnace slag, and metakaolin. For high calcium fly ash geopolymer (HCFG), the unique properties include fast setting, and high early strength. In this study, in order to enhance the properties of HCF geopolymer, multiwall carbon nanotubes (MWCNTs) were introduced into the matrix. In addition to the investigation into basic properties, the effect of MWCNT on electrical resistivity was also investigated to determine its potential use in piezoelectric sensor applications. The results showed that the addition of MWCNTs improved the mechanical properties of HCFG. The maximum compressive and flexural strengths were obtained with a mix containing 0.2% MWCNTs. The EDS test also indicated the increase in geopolymerization and hydration products with the addition of MWCNTs. To investigate the piezoelectricity potential, the electrical resistivity under different levels of compression loads was investigated. The resistivity decreased with the increasing load level up to the first crack, and then decreased. The changes in electrical resistivity indicated the potential use of HCFG incorporated MWCNTs in self-sensing for structural health monitoring.
Keywords: Geopolymer | High calcium fly ash | Multiwall carbon nanotube | Electrical resistivity
A portable three-component displacement measurement technique using an unmanned aerial vehicle (UAV) and computer vision: A proof of concept
تکنیک اندازه گیری جابجایی سه جزء قابل حمل با استفاده از هواپیمای بدون سرنشین (UAV) و بینایی ماشین: اثبات مفهوم-2021
This study proposes a new remote sensing technique to measure three-component (3C) dynamic displacement of three-dimensional (3D) structures. A sensing system with a UAV platform and contact-free sensors (e.g., optical and infrared (IR) cameras) is employed to provide a portable and convenient alternative to conventional approaches that require sensor installation on a structure. The original contributions of this study include (1) integrating both optical and IR cameras with a UAV platform to measure dynamic structural response, and (2) developing new data post-processing algorithms (based on target identification, Direct Linear Transformation, and active stereo vision) to simultaneously extract the 3C displacement of a 3D structure from optical and IR videos, which presents a unique advantage compared to the existing UAV-based displacement measurement techniques that allow the measurements in only one or two directions using optical cameras or laser sensors. The efficacy of the proposed technique is validated through laboratory experiments.
Keywords: Computer vision | Structural health monitoring (SHM) | Unmanned aerial vehicle (UAV) | Dynamic displacement | Bridge inspection
An object detection approach for detecting damages in heritage sites using 3-D point clouds and 2-D visual data
یک رویکرد تشخیص شی برای تشخیص خسارت در میراث با استفاده از ابرهای نقطه سه بعدی و داده های بصری دو بعدی-2021
We propose a novel pipeline for structural damage detection on surfaces of complex heritage structures using visual 2D and 3D data. We use deep learning and computer vision to detect damages in images of heritage sites, and subsequently localize the detected damage on corresponding 3D models. This enables intuitive visualization, giving a concrete idea about the extent of damage in 3D space. To train deep learning models for damage detection, we curate a labeled database consisting of images of Ayutthaya – Wat Phra Si Sanphet Temple (situated in Thailand), essentially converting the damage detection problem into an object detection task. We consider the two most common kinds of damages occurring in heritage structures, namely Crack and Spalling. Models trained using these database are experimentally observed to be robust as they can detect damages among intricate architectural designs and backgrounds. Post- training, we test the model’s domain transferability by detecting damages on unseen rendered images from 3D Models of UNESCO World Heritage Site – Hampi (situated in India). We also present a comparison of the performance of different conﬁgurations of Faster-RCNN as the damage detection model over heritage structure data and demonstrate the obtained results.© 2021 Elsevier Masson SAS. All rights reserved.
Keywords: Deep learning | Object detection | Computer vision | 3D point clouds | Image rendering | Structural health monitoring
Machine learning based real-time visible fatigue crack growth detection
یادگیری ماشین مبتنی بر تشخیص رشد ترک خستگی قابل مشاهده در زمان واقعی-2021
Many large-scale and complex structural components are applied in the aeronautics and automobile industries. However, the repeated alternating or cyclic loads in service tend to cause unexpected fatigue fracture. Therefore, developing real-time and visible monitoring methods for fatigue crack initiation and propagation is critically important for the structural safety. This paper proposes a machine learning-based fatigue crack growth detection method that combines computer vision and machine leaning. In our model, computer vision is used for data creation, and the machine learning model is used for crack detection, then computer vision is used for marking and analyzing the crack growth path and length. We apply seven models for the crack classiﬁcation and ﬁnd that the decision tree is the best model in this research. The experimental results prove the effectiveness of our method and the crack length measurement accuracy achieved is 0.6 mm. Furthermore, the slight machine learning models help us realize the real-time and visible fatigue crack detection.
Keywords: Fatigue crack | Growth prediction | Mechanoresponsive luminogen | Structural health monitoring | Computer vision | Machine learning
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
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
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
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
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
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