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1 |
A robust structural vibration recognition system based on computer vision
یک سیستم قوی تشخیص ارتعاش ساختاری بر اساس بینایی کامپیوتری-2022 Vibration-based structural health monitoring (SHM) systems are useful tools for assessing structural safety performance quantitatively. When employing traditional contact sensors, achieving high-resolution spatial measurements for large-scale structures is challenging, and fixed contact sensors may also lose dependability when the lifetime of the host structure is surpassed. Researchers have paid close attention to computer vision because it is noncontact, saves time and effort, is inexpensive, and has high efficiency in giving visual perception. In advanced noncontact measurements, digital cameras can capture the vibration information of structures remotely and swiftly. Thus, this work studies a system for recognizing structural vibration. The system ensures acquiring high-quality structural vibration signals by the following: 1) Establishing a novel image preprocessing, which includes visual partitioning measurement and image enhancement techniques; 2) initial recognition of structural vibration using phase-based optical flow estimation (POFE), which introduces 2-D Gabor wavelets to extract the independent phase information of the image to track the natural texture targets on the surface of the structure; 3) extracting the practical vibration information of the structure using mode decomposition to remove the complex environment of the camera vibration and other noises; 4) employing phase-based motion magnification (PMM) techniques to magnify small vibration signals, and then recognizing the complete information on the vibration time range of the structure. The research results of the laboratory experiments and field testing conducted under three different cases reveal that the system can recognize structural vibration in complicated environments.
keywords: Computer vision | Phase | Motion estimation | Motion magnification | Mode decomposition | Structural vibration |
مقاله انگلیسی |
2 |
A computer vision-based method for bridge model updating using displacement influence lines
یک روش مبتنی بر بینایی کامپیوتری برای بهروزرسانی مدل پل با استفاده از خطوط موثر جابجایی-2022 This paper presents a new computer vision-based method that simultaneously provides the moving vehicle’s
tire loads, the location of the loads on a bridge, and the bridge’s response displacements, based on which the
bridge’s influence lines can be constructed. The method employs computer vision techniques to measure the
displacement influence lines of the bridge at different target positions, which is then later used to perform
model updating of the finite element models of the monitored structural system.
The method is enabled by a novel computer vision-based vehicle weigh-in-motion method which the coauthors recently introduced. A correlation discriminating filter tracker is used to estimate the displacements at target points and the location of single or multiple moving loads, while a low-cost, non-contact weigh-in-motion technique evaluates the magnitude of the moving vehicle loads. The method described in this paper is tested and validated using a laboratory bridge model. The system was loaded with a vehicle with pressurized tires and equipped with a monitoring system consisting of laser displacement sensors, accelerometers, and cameras. Both artificial and natural targets were considered in the experimental tests to track the displacements with the cameras and yielded robust results consistent with the laser displacement measurements. The extracted normalized displacement influence lines were then successfully used to perform model updating of the structure. The laser displacement sensors were used to validate the accuracy of the proposed computer vision-based approach in deriving the displacement measurements, while the accelerometers were used to derive the system’s modal properties employed to validate the updated finite element model. As a result, the updated finite element model correctly predicted the bridge’s displacements measured during the tests. Furthermore, the modal parameters estimated by the updated finite element model agreed well with those extracted from the experimental modal analysis carried out on the bridge model. The method described in this paper offers a low-cost non-contact monitoring tool that can be efficiently used without disrupting traffic for bridges in model updating analysis or long-term structural health monitoring. keywords: Computer vision | Displacement influence line | Vehicle weigh-in-motion | Structural identification | Finite element method model | Model updating | Modal analysis | Bridge systems |
مقاله انگلیسی |
3 |
Computer vision-based illumination-robust and multi-point simultaneous structural displacement measuring method
روش اندازه گیری جابجایی ساختاری همزمان با روشنایی مبتنی بر بینایی کامپیوتری-2022 Computer vision-based techniques for structural displacement measurement are rapidly becoming
popular in civil structural engineering. However, most existing computer vision-based displace-
ment measurement methods require man-made targets for object matching or tracking, besides
usually the measurement accuracies are seriously sensitive to the ambient illumination variations.
A computer vision-based illumination robust and multi-point simultaneous measuring method is
proposed for structural displacement measurements. The method consists of two part, one is for
segmenting the beam body from its background, the segmentation is perfectly carried out by fully
convolutional network (FCN) and conditional random field (CRF); another is digital image cor-
relation (DIC)-based displacement measurement. A simply supported beam is built in laboratory.
The accuracy and illumination robustness are verified through three groups of elaborately
designed experiments. Due to the exploitation of FCN and CRF for pixel-wise segmentation,
numbers of locations along with the segmented beam body can be chosen and measured simul-
taneously. It is verified that the method is illumination robust since the displacement measure-
ments are with the smallest fluctuations to the illumination variations. The proposed method does
not require any man-made targets attached on the structure, but because of the exploitation of
DIC in displacement measurement, the regions centered on the measuring points need to have
texture feature. keywords: پایش سلامت سازه | اندازه گیری جابجایی | بینایی کامپیوتر | یادگیری عمیق | تقسیم بندی شی | همبستگی تصویر دیجیتال | Structural health monitoring | Displacement measurement | Computer vision | Deep learning | Object segmentation | Digital image correlation |
مقاله انگلیسی |
4 |
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 |
مقاله انگلیسی |
5 |
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 |
مقاله انگلیسی |
6 |
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 |
مقاله انگلیسی |
7 |
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 |
مقاله انگلیسی |
8 |
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 |
9 |
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 |
10 |
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 |