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Digital image correlation-based structural state detection through deep learning

《结构与土木工程前沿（英文）》 2022年 第16卷 第1期   页码 45-56 doi: 10.1007/s11709-021-0777-x

摘要： This paper presents a new approach for automatical classification of structural state through deep learning. In this work, a Convolutional Neural Network (CNN) was designed to fuse both the feature extraction and classification blocks into an intelligent and compact learning system and detect the structural state of a steel frame; the input was a series of vibration signals, and the output was a structural state. The digital image correlation (DIC) technology was utilized to collect vibration information of an actual steel frame, and subsequently, the raw signals, without further pre-processing, were directly utilized as the CNN samples. The results show that CNN can achieve 99% classification accuracy for the research model. Besides, compared with the backpropagation neural network (BPNN), the CNN had an accuracy similar to that of the BPNN, but it only consumes 19% of the training time. The outputs of the convolution and pooling layers were visually displayed and discussed as well. It is demonstrated that: 1) the CNN can extract the structural state information from the vibration signals and classify them; 2) the detection and computational performance of the CNN for the incomplete data are better than that of the BPNN; 3) the CNN has better anti-noise ability.

关键词： structural state detection     deep learning     digital image correlation     vibration signal     steel frame    

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A study on fatigue damage of asphalt mixture under different compaction using 3D-microstructural characteristics

Jing HU, Pengfei LIU, Bernhard STEINAUER

《结构与土木工程前沿（英文）》 2017年 第11卷 第3期   页码 329-337 doi: 10.1007/s11709-017-0407-9

摘要： The aim of this paper is investigating the microstructural characteristics of asphalt mixture under different compaction powers. In order to achieve this aim, a test track was built to provide asphalt mixture specimens and X-ray computed tomography (XCT) device was used to scan the internal structure. The aggregate particles and air-voids were extracted using Digital Image Processing (DIP), so the relationship between compaction and air-voids was determined at first, and then, the effect of aggregate particles on the morphology of air-voids can be evaluated, finally, fatigue properties of asphalt mixture with different air-void ratio were measured by indirect tensile fatigue test as well. The research results release the distribution of microstructures in asphalt pavement. 3D fractal dimension is an effective indicator to quantize the complexity of aggregate particles and air-voids; suffering the same compaction power, aggregates cause different constitutions of air-voids in asphalt mixture; investigation in this paper can present the essential relationship between microstructures and fatigue properties.

关键词： asphalt mixture     microstructure     morphology     digital image processing     fatigue damage    

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Deformation field and crack analyses of concrete using digital image correlation method

Yijie HUANG, Xujia HE, Qing WANG, Jianzhuang XIAO

《结构与土木工程前沿（英文）》 2019年 第13卷 第5期   页码 1183-1199 doi: 10.1007/s11709-019-0545-3

摘要： The study on the deformation distribution and crack propagation of concrete under axial compression was conducted by the digital image correlation (DIC) method. The main parameter in this test is the water-cement ( / ) ratio. The novel analysis process and numerical program for DIC method were established. The displacements and strains of coarse aggregate, and cement mortar and interface transition zone (ITZ) were obtained and verified by experimental results. It was found that the axial displacement distributed non-uniformly during the loading stage, and the axial displacements of ITZs and cement mortar were larger than that of coarse aggregates before the occurrence of macro-cracks. The effect of / on the horizontal displacement was not obvious. Test results also showed that the transverse and shear deformation concentration areas (DCAs) were formed when stress reached 30%–40% of the peak stress. The transverse and shear DCAs crossed the cement mortar, and ITZs and coarse aggregates. However, the axial DCA mainly surrounded the coarse aggregate. Generally, the higher / was, the more size and number of DCAs were. The crack propagations of specimens varied with the variation of / . The micro-crack of concrete mainly initiated in the ITZs, irrespective of the / . The number and distribution range of cracks in concrete with high / were larger than those of cracks in specimen adopting low / . However, the value and width of cracks in high / specimen were relatively small. The / had an obvious effect on the characteristics of concrete deterioration. Finally, the characteristics of crack was also evaluated by comparing the calculated results.

关键词： deformation filed distribution     crack development     digital image correlation method     mechanical properties     water-cement ratio     characteristics of deformation and crack    

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a Novel Fuzzy Clustering Method Based on Chaos Immune Evolutionary Algorithm for Edge Detection in ImageProcessing

《机械工程前沿（英文）》 2006年 第1卷 第1期   页码 85-89 doi: 10.1007/s11465-005-0023-6

摘要：

A novel fuzzy clustering method based on chaos immune evolutionary algorithm (CIEFCM) is presented to solve fuzzy edge detection problems in image processing. In CIEFCM, a tiny disturbance is added to a filial generation group using a chaos variable and the disturbance amplitude is adjusted step by step, which greatly improves the colony diversity of the immune evolution algorithm (IEA). The experimental results show that the method not only can correctly detect the fuzzy edge and exiguous edge but can evidently improve the searching efficiency of fuzzy clustering algorithm based on IEA.

关键词： disturbance amplitude     disturbance     diversity     generation     processing    

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fracture process in forta and polypropylene fiber-reinforced concrete using experimental analysis and digitalimage correlation

Seyed Hamid KALALI; Hamid ESKANDARI-NADDAF; Seyed Ali EMAMIAN

《结构与土木工程前沿（英文）》 2022年 第16卷 第12期   页码 1633-1652 doi: 10.1007/s11709-022-0876-3

摘要： This paper aims to characterize the evolution of the fracture process and the cracking behavior in Forta-Ferro (FF) and Polypropylene (PP) fiber-reinforced concrete under the uniaxial compressive loading using experimental analysis and digital image correlation (DIC) on the surface displacement. For this purpose, 6 mix designs, including two FF volume fractions of 0.10, and 0.20% and three PP volume fractions of 0.20, 0.30, and 0.40%, in addition to a control mix were evaluated according to compressive strength, modulus of elasticity, toughness index, and stress-strain curves. The influence of fibers on the microstructural texture of specimens was analyzed by scanning electron microscope (SEM) imaging. Results show that FF fiber-reinforced concrete specimens demonstrated increased ductility and strength compared to PP fiber. DIC results revealed that the major crack and fracture appeared at the peak load of the control specimen due to brittleness and sudden gain of large lateral strain, while a gradual increase in micro-crack quantity at 75% of peak load was observed in the fiber specimens, which thenbegan to connect with each other up to the final fracture. The accuracy of the results supports DIC as a reliable alternative for the characterization of the fracture process in fiber-reinforced concrete.

关键词： fiber-reinforced concrete     forta-ferro and polypropylene fiber     fracture process     cracking behavior     digital image correlation    

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Deep learning in digital pathology image analysis: a survey

Shujian Deng, Xin Zhang, Wen Yan, Eric I-Chao Chang, Yubo Fan, Maode Lai, Yan Xu

《医学前沿（英文）》 2020年 第14卷 第4期   页码 470-487 doi: 10.1007/s11684-020-0782-9

摘要： deep learning (DL) has achieved state-of-the-art performance in many digital pathology analysis tasks. Traditional methods usually require hand-crafted domain-specific features, and DL methods can learn representations without manually designed features. In terms of feature extraction, DL approaches are less labor intensive compared with conventional machine learning methods. In this paper, we comprehensively summarize recent DL-based image analysis studies in histopathology, including different tasks (e.g., classification, semantic segmentation, detection, and instance segmentation) and various applications (e.g., stain normalization, cell/gland/region structure analysis). DL methods can provide consistent and accurate outcomes. DL is a promising tool to assist pathologists in clinical diagnosis.

关键词： pathology     deep learning     segmentation     detection     classification    

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Three-dimensional reconstruction of light microscopy image sections: present and future

null

《医学前沿（英文）》 2015年 第9卷 第1期   页码 30-45 doi: 10.1007/s11684-014-0337-z

摘要：

Three-dimensional (3D) image reconstruction technologies can reveal previously hidden microstructures in human tissue. However, the lack of ideal, non-destructive cross-sectional imaging techniques is still a problem. Despite some drawbacks, histological sectioning remains one of the most powerful methods for accurate high-resolution representation of tissue structures. Computer technologies can produce 3D representations of interesting human tissue and organs that have been serial-sectioned, dyed or stained, imaged, and segmented for 3D visualization. 3D reconstruction also has great potential in the fields of tissue engineering and 3D printing. This article outlines the most common methods for 3D tissue section reconstruction. We describe the most important academic concepts in this field, and provide critical explanations and comparisons. We also note key steps in the reconstruction procedures, and highlight recent progress in the development of new reconstruction methods.

关键词： microtomy     3D imaging     computer-assisted image processing     3D printing     tissue scaffold    

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polypropylene-reinforced ultra-high-performance concrete through numerical analyses and experimental multi-target digitalimage correlation

《结构与土木工程前沿（英文）》   页码 1228-1248 doi: 10.1007/s11709-023-0931-8

摘要： This study presents experimental and numerical investigations on the mechanical properties of ultra-high-performance concrete (UHPC) reinforced with single and hybrid micro- and macro-steel and polypropylene fibers. For this purpose, a series of cubic, cylindrical, dog-bone, and prismatic beam specimens (total fiber by volume = 1%, and 2%) were tested under compressive, tensile, and flexural loadings. A method, namely multi-target digital image correlation (MT-DIC) was used to monitor the displacement and deflection values. The obtained experimental data were subsequently used to discuss influential parameters, i.e., flexural strength, tensile strength, size effect, etc. Numerical analyses were also carried out using finite element software to account for the sensitivity of different parameters. Furthermore, nonlinear regression analyses were conducted to obtain the flexural load-deflection curves. The results showed that the MT-DIC method was capable of estimating the tensile and flexural responses as well as the location of the crack with high accuracy. In addition, the regression analyses showed excellent consistency with the experimental results, with correlation coefficients close to unity. Furthermore, size-effect modeling revealed that modified Bazant theory yielded the best estimation of the size-effect phenomenon compared to other models.

关键词： UHPC     MT-DIC     flexural behavior     tensile behavior     steel fiber     polypropylene fiber    

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简述图像被动取证技术 Review

林祥, 李建华, 王士林, 刘伟聪, 程峰, 黄潇洒

《工程（英文）》 2018年 第4卷 第1期   页码 29-39 doi: 10.1016/j.eng.2018.02.008

摘要：

随着图像编辑和篡改技术越发成熟，数字图像的真实性通常难以从视觉上直接分辨。为了检测数字图像篡改，在过去十年内，已经出现多种数字图像取证技术。其中，主动取证方法需要嵌入额外信息。相比之下，被动取证方法因为其适用场景更广而更加流行，也吸引了学术界和工业界越来越多的研究兴趣。一般而言，被动取证基于以下依据来检测图像伪造：图像采集或存储过程中会在原始图像中遗留某些固有的模式特征，或者在图像存储或编辑过程中会留下某些特定的模式特征。通过分析上述模式特征，可以验证图像的真实性。被动数字取证方法正处于快速发展之中，本文简要回顾其发展，并全面介绍该研究领域的最新进展。根据所追踪痕迹的不同，这些取证方法被分为3 类，即采集痕迹法、存储痕迹法和编辑痕迹法。我们将逐一详解这些方法的取证场景、基本原理和研究现状。此外，我们讨论了当前图像取证方法的主要局限，并指出了该领域一些可能的研究方向和关键问题。

关键词： 数字图像取证     图像篡改检测     多媒体安全    

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Three-scale stochastic homogenization of elastic recycled aggregate concrete based on nano-indentation digital

Chen WANG, Yuching WU, Jianzhuang XIAO

《结构与土木工程前沿（英文）》 2018年 第12卷 第4期   页码 461-473 doi: 10.1007/s11709-017-0441-7

摘要： In this paper, three-scale stochastic elastic finite element analyses are made for recycled aggregate concrete (RAC) based on nano-indentation digital images. The elastic property of RAC contains uncertainties across scales. It has both theoretical and practical values to model and predict its mechanical performance. Based on homogenization theory, effective stochastic elastic moduli of RAC at three different scales are obtained using moving window technique, nano-indentation digital images, and Monte-Carlo method. It involves the generation of a large number of random realizations of microstructure geometry based on different volume fraction of the inclusions and other parameters. The mean value, coefficient of variation and probability distribution of the effective elastic moduli are computed considering the material multiscale structure. The microscopic randomness is taken into account, and correlations of RAC among five phases are investigated. The effective elastic properties are used to obtain the global behavior of a composite structure. It is indicated that the response variability can be considerably affected by replacement percentage of recycled aggregates.

关键词： RAC     nano-indentation digital image     multiscale     microscopic randomness     homogenization    

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Assessing compressive strengths of mortar and concrete from digital images by machine learning techniques

Amit SHIULY; Debabrata DUTTA; Achintya MONDAL

《结构与土木工程前沿（英文）》 2022年 第16卷 第3期   页码 347-358 doi: 10.1007/s11709-022-0819-z

摘要： Compressive strength is the most important metric of concrete quality. Various nondestructive and semi-destructive tests can be used to evaluate the compressive strength of concrete. In the present study, a new image-based machine learning method is used to predict concrete compressive strength, including evaluation of six different models. These include support-vector machine model and various deep convolutional neural network models, namely AlexNet, GoogleNet, VGG19, ResNet, and Inception-ResNet-V2. In the present investigation, cement mortar samples were prepared using each of the cement:sand ratios of 1:3, 1:4, and 1:5, and using the water:cement ratios of 0.35 and 0.55. Cement concrete was prepared using the cement:sand:coarse aggregate ratios of 1:5:10, 1:3:6, 1:2:4, 1:1.5:3 and 1:1:2, using the water:cement ratio of 0.5 for all samples. The samples were cut, and several images of the cut surfaces were captured at various zoom levels using a digital microscope. All samples were then tested destructively for compressive strength. The images and corresponding compressive strength were then used to train machine learning models to allow them to predict compressive strength based upon the image data. The Inception-ResNet-V2 models exhibited the best predictions of compressive strength among the models tested. Overall, the present findings validated the use of machine learning models as an efficient means of estimating cement mortar and concrete compressive strengths based on digital microscopic images, as an alternative nondestructive/semi-destructive test method that could be applied at relatively less expense.

关键词： support vector machine     deep convolutional neural network     microscope     digital image     curing period    

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How do digital technologies improve supply chain resilience in the COVID-19 pandemic?

《工程管理前沿（英文）》   页码 39-50 doi: 10.1007/s42524-022-0230-4

摘要： Digital technologies (DTs) can assist businesses in coping with supply chain (SC) disruptions caused by unpredictability, such as pandemics. However, the current knowledge of the relationship between DTs and supply chain resilience (SCR) is insufficient. This study draws on information processing theory to develop a serial mediation model to address this deficiency. We analyze a sample set consisting of 264 Chinese manufacturers. The empirical results reveal that digital supply chain platforms (DSCPs), as well as supply chain traceability (SCT) and supply chain agility (SCA), fully mediate the favorable association between DTs and SCR. Specifically, the four significant indirect paths indicated that firms can improve SCR only if they use DTs to directly or indirectly improve SCT and SCA (through DSCPs). Our study contributes to the literature on resilience by examining the possible mechanism of mediation through which DTs influence SCR. The findings also offer essential insights for firms to modify their digital strategies and thrive in a turbulent environment.

关键词： digital technologies     supply chain resilience     information processing theory     COVID-19     China    

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基于图像处理的超高速撞击碎片云的动态建模与损伤估计 Research Article

曾入,宋燕,吕伟臻

《信息与电子工程前沿（英文）》 2022年 第23卷 第4期   页码 555-570 doi: 10.1631/FITEE.2100049

摘要： 由于难以从实验中获得高质量碎片云图像，对薄板上超高速撞击产生的碎片云进行轨迹建模和有效损伤估计一直是一项具有挑战性的任务。为提高超高速撞击对典型双层板防护结构损伤的估计精度，本文结合传统数值分析结果，利用图像处理技术，研究了连续阴影图中碎片云的分布特征。本文的目标是从图像处理获取的阴影图中提取碎片云的目标运动参数，并构建轨迹模型用来估计损伤。在超高速撞击实验中，我们从超高速序列激光阴影成像设备中获得8个连续阴影图片帧，从中选择4个具有代表性的帧用于后续特征分析。然后，利用去噪和分割等图像处理技术，从连续图像帧中提取特殊碎片特征。在提取的信息基础上，进行碎片图像匹配，并根据匹配的碎片对碎片云的轨迹进行建模。本文方法获得的结果与传统数值推导结果的对比表明，从图像处理中获取超高速撞击实验数据的方法可以为改进数值模拟方法提供关键信息。最后，基于所构建的模型，提出一种改进的后壁损伤估计方法。估计的损坏与后墙实际损坏情况的对比证明了所提模型的有效性。

关键词： 碎片云；超高速撞击；图像处理；损伤估计    

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基于学习自适应区域选择的自动增强图像 None

Na LI, Jian ZHAN

《信息与电子工程前沿（英文）》 2019年 第20卷 第2期   页码 206-221 doi: 10.1631/FITEE.1700125

摘要： 如今数码相机被广泛用于日常摄影。然而，部分照片缺乏细节，需要增强处理。很多现有图像增强算法基于局部区域，而且同一图像所选区域尺寸通常是固定的。用户需手工选择合适的区域尺寸获取最佳图像增强效果。提出一种基于自适应区域选择的自动增强图像算法。该算法采用明暗两个通道，解决各类图像曝光问题。对网上爬取的大量自然图像统计分析获取阈值，自动选择用于通道提取的区域尺寸。该方法可自动增强模糊或者曝光不足/背光的图像，无需任何用户交互。实验结果表明，该算法对现有基于区域的图像增强算法有显著改进。

关键词： 图像增强；对比度增强；暗通道；明通道；自适应区域处理    

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Quantification of coarse aggregate shape in concrete

Xianglin GU,Yvonne TRAN,Li HONG

《结构与土木工程前沿（英文）》 2014年 第8卷 第3期   页码 308-321 doi: 10.1007/s11709-014-0266-6

摘要： The objective of this study is to choose indices for the characterization of aggregate form and angularity for large scale application. For this purpose, several parameters for aggregate form and angularity featured in previous research are presented. Then, based on these established parameters, 200 coarse quartzite aggregates are analyzed herein by using image processing technology. This paper also analyzes the statistical distributions of parameters for aggregate form and angularity as well as the correlation between form and angularity parameters. It was determined that the parameters for form or angularity of coarse aggregates could be fitted by either normal distribution or log-normal distribution at a 95% confidence level. Some of the form parameters were influenced by changes in angularity characteristics, while aspect ratio and angularity using outline slope, area ratio and radius angularity index, and aspect ratio and angularity index were independent of each other, respectively; and consequently, the independent parameters could be used to quantify the aggregate form and angularity for the purpose to study the influence of aggregate shape on the mechanical behavior of concrete. Furthermore, results from this study’s in-depth investigations showed that the aspect ratio and the angularity index can further understanding of the effects of coarse aggregates form and angularity on concrete mechanical properties, respectively. Finally, coarse aggregates with the same content, type and surfaces texture, but different aspect ratios and angularity indices were used to study the influence of coarse aggregate form and angularity on the behavior of concrete. It was revealed that the splitting tensile strength of concrete increased with increases in the aspect ratio or angularity index of coarse aggregates.

关键词： coarse aggregate     form     angularity     digital image analysis     statistical distribution     splitting tensile strength    

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标题 作者 时间 类型 操作

Digital image correlation-based structural state detection through deep learning

期刊论文

A study on fatigue damage of asphalt mixture under different compaction using 3D-microstructural characteristics

Jing HU, Pengfei LIU, Bernhard STEINAUER

期刊论文

Deformation field and crack analyses of concrete using digital image correlation method

Yijie HUANG, Xujia HE, Qing WANG, Jianzhuang XIAO

期刊论文

a Novel Fuzzy Clustering Method Based on Chaos Immune Evolutionary Algorithm for Edge Detection in ImageProcessing

期刊论文

fracture process in forta and polypropylene fiber-reinforced concrete using experimental analysis and digitalimage correlation

Seyed Hamid KALALI; Hamid ESKANDARI-NADDAF; Seyed Ali EMAMIAN

期刊论文

Deep learning in digital pathology image analysis: a survey

Shujian Deng, Xin Zhang, Wen Yan, Eric I-Chao Chang, Yubo Fan, Maode Lai, Yan Xu

期刊论文

Three-dimensional reconstruction of light microscopy image sections: present and future

null

期刊论文

polypropylene-reinforced ultra-high-performance concrete through numerical analyses and experimental multi-target digitalimage correlation

期刊论文

简述图像被动取证技术

林祥, 李建华, 王士林, 刘伟聪, 程峰, 黄潇洒

期刊论文

Three-scale stochastic homogenization of elastic recycled aggregate concrete based on nano-indentation digital

Chen WANG, Yuching WU, Jianzhuang XIAO

期刊论文

Assessing compressive strengths of mortar and concrete from digital images by machine learning techniques

Amit SHIULY; Debabrata DUTTA; Achintya MONDAL

期刊论文

How do digital technologies improve supply chain resilience in the COVID-19 pandemic?

期刊论文

基于图像处理的超高速撞击碎片云的动态建模与损伤估计

曾入,宋燕,吕伟臻

期刊论文

基于学习自适应区域选择的自动增强图像

Na LI, Jian ZHAN

期刊论文

Quantification of coarse aggregate shape in concrete

Xianglin GU,Yvonne TRAN,Li HONG

期刊论文