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6th International Conference on Fuzzy Systems and Data Mining (FSDM)

作者： Hou, Huan Lin, Weiguo Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China

ISBN: (纸本)9781643681351;9781643681344

cracks are an important sign of distress of concrete bridges and may reduce their service life and safety. For the case where there are stains, peeling, scratches, and uneven illumination on the surface of concrete bridges, and where it is difficult to accurately detect complete cracks, this paper proposes a new method to connect the breaks in cracks by adaptive morphological dilation based on crack direction. Most of the existing crack image detection methods attempt to achieve high detection accuracy by increasing the algorithm complexity but sacrifice real-time detection efficiency. A multiple filtering method based on a few adaptive feature thresholds is proposed to filter non-cracks and obtain a clear crack image by analyzing the morphological characteristic differences between real cracks and noise and pseudo-cracks The experimental results show that the proposed method can effectively improve the integrity of cracks, remove different noise and pseudo-cracks, without modeling, and has a higher detection accuracy and speed, which is suitable for practical engineering applications.

关键词： concrete crack detection infrared image morphology multiple feature thresholds adaptive filtering

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Optical crack following on tunnel surfaces

Optical crack following on tunnel surfaces

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Conference on Two- and Three-Dimensional Methods for Inspection and Metrology IV

作者： Paar, Gerhard Caballo-Perucha, Maria d. P. Kontrus, Heiner Sidla, Oliver Joanneum Res Inst Digital Image Proc Wastiangasse 6 A-8010 Graz Austria Dibit Messtech GmbH A-6020 Innsbruck Austria

ISBN: (纸本)9780819464804

One of the most important monitoring tasks of tunnel inspection is the observation of cracks. This paper describes an approach for crack following using mid-resolution (2-5mm per pixel) images of the tunnel surface. A mosaic on the basis of the tunnel design surface is built from images taken with a mobile platform. On this image representing the unwrapped tunnel surface texture the starting points of each crack are found semiautomatically using a modified Hough transform. crack following takes place on the basis of local line fitting and exhaustive search in both directions of the crack, taking into account several restrictions, rules and optimization criteria to find the correct crack trajectory. A practical implementation polygonizes the extracted cracks and feeds them into a tunnel inspection data base. The method is applicable to various types of background texture as expected in the tunnel environment.

关键词： tunnel inspection crack following concrete crack detection line following

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crack depth measurement and key points of accurate identification in concrete structures: a review

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NONDESTRUCTIVE TESTING AND EVALUATION 2024年第8期39卷 2073-2107页

作者： Wang, Yulei Jia, Yu Liang, Jiahui Tang, Lei Guan, Fuhai Li, Po Zhang, Shengxing Tian, Hui Nanjing Hydraul Res Inst Mat & Struct Engn Dept Nanjing Peoples R China Natl Key Lab Water Disaster Prevent Nanjing Peoples R China

The cracks with a width of more than 0.3mm can seriously affect the safety and stability of concrete structures. Presently, there are many techniques for measuring the crack depth, each with its own advantages and disadvantages. However, due to the technical limitations and different applicability of each method, their accuracy in actual crack detection is often insufficient. In this article, to improve the detection accuracy in technological applications, the mature concrete crack depth detection technologies are summarised. The currently popular non-destructive testing technologies are divided into two categories: stress wave detection technology and electromagnetic wave detection technology. The specific detection methods and development status of the two technologies are discussed. Further, based on the detection principles, the advantages and disadvantages of various detection methods are systematically compared. The application conditions of each detection method are explored, and their feasibility for different forms of cracks are comprehensively analysed. A set of optimal methods for crack depth detection is proposed. Besides, the key points for the accurate measurement of concrete crack depth are discussed.

关键词： concrete crack detection stress wave detection technology electromagnetic wave detection technology accurate identification points

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Automatic detection of cracks on concrete Surfaces in the Presence of Shadows

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SENSORS 2022年第10期22卷 3662-3662页

作者： Palevicius, Paulius Pal, Mayur Landauskas, Mantas Orinaite, Ugne Timofejeva, Inga Ragulskis, Minvydas Kaunas Univ Technol Dept Math Modelling Ctr Nonlinear Syst LT-51368 Kaunas Lithuania

Deep learning-based methods, especially convolutional neural networks, have been developed to automatically process the images of concrete surfaces for crack identification tasks. Although deep learning-based methods claim very high accuracy, they often ignore the complexity of the image collection process. Real-world images are often impacted by complex illumination conditions, shadows, the randomness of crack shapes and sizes, blemishes, and concrete spall. Published literature and available shadow databases are oriented towards images taken in laboratory conditions. In this paper, we explore the complexity of image classification for concrete crack detection in the presence of demanding illumination conditions. Challenges associated with the application of deep learning-based methods for detecting concrete cracks in the presence of shadows are elaborated on in this paper. Novel shadow augmentation techniques are developed to increase the accuracy of automatic detection of concrete cracks.

关键词： concrete crack detection deep learning convolution neural networks image classification image augmentation

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Temperature measurement and damage detection in concrete beams exposed to fire using PPP-BOTDA based fiber optic sensors

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SMART MATERIALS AND STRUCTURES 2017年第10期26卷 105034页

作者： Bao, Yi Hoehler, Matthew S. Smith, Christopher M. Bundy, Matthew Chen, Genda Missouri Univ Sci & Technol Dept Civil Architectural & Environm Engn 1401 N Pine St Rolla MO 65409 USA NIST Natl Fire Res Lab 100 Bur Dr Gaithersburg MD 20899 USA

In this study, Brillouin scattering-based distributed fiber optic sensor is implemented to measure temperature distributions and detect cracks in concrete structures subjected to fire for the first time. A telecommunication-grade optical fiber is characterized as a high temperature sensor with pulse pre-pump Brillouin optical time domain analysis (PPP-BODTA), and implemented to measure spatially-distributed temperatures in reinforced concrete beams in fire. Four beams were tested to failure in a natural gas fueled compartment fire, each instrumented with one fused silica, single-mode optical fiber as a distributed sensor and four thermocouples. Prior to concrete cracking, the distributed temperature was validated at locations of the thermocouples by a relative difference of less than 9%. The cracks in concrete can be identified as sharp peaks in the temperature distribution since the cracks are locally filled with hot air. concrete cracking did not affect the sensitivity of the distributed sensor but concrete spalling broke the optical fiber loop required for PPP-BOTDA measurements.

关键词： concrete crack detection distributed fiber optic sensor PPP-BOTDA structural fire safety temperature distribution

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Intelligent concrete Surface cracks detection using Computer Vision, Pattern Recognition, and Artificial Neural Networks

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Procedia Computer Science 2023年 217卷 52-61页

作者： Majid Mirbod Maryam Shoar

This paper presents an intelligent concrete surface crack detection model, using machine vision, pattern recognition, artificial neural networks, and histogram of oriented gradients to feature extraction. This is an applied research category that so far, has been used in face recognition systems, and scaled conjugate gradient to training purpose multilayer neural networks, to achieve the monitoring of concrete surfaces to increase the protection that these are the innovations of this research. The main purpose of this research is to provide a reliable method for inspecting sensitive structures such as bridges using a continuous intelligent inspection system consisting of a camera and intelligent software. For this purpose, we used the Utah State University image dataset that contains over 56,000 images of cracked and non-cracked concrete bridge decks, walls, and pavements. Moreover, it includes data on cracks as narrow as 0.06 mm and a width of 25 mm with a variety of obstructions, shadows, surface roughness, scaling, edges, holes, and background debris. The ANN proposed in this study can recognize cracking images from non-cracking images. The efficiency of this network is 84.88% and it reduces network error from 0.631 to 0.199 and gradient index from 25.5 to 0.0801. This model will be useful for structural health monitoring and other sensitive surfaces such as aircraft engine shells and train moving rails. Based on the obtained results, the difference between the proposed method and other neural networks such as Convolutional Neural networks is that the former is simpler in coding and needs cheaper hardware.

关键词： concrete crack detection Histogram of oriented gradients Pattern recognition Machine vision Scaled conjugate gradient Multilayer neural network

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Performance Degradation Assessment of concrete Beams Based on Acoustic Emission Burst Features and Mahalanobis-Taguchi System

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SENSORS 2020年第12期20卷 3402.-3402.页

作者： Habib, Md Arafat Rai, Akhand Kim, Jong-Myon Univ Ulsan Sch Comp Engn & Informat Technol 93 Daehak Ro Ulsan 44610 South Korea

Acoustic emission (AE) has been used extensively for structural health monitoring based on the stress waves generated due to evolution of cracks in concrete structures. A major concern while using AE features is that each of them responds differently to the fractures in concrete structures. To tackle this problem, Mahalanobis-Taguchi system (MTS) is utilized, which fuses the AE feature space to provide comprehensive and reliable degradation indicator with a feature selection method to determine useful features. Further, majority of the existing investigations gave little attention to naturally occurring cracks, which are actually more difficult to detect. In this study, a novel degradation indicator (DI) based on AE features and MTS is proposed to indicate the performance degradation in reinforced concrete beams. The experimental results confirm that the MTS can successfully distinguish between healthy and faulty conditions. To alleviate the noise from the DI obtained through MTS, a noise-removal strategy based on Chebyshev inequality is suggested. The results show that the proposed DI based on AE features and MTS is capable of detecting early stage cracks as well as development of damage in concrete beams.

关键词： concrete crack detection acoustic emission features Mahalanobis-Taguchi system

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Initial Proof of Concept on the Use of 3D Models to Identify Structural Deviations of Bridge Components

Initial Proof of Concept on the Use of 3D Models to Identify...

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IEEE SoutheastCon

作者： Luis Daniel Otero Mark Moyou Adrian Peter Dennis Dalli Nick Gagliardo Department of Engineering Systems Florida Institute of Technology Melbourne FL USA

ISBN: (纸本)9781538661345

This research paper presents ongoing research work towards developing an effective remote sensing system for bridge inspections. The paper describes various tests that were conducted to address a research question regarding the applicability of 3D models developed from mobile LiDAR data to help identify signs of bridge settlement. The general solution approach involved test planning, collecting data, aligning LiDAR data into a common coordinate system, developing 3D models, and evaluating the use of the resulting models to identify structural deviations. A mockup bridge structure was developed using PVC material to conduct initial experiments prior to conducting more expensive field tests. Prior to data collection, the LiDAR sensor was placed on a tripod mount at a distance of x feet from the PVC structure, and a portion of the PVC structure was inclined y inches using 0.5 inches thick wooden stakes. The resulting 3D models showed that a 0.5 inch inclination was easily identified with the sensor located 10 feet from the structure, and using only 20 LiDAR scans. Future areas of research are discussed to extend the ongoing work presented in this paper.

关键词： Structural inspections Bridge inspections concrete crack detection Transportation systems engineering

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Deep Learning Neural Networks for sUAS-Assisted Structural Inspections: Feasibility and Application

Deep Learning Neural Networks for sUAS-Assisted Structural I...

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International Conference on Unmanned Aircraft Systems

作者： Sattar Dorafshan Robert J. Thomas Calvin Coopmans Marc Maguire Dept. of Civil and Environmental Engineering Utah State University Logan UT USA AggieAir at Utah Water Research Laboratory Utah State University Logan UT USA

This paper investigates the feasibility of using a Deep Learning Convolutional Neural Network (DLCNN) in inspection of concrete decks and buildings using small Unmanned Aerial Systems (sUAS). The training dataset consists of images of lab-made bridge decks taken with a point-and-shoot high resolution camera. The network is trained on this dataset in two modes: fully trained (94.7% validation accuracy) and transfer learning (97.1% validation accuracy). The testing datasets consist of 1620 sub-images from bridge decks with the same cracks, 2340 sub-images from bridge decks with similar cracks, and 3600 sub-images from a building with different cracks, all taken by sUAS. The sUAS used in the first dataset has a low-resolution camera whereas the sUAS used in the second and third datasets has a camera comparable to the point-and-shoot camera. In this study it has been shown that it is feasible to apply DLCNNs in autonomous civil structural inspections with comparable results to human inspectors when using off-the-shelf sUAS and training datasets collected with point-and-shoot handheld cameras.

关键词： Small Unmanned Aerial Systems Deep Learning Convolutional Neural Networks concrete crack detection Infrastructure Inspection Remote Sensing

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