检索结果-内蒙古大学图书馆

crack detection and Analysis of concrete Structures Based on Neural Network and Clustering

SENSORS 2024年第6期24卷 1725页

作者： Choi, Young Park, Hee Won Mi, Yirong Song, Sujeen Earth Turbine 36 Dongdeok Ro 40 Gil Daegu 41905 South Korea Kyungpook Natl Univ Sch Architecture Civil Environm & Energy Engn 80 Daehak Ro Daegu 41566 South Korea

concrete is extensively used in the construction of infrastructure such as houses and bridges. However, the appearance of cracks in concrete structures over time can diminish their sealing and load-bearing capability, potentially leading to structural failures and disasters. The timely detection of cracks allows for repairs without the need to replace the entire structure, resulting in cost savings. Currently, manual inspection remains the predominant method for identifying concrete cracks. However, in today's increasingly complex construction environments, subjective errors may arise due to human vision and perception. The purpose of this work is to investigate and design an autonomous convolutional neural network-based concrete detection system that can identify cracks automatically and use that information to calculate the crack proportion. The experiment's findings show that the trained model can classify concrete cracks with an accuracy of 99.9%. Moreover, the clustering technique applied to crack images enables the clear identification of the percentage of cracks, which facilitates the development of concrete damage level detection over time.

关键词： deep learning concrete crack detection Resnet50 k-means clustering

来源：评论

学校读者我要写书评

暂无评论

Threshold-Based BRISQUE-Assisted Deep Learning for Enhancing crack detection in concrete Structures

引用

JOURNAL OF IMAGING 2023年第10期9卷 218-218页

作者： Pennada, Sanjeetha Perry, Marcus McAlorum, Jack Dow, Hamish Dobie, Gordon Univ Strathclyde Dept Civil & Environm Engn 75 Montrose St Glasgow G1 1XJ Scotland Univ Strathclyde Dept Elect & Elect Engn 204 George St Glasgow G1 1XW Scotland

Automated visual inspection has made significant advancements in the detection of cracks on the surfaces of concrete structures. However, low-quality images significantly affect the classification performance of convolutional neural networks (CNNs). Therefore, it is essential to evaluate the suitability of image datasets used in deep learning models, like Visual Geometry Group 16 (VGG16), for accurate crack detection. This study explores the sensitivity of the BRISQUE method to different types of image degradations, such as Gaussian noise and Gaussian blur. By evaluating the performance of the VGG16 model on these degraded datasets with varying levels of noise and blur, a correlation is established between image degradation and BRISQUE scores. The results demonstrate that images with lower BRISQUE scores achieve higher accuracy, F1 score, and Matthew's correlation coefficient (MCC) in crack classification. The study proposes the implementation of a BRISQUE score threshold (BT) to optimise training and testing times, leading to reduced computational costs. These findings have significant implications for enhancing accuracy and reliability in automated visual inspection systems for crack detection and structural health monitoring (SHM).

关键词： image quality assessment deep learning VGG16 image processing structural health monitoring neural networks binary classification data cleaning BRISQUE concrete crack detection

来源：评论

学校读者我要写书评

暂无评论

crack detection Based on Support Vector Data Description 29

Crack Detection Based on Support Vector Data Description

引用

第29届中国控制与决策会议

作者： LIN Weiguo LIN Yaru Wang Fang College of Information Science and Technology Beijing University of Chemical Technology

ISBN: (纸本)9781509046584

It is difficult to detect concrete cracks because of the existence of background *** solve this problem,some methods for crack detection on concrete surfaces are *** to shape features,a new concrete crack detection method is *** of all,iteration method is applied to get the optimal threshold for image segmentation after grayscale ***,binary images are processed by morphological closing operation and *** including eccentricity,circularity and packing density are selected as input training vectors for Support Vector Data Description(SVDD).The experimental results show that crack detection method based on SVDD can accurately distinguish cracks from other kinds of defects(non-crack) and reduce the false negative detections.

关键词： concrete crack detection Optimal threshold Shape feature Support Vector Data Description

来源：评论

学校读者我要写书评

暂无评论

Real-time detection and geometric analysis algorithm for concrete cracks based on the improved U-net model

引用

JOURNAL OF REAL-TIME IMAGE PROCESSING 2024年第4期21卷 139-139页

作者： Zhang, Qian Zhang, Fan Liu, Hongbo Wang, Longxuan Chen, Zhihua Guo, Liulu China Construct Sixth Engn Bur Corp Tianjin 300072 Peoples R China Tianjin Univ State Key Lab Hydraul Engn Simulat & Safety Tianjin 300072 Peoples R China Hebei Univ Engn Dept Civil Engn Handan 056000 Peoples R China

Aiming at complex operation problems, low precision and poor robustness of traditional concrete crack detection methods, a real-time concrete crack detection and geometric analysis algorithm based on the improved U-net model is proposed. First, the efficient channel attention (ECA) module is embedded in the U-net model to reduce the loss of target information. The DenseNet network is used instead of the VGG16 network in the U-net basic model architecture, making transmitting features and gradients more effective. Then, based on the improved U-net model, the concrete crack detection experiment is performed. The experimental results indicate that the improved U-net model has 91.56% pixel accuracy (PA), 80.12% mean intersection over union (mIoU), 84.89% recall and 88.10% F1_score. The mIoU, PA, recall and F1_score of the improved U-net model increased by 17.39%, 7.82%, 2.62% and 5.10%, respectively, compared with the original model. Next, the real-time detection experiment of concrete cracks is performed based on the improved U-net model. The FPS of the improved model is the same as that of the original model and reaches 42. Finally, the geometric analysis of concrete cracks is performed based on the detection results of the improved U-net model. The area, density, length and average width information of concrete cracks are effectively extracted. The research results indicate that the detection effect of this study's model on concrete cracks is considerably improved and that the model has good robustness. The model proposed in this study can achieve intelligent real-time and accurate identification of concrete cracks, which has broad application prospects.

关键词： concrete crack detection Improved U-net model Real-time detection Geometric analysis Deep learning Computer vision

来源：评论

学校读者我要写书评

暂无评论

Deep learning-based autonomous concrete crack evaluation through hybrid image scanning

引用

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2019年第5-6期18卷 1722-1737页

作者： Jang, Keunyoung Kim, Namgyu An, Yun-Kyu Sejong Univ Dept Architectural Engn 209 Neungdong Ro Seoul 143747 South Korea Sejong Univ Dept Civil & Environm Engn Seoul South Korea

This article proposes a deep learning-based autonomous concrete crack detection technique using hybrid images. The hybrid images combining vision and infrared thermography images are able to improve crack detectability while minimizing false alarms. In particular, large-scale concrete-made infrastructures such as bridge and dam can be effectively inspected by spatially scanning the unmanned vehicle-mounted hybrid imaging system including a vision camera, an infrared camera, and a continuous-wave line laser. However, the expert-dependent decision-making for crack identification which has been widely used in industrial fields is often cumbersome, time-consuming, and unreliable. As a target concrete structure gets larger, automated decision-making becomes more desirable from the practical point of view. The proposed technique is able to achieve automated crack identification and visualization by transfer learning of a well-trained deep convolutional neural network, that is, GoogLeNet, while retaining the advantages of the hybrid images. The proposed technique is experimentally validated using a lab-scale concrete specimen with cracks of various sizes. The test results reveal that macro- and microcracks are automatically visualized while minimizing false alarms.

关键词： concrete crack detection deep convolutional neural network hybrid image scanning vision image infrared thermography structural health monitoring

来源：评论

学校读者我要写书评

暂无评论

Review of Non-Destructive Civil Infrastructure Evaluation for Bridges: State-of-the-Art Robotic Platforms, Sensors and Algorithms

引用

SENSORS 2020年第14期20卷 3954.-3954页

作者： Ahmed, Habib La, Hung Manh Gucunski, Nenad Univ Nevada Dept Comp Sci & Engn Adv Robot & Automat Lab Reno NV 89512 USA Rutgers State Univ Dept Civil & Environm Engn Piscataway NJ 08854 USA

The non-destructive evaluation (NDE) of civil infrastructure has been an active area of research in recent decades. The traditional inspection of civil infrastructure mostly relies on visual inspection using human inspectors. To facilitate this process, different sensors for data collection and techniques for data analyses have been used to effectively carry out this task in an automated fashion. This review-based study will examine some of the recent developments in the field of autonomous robotic platforms for NDE and the structural health monitoring (SHM) of bridges. Some of the salient features of this review-based study will be discussed in the light of the existing surveys and reviews that have been published in the recent past, which will enable the clarification regarding the novelty of the present review-based study. The review methodology will be discussed in sufficient depth, which will provide insights regarding some of the primary aspects of the review methodology followed by this review-based study. In order to provide an in-depth examination of the state-of-the-art, the current research will examine the three major research streams. The first stream relates to technological robotic platforms developed for NDE of bridges. The second stream of literature examines myriad sensors used for the development of robotic platforms for the NDE of bridges. The third stream of literature highlights different algorithms for the surface- and sub-surface-level analysis of bridges that have been developed by studies in the past. A number of challenges towards the development of robotic platforms have also been discussed.

关键词： non-destructive evaluation (NDE) structural health monitoring (SHM) electric resistivity (ER) sensors ground-penetrating radar (GPR) infrared (IR) thermography impact echo (IE) NDE sensor fusion convolutional neural network (CNNs) concrete crack detection rebar detection and localization

来源：评论

学校读者我要写书评

暂无评论

Classification and detection of Various Structural cracks Using Deep Learning Approach 10th

Classification and Detection of Various Structural Cracks Us...

引用

10th European Workshop on Structural Health Monitoring (EWSHM)

作者： Vundekode, Narasimha Reddy Kalapatapu, Prafulla Pasupuleti, Venkata Dilip Kumar Mahindra Univ Ecole Cent Coll Engn Hyderabad India

ISBN: (纸本)9783031072581;9783031072574

concrete has evolved to be the most used construction material all over the world from minor to major structures. The accelerated deterioration of the structure can be reduced if the cracks are detected at their early stage, and it will be more effective if the crack type is also known. Development of crack and its propagation if unnoticed overtime can lead to minor to major damage. The proposed study targets to revolutionize the visual inspection process using Deep Learning (DL) algorithms. The most important part of training a DL algorithm is the data, hence the major challenge for the study is collection of various images consisting different types of cracks from the visual inspections carried manually during real-time projects. Overall, 3500 images were collected and out of which considering the computational complexity the proposed model was developed based on a 500-image sub dataset. The pre-trained DL model has demonstrated the desirable accuracies while no pre-trained model has dropped the accuracy. In practice, the paper also focuses on the current achievements and limitations of the existing methods and as well as opens lot of avenues for the upcoming researchers in the cross disciplinary fields of Structural and Computer Science.

关键词： concrete crack detection crack type Deep learning algorithm Computer vision Structural health monitoring

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

INTERNATIONAL CONFERENCE ON UNMANNED AIRCRAFT SYSTEMS (ICUAS)

作者： Dorafshan, Sattar Coopmans, Calvin Thomas, Robert J. Maguire, Marc Utah State Univ Dept Civil & Environm Engn Logan UT 84322 USA Utah State Univ Utah Water Res Lab AggieAir Logan UT 84322 USA

ISBN: (纸本)9781538613542

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

来源：评论

学校读者我要写书评

暂无评论

Real-Time Surface Damage Analysis of concrete Structures using Python-based Image Analysis 17

Real-Time Surface Damage Analysis of Concrete Structures usi...

引用

Conference on Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XVII

作者： Jha, Shivam Kaur, Amardeep Southern Illinois Univ Edwardsville Edwardsville IL 62026 USA

ISBN: (纸本)9781510660816;9781510660823

Aging infrastructure worldwide has led to an interest in using innovative solutions for non-destructive damage assessment. The traditional in-person assessment of large-scale infrastructure like dams and bridges etc. can be very time and resource-consuming. With advances in technology, image processing and machine learning have shown promise in providing alternate ways of damage assessment in large-scale and difficult-to-access infrastructure. However, this approach has mostly been applied passively and not in real-time. The work presented here describes a supervised machine learning-based approach to damage analysis of concrete structures. Python programming language is used to write and train algorithms to provide a real-time damage analysis. In addition to crack detection, the dimensional analysis provides additional information regarding an existing or developing crack. With this type of real-time information, timely action can be taken regarding decisions like performing repairs or decommissioning a structure for public safety.

关键词： Image Analysis concrete crack detection Structural Health Monitoring Machine Learning Python programming

来源：评论

学校读者我要写书评

暂无评论

crack identification for reinforced concrete using PZT based smart rebar active sensing diagnostic network

Crack identification for reinforced concrete using PZT based...

引用

Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, and Civil Infrastructure 2016

作者： Song, N. N. Wu, F. Shanghai Jiao Tong Univ Sch Naval Architecture Ocean & Civil Engn Dept Civil Engn Shanghai Peoples R China

ISBN: (纸本)9781510600454

An active sensing diagnostic system using PZT based smart rebar for SHM of RC structure has been currently under investigation. Previous test results showed that the system could detect the de-bond of concrete from reinforcement, and the diagnostic signals were increased exponentially with the de-bonding size. Previous study also showed that the smart rebar could function well like regular reinforcement to undertake tension stresses. In this study, a smart rebar network has been used to detect the crack damage of concrete based on guided waves. Experimental test has been carried out for the study. In the test, concrete beams with 2 reinforcements have been built. 8 sets of PZT elements were mounted onto the reinforcement bars in an optimized way to form an active sensing diagnostic system. A 90 kHz 5-cycle Hanning-windowed tone burst was used as input. Multiple cracks have been generated on the concrete structures. Through the guided bulk waves propagating in the structures from actuators and sensors mounted from different bars, crack damage could be detected clearly. Cases for both single and multiple cracks were tested. Different crack depths from the surface and different crack numbers have been studied. Test result shows that the amplitude of sensor output signals is deceased linearly with a propagating crack, and is decreased exponentially with increased crack numbers. From the study, the active sensing diagnostic system using PZT based smart rebar network shows a promising way to provide concrete crack damage information through the "talk" among sensors.

关键词： PZT smart rebar net work Guided wave concrete crack detection experimental test

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：