Concrete Defect Localization Based on Multilevel Convolutional Neural Networks

被引:0
|
作者
Wang, Yameng [1 ]
Wang, Lihua [1 ]
Ye, Wenjing [1 ]
Zhang, Fengyi [1 ]
Pan, Yongdong [1 ]
Li, Yan [1 ]
机构
[1] Tongji Univ, Sch Aerosp Engn & Appl Mech, Shanghai 200092, Peoples R China
基金
中国国家自然科学基金;
关键词
convolutional neural networks; concrete structures; array ultrasonic testing; defect localization; NONDESTRUCTIVE TECHNIQUES; ALGORITHM; MODEL;
D O I
10.3390/ma17153685
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
Concrete structures frequently manifest diverse defects throughout their manufacturing and usage processes due to factors such as design, construction, environmental conditions and distress mechanisms. In this paper, a multilevel convolutional neural network (CNN) combined with array ultrasonic testing (AUT) is proposed for identifying the locations of hole defects in concrete structures. By refining the detection area layer by layer, AUT is used to collect ultrasonic signals containing hole defect information, and the original echo signal is input to CNN for the classification of hole locations. The advantage of the proposed method is that the corresponding defect location information can be obtained directly from the input ultrasonic signal without manual discrimination. It effectively addresses the issue of traditional methods being insufficiently accurate when dealing with complex structures or hidden defects. The analysis process is as follows. First, COMSOL-Multiphysics finite element software is utilized to simulate the AUT detection process and generate a large amount of ultrasonic echo data. Next, the extracted signal data are trained and learned using the proposed multilevel CNN approach to achieve progressive localization of internal structural defects. Afterwards, a comparative analysis is conducted between the proposed multilevel CNN method and traditional CNN approaches. The results show that the defect localization accuracy of the proposed multilevel CNN approach improved from 85.38% to 95.27% compared to traditional CNN methods. Furthermore, the computation time required for this process is reduced, indicating that the method not only achieves higher recognition precision but also operates with greater efficiency. Finally, a simple experimental verification is conducted; the results show that this method has strong robustness in recognizing noisy ultrasonic signals, provides effective solutions, and can be used as a reference for future defect detection.
引用
收藏
页数:22
相关论文
共 50 条
  • [1] Occlusion Localization Based On Convolutional Neural Networks
    Hou, Ya-Li
    Peng, Jinzhang
    Hao, Xiaoli
    Shen, Yan
    Qian, Manyi
    2017 IEEE INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING, COMMUNICATIONS AND COMPUTING (ICSPCC), 2017,
  • [2] Convolutional Neural Networks based Denoising for Indoor Localization
    Njima, Wafa
    Chafii, Marwa
    Nimr, Ahmad
    Fettweis, Gerhard
    2021 IEEE 93RD VEHICULAR TECHNOLOGY CONFERENCE (VTC2021-SPRING), 2021,
  • [3] Automatic Localization of Vertebrae Based on Convolutional Neural Networks
    Shen, Wei
    Yang, Feng
    Mu, Wei
    Yang, Caiyun
    Yang, Xin
    Tian, Jie
    MEDICAL IMAGING 2015: IMAGE PROCESSING, 2015, 9413
  • [4] A novel approach for industrial concrete defect identification based on image processing and deep convolutional neural networks
    Gaur, Ashish
    Kishore, Kamal
    Jain, Rajul
    Pandey, Aaysha
    Singh, Prakash
    Wagri, Naresh Kumar
    Roy-Chowdhury, Abhirup B.
    CASE STUDIES IN CONSTRUCTION MATERIALS, 2023, 19
  • [5] Wood Defect Classification Based on Lightweight Convolutional Neural Networks
    Xie, Yonghua
    Ling, Jiaxin
    BIORESOURCES, 2023, 18 (04) : 7663 - 7680
  • [6] Concrete Bridge Defects Identification and Localization Based on Classification Deep Convolutional Neural Networks and Transfer Learning
    Zoubir, Hajar
    Rguig, Mustapha
    El Aroussi, Mohamed
    Chehri, Abdellah
    Saadane, Rachid
    Jeon, Gwanggil
    REMOTE SENSING, 2022, 14 (19)
  • [7] CRACK DETECTION OF CONCRETE SURFACE BASED ON CONVOLUTIONAL NEURAL NETWORKS
    Yao, Gang
    Wei, Fu-Jia
    Qian, Ji-Ye
    Wu, Zhao-Guo
    PROCEEDINGS OF 2018 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS (ICMLC), VOL 1, 2018, : 246 - 250
  • [8] PILC: Passive Indoor Localization Based on Convolutional Neural Networks
    Cai, Chenwei
    Deng, Li
    Zheng, Mingyang
    Li, Shufang
    PROCEEDINGS OF 5TH IEEE CONFERENCE ON UBIQUITOUS POSITIONING, INDOOR NAVIGATION AND LOCATION-BASED SERVICES (UPINLBS), 2018, : 509 - 514
  • [9] Localization-based Visual Tracking with Convolutional Neural Networks
    Moridi, Abolfazl
    Azimifar, Zohreh
    2016 24TH IRANIAN CONFERENCE ON ELECTRICAL ENGINEERING (ICEE), 2016, : 661 - 664
  • [10] Disease and Defect Detection System for Raspberries Based on Convolutional Neural Networks
    Naranjo-Torres, Jose
    Mora, Marco
    Fredes, Claudio
    Valenzuela, Andres
    APPLIED SCIENCES-BASEL, 2021, 11 (24):