基于深度学习的中国古代木结构中木质构件的裂缝识别与量化

IF 4.6 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
Lipeng Zhang, Qifang Xie, Hanlong Wang, Jiang Han, Yajie Wu
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引用次数: 0

摘要

中国古代木结构的大部分构件都存在裂缝,并导致了严重的力学性能退化问题,威胁着整体结构的安全,因此木构件的裂缝检测势在必行。随着文化建筑智能保护技术的飞速发展,建立一套科学的木构件裂缝识别和量化方法来替代传统的人工检测技术显得尤为重要。深度学习正是这样一种先进的技术。本研究首先从应县木塔上采集了木构件裂缝图像,并分析了裂缝特征。利用总共 501 张开裂木构件图像建立了裂缝分割数据集,其中包括 450 张训练数据集和 51 张验证数据集。根据深度学习和全卷积神经网络(FCNN)的数学原理,构建了基于编码和解码方法的深度全卷积神经网络(d-FCNN)模型。分析了像素准确率(PA)、平均像素准确率(mPA)、平均交集大于联合(mIoU)和F1-score四项模型指标,对模型进行了训练,并确定了最佳模型参数,包括学习率、批量大小和epoch。结果表明,最佳初始学习率为 10-4,批量大小为 6,epoch 为 100,平均准确率达到 78.8%。此外,基于像素累积原理,提出了裂缝长度和最大宽度的定量计算方法。制备了两根开裂的木柱,并进行了裂纹图像识别和量化实验,以验证所构建的 d-FCNN 模型和所提出的裂纹量化方法的正确性。结果表明,该模型适用于裂纹木质构件的裂纹智能检测、识别和量化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Deep-Learning-Based Crack Identification and Quantification for Wooden Components in Ancient Chinese Timber Structures

Deep-Learning-Based Crack Identification and Quantification for Wooden Components in Ancient Chinese Timber Structures

Cracks exist in the majority of components of ancient Chinese timber structures and have led to serious mechanical property degradation problems, threatening the safety of the whole structures and making the cracks’ detection for wooden components a necessity. With the rapid development of intelligent protection technology of cultural buildings, it is important to establish a scientific identification and quantification method for cracks in wooden components to replace traditional manual detection techniques. Deep learning is precisely such an advanced technology. In this study, images of cracked wooden components were first collected from the Yingxian wooden pagoda and the crack characteristics were analyzed. A dataset for crack segmentation was established using a total of 501 images of cracked wooden components, including a training dataset of 450 images and a validation dataset of 51 images. Based on the mathematical principles of deep learning and the fully convolutional neural networks (FCNN), a deep fully convolutional neural network (d-FCNN) model was constructed based on encoding and decoding methodology. Four model indicators, pixel accuracy (PA), average pixel accuracy (mPA), mean intersection over union (mIoU), and F1-score were analyzed to train the model and determine the optimal model parameters, including learning rate, batch size, and epoch. It concluded that the optimal initial learning rate takes the value of 10−4, batch size of 6, and epoch of 100, achieving the average accuracy of 78.8%. Further, based on the pixel’s accumulation principle, a quantitative calculation method for crack length and maximum width was proposed. Two cracked wooden columns were prepared, and crack image identification and quantification experiments were conducted to verify the correctness of the constructed d-FCNN model and the proposed crack quantification method. The results show that the model is suitable for crack intelligence detection, identification, and quantification of cracked wooden components.

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来源期刊
Structural Control & Health Monitoring
Structural Control & Health Monitoring 工程技术-工程:土木
CiteScore
9.50
自引率
13.00%
发文量
234
审稿时长
8 months
期刊介绍: The Journal Structural Control and Health Monitoring encompasses all theoretical and technological aspects of structural control, structural health monitoring theory and smart materials and structures. The journal focuses on aerospace, civil, infrastructure and mechanical engineering applications. Original contributions based on analytical, computational and experimental methods are solicited in three main areas: monitoring, control, and smart materials and structures, covering subjects such as system identification, health monitoring, health diagnostics, multi-functional materials, signal processing, sensor technology, passive, active and semi active control schemes and implementations, shape memory alloys, piezoelectrics and mechatronics. Also of interest are actuator design, dynamic systems, dynamic stability, artificial intelligence tools, data acquisition, wireless communications, measurements, MEMS/NEMS sensors for local damage detection, optical fibre sensors for health monitoring, remote control of monitoring systems, sensor-logger combinations for mobile applications, corrosion sensors, scour indicators and experimental techniques.
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