基于FCN的岩石混凝土裂隙几何智能识别

doi:10.13722/j.cnki.jrme.2019.0010

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摘要裂隙几何的精准、高效识别是建立岩石混凝土材料非连续力学模型的基础。传统算法，如阈值分割、边缘检测与区域生长法等，基于有限经验，工作量大且识别精度低。人工智能识别迥异于人类经验，可自我循环、可自我正反馈甚至可实现自我学习。人造类材料中混凝土性能最接近于岩石，具有相似的裂纹几何分布特征。针对天然岩石与混凝土裂隙识别，尝试提出基于全卷积神经网络的智能识别算法。首先建立基于混凝土3种应用场景(建筑结构、路面及隧道表面)的裂隙数据集，经过卷积操作、池化操作与反卷积操作实现了误差值的迅速收敛。引入新的卷积核参数，架构新的全卷积网络，基于像素的二分类问题，利用查准率与查全率对识别模型进行评价，全卷积神经网络识别算法评价指标较好。进而结合矢量化算法，实现了裂隙几何长度、宽度及面积的实时统计。最后对边缘检测、大律法、区域生长法及全卷积神经网络算法进行了验证，任意选取5组图像进行识别，新算法提取的裂隙与标签图在定性上最相近，并且在定量上查全率与查准率也极大优于传统算法。全卷积神经网络随着自我学习的深入还可不断提高识别精度，降低误差，未来在岩石、混凝土等工程领域的应用具有巨大生命力。

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	薛东杰1
	2
	3
	4
	唐麒淳1
	王傲5
	张辽1
	周宏伟2
	5

关键词 ：岩石力学, 裂隙识别, 传统算法, 人工智能识别, 全卷积神经网络, 查准率, 查全率

Abstract：Efficient and accurate extraction of crack geometry is fundamental for rock or concrete engineering. Traditional algorithms of crack identification，such as threshold segmentation，edge detection and region growing method，are based on limited human experience with tedious work and low accuracy. Artificial intelligence(AI) recognition is self-circulating dependent on big data，and can be positive feedback or even self-learning. The concrete as one of many artificial materials has the similar properties with rock，especially in geometric distribution. An intelligent recognition algorithm based on full convolutional neural network is proposed to identify cracks on rock and concrete surface. The dataset of crack geometry commonly seen in three scenarios of concrete used in building structure，pavement and tunnel surface is established. Through convolution operation，pooling operation and deconvolution operation，the error is quickly reduced with training times. A new convolution kernel parameter is introduced into a new full convolutional network，and a recognition model is evaluated based on the pixel-based two-classification problem. The results shows the better recognition of the new full convolutional network than traditional algorithms. Combined with the vectorization algorithm，the real-time statistical analysis of crack geometry including length，width and area is realized. Finally，the crack identification determined by artificial intelligence and traditional algorithm is compared by arbitrarily selecting five groups of images. The cracks extracted by the new algorithm are qualitatively similar to the ground truth，and the precision and recall are higher than the traditional algorithms. The full convolutional network can continuously improve the recognition accuracy，reduce the error，and will show great vitality in the application of rock or concrete engineering.

Key words： rock mechanics crack identification traditional algorithm artificial intelligence identification fully convolutional network(FCN) precision recall

引用本文:

薛东杰1，2，3，4，唐麒淳1，王傲5，张辽1，周宏伟2，5. 基于FCN的岩石混凝土裂隙几何智能识别[J]. 岩石力学与工程学报, 2019, 38(S2): 3393-3403.
XUE Dongjie1，2，3，4，TANG Qichun1，WANG Ao5，ZHANG Liao1，ZHOU Hongwei2，5. FCN-based intelligent identification of crack geometry in rock or concrete. , 2019, 38(S2): 3393-3403.

链接本文:

http://rockmech.whrsm.ac.cn/CN/10.13722/j.cnki.jrme.2019.0010 或 http://rockmech.whrsm.ac.cn/CN/Y2019/V38/IS2/3393

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