An Exploratory Investigation into Image-Data-Driven Deep Learning for Stability Analysis of Geosystems
An Exploratory Investigation into Image-Data-Driven Deep Learning for Stability Analysis of Geosystems
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DOI：
10.1007/s10706-021-01921-w
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发表时间：
2021-08
影响因子：
1.7
通讯作者：
Zhen Liu;Shiyan Hu;Ye Sun;Behnam Azmoon
中科院分区：
文献类型：
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作者：
Zhen Liu;Shiyan Hu;Ye Sun;Behnam Azmoon
本研究探讨了图像数据驱动的深度学习在地理系统稳定性分析中的应用。为此，研究了最近以卷积神经网络（CNN）为代表的计算机视觉的突破，该网络后来被用作开发谷歌AlphaGo的核心技术，用于评估挡土墙的稳定性。采用了著名的狗与猫Kaggle挑战赛中使用的概念，其中使用机器学习算法来分类图像是否包含狗或猫。CNN被用来分析挡土墙的图像，以判断一堵墙是“猫”（安全）还是“狗”（失败）。对于定量分析，挡土墙的2D图像，组织为从500到200，000的大小的数据集，使用随机方法生成，并使用传统的机械方法标记。通过CNN的二元分类，预测挡土墙是否安全的准确率达到97.94%。通过分析20，000个独立和相同分布的额外图像进行测试，证实了结果。对数据集大小和计算能力的进一步研究，对数据和计算资源对深度学习在地理系统稳定性分析中的应用的影响产生了定量的见解。该研究首次证明了利用图像数据进行地质系统稳定性分析的可行性，并为岩土工程以及其他土木工程领域提供了潜在的大数据解决方案。
This study investigates image-data-driven deep learning in the stability analysis of geosystems. For the purpose, the recent breakthrough in computer vision represented by the Convolutional Neural Network (CNN), which was later used as a core technique in developing Google’s AlphaGo, was studied for its capacity in assessing the stability of retaining walls. The concept used in the famous Dogs vs. Cats Kaggle challenge, in which machine learning algorithms are used to classify whether an image contains a dog or a cat, was employed. A CNN was used to analyze images for retaining walls to tell whether a wall is “cat” (safe) or “dog” (failed). For quantitative analysis, 2D images for retaining walls, organized as datasets of sizes from 500 to 200,000, were generated using a stochastic method and labeled using a traditional mechanistic method. An accuracy of 97.94% was achieved for predicting whether the retaining wall is safe via binary classifications with the CNN. Testing via the analysis of 20,000 additional images, which were independent and identically distributed, confirmed the results. Further investigations into the dataset sizes and computational power yielded quantitative insights into the influence of data and computing resources on the application of deep learning in the stability analysis of geosystems. The study, for the first time, proves the feasibility of stability analysis of geosystems with image data and provides a potential big data solution for geotechnical engineering as well as other civil engineering areas.