基于深度卷积神经网络和迁移学习的农村房屋洪涝灾害后受损等级分类

吴禄源; 仝敬博; 王自法; 马丹; 张建伟; 廖吉安

doi:10.3799/dqkx.2022.502

Volume 48 Issue 5

May 2023

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Article Navigation > Earth Science > 2023 > 48(5): 1742-1754

Wu Luyuan, Tong Jingbo, Wang Zifa, Ma Dan, Zhang Jianwei, Liao Ji’an, 2023. Classification of Damaged Grade on Rural Houses after Flood Disaster Based on Deep Convolutional Neural Network and Transfer Learning. Earth Science, 48(5): 1742-1754. doi: 10.3799/dqkx.2022.502

Citation:

Wu Luyuan, Tong Jingbo, Wang Zifa, Ma Dan, Zhang Jianwei, Liao Ji’an, 2023. Classification of Damaged Grade on Rural Houses after Flood Disaster Based on Deep Convolutional Neural Network and Transfer Learning. Earth Science, 48(5): 1742-1754. doi: 10.3799/dqkx.2022.502

Citation:

Wu Luyuan, Tong Jingbo, Wang Zifa, Ma Dan, Zhang Jianwei, Liao Ji’an, 2023. Classification of Damaged Grade on Rural Houses after Flood Disaster Based on Deep Convolutional Neural Network and Transfer Learning. Earth Science, 48(5): 1742-1754. doi: 10.3799/dqkx.2022.502

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Classification of Damaged Grade on Rural Houses after Flood Disaster Based on Deep Convolutional Neural Network and Transfer Learning

doi: 10.3799/dqkx.2022.502

Wu Luyuan^{1
,
,},
Tong Jingbo¹,
Wang Zifa^{2, 3
,
,},
Ma Dan⁴,
Zhang Jianwei¹,
Liao Ji’an²

1.
School of Civil Engineering and Architecture, Henan University, Kaifeng 475004, China
2.
Institute of Engineering Mechanics, China Earthquake Administration, Harbin 150080, China
3.
Institute of Disaster Prevention and Mitigation of China Earthquake Engineering (Guangdong), Shaoguan 512026, China
4.
State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, Xuzhou 221116, China

Received Date: 2022-09-28
Available Online: 2023-06-06
Publish Date: 2023-05-25

Abstract

Abstract

Flood disasters can cause great damage to buildings, and identification of post-disaster house damage grade is very important to ensure people's safety. However, the traditional method of artificial identification costs a lot of manpower, financial resources, and time. Based on the data of rural housing damage caused by the "7.20" heavy rainstorm in Zhengzhou, Henan Province, the deep Convolutional neural network (CNN) theory was used to obtain the intelligent classification model of post-disaster house damage grade. Four classic deep CNN architectures, AlexNet, VGGNet, GoogleNet, and ResNet, were used to train, validate and test the data sets, and four intelligent classification models of post-disaster house damage grade were obtained. The weights of the pre-training model were fine-tuned to improve the generalization of the model, then the ResNet-50 with the transfer learning was selected as the main model of the classification. Finally, the influence of hyperparameters in CNN architecture was analyzed. The results show that when the learning rate of the ResNet network was 0.000 5, the epoch was 50, and the batch_size was 16, the network training result was optimal, and the prediction accuracy of the test set reached 95.5%; the visual analysis of the characteristics of the housing risk level clarified the mechanism and accuracy of the model classification. Experimental results show that the ensemble model had a high accuracy rate, which provided an idea and explored an approach for classification of damaged grade rural houses after flood disaster.
- flood disaster,
- convolutional neural network,
- transfer learning,
- house damage grade,
- Henan Zhengzhou "7.20" heavy rainstorm,
- engineering geology

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References(56)

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Classification of Damaged Grade on Rural Houses after Flood Disaster Based on Deep Convolutional Neural Network and Transfer Learning

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