基于卷积神经网络的土体含水率智能识别

庞元恩; 王智诚; 李旭; 杜赛朝

doi:10.3799/dqkx.2023.043

Volume 49 Issue 5

May 2024

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Article Navigation > Earth Science > 2024 > 49(5): 1746-1758

Pang Yuanen, Wang Zhicheng, Li Xu, Du Saizhao, 2024. Moisture Content Recognition Model of Unsaturated Soil Based on Convolutional Neural Networks. Earth Science, 49(5): 1746-1758. doi: 10.3799/dqkx.2023.043

Citation:

Pang Yuanen, Wang Zhicheng, Li Xu, Du Saizhao, 2024. Moisture Content Recognition Model of Unsaturated Soil Based on Convolutional Neural Networks. Earth Science, 49(5): 1746-1758. doi: 10.3799/dqkx.2023.043

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Moisture Content Recognition Model of Unsaturated Soil Based on Convolutional Neural Networks

doi: 10.3799/dqkx.2023.043

School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China

Received Date: 2023-02-10
Available Online: 2024-06-04
Publish Date: 2024-05-25

Abstract

Abstract

The moisture content of the soil is the main factor affecting the quality of fine-grained soil. Rapid recognition of soil surface moisture content is an urgent need for developing intelligent monitoring and construction technology in agricultural and geotechnical engineering. In order to overcome the limitation that traditional water content measurement or monitoring methods cannot meet the real-time nondestructive monitoring of soil surface moisture content, an intelligent moisture content recognition algorithm based on the image is developed. Firstly, we collected surface photos of 4 different types of soils under different moisture contents in the laboratory and obtained a high-quality sample library of more than 1 400 pictures, which laid a data foundation for machine learning model construction. Then the classical convolutional neural network is used to learn the image dataset of soil moisture content, and the intelligent recognition model of soil moisture content is established. The model comparison results show that the model based on ResNet34 architecture has the best moisture content recognition effect, and the average error of moisture content prediction on the test set is about 2%. This model basically meets the requirement of real-time nondestructive monitoring of soil surface moisture content and can provide an essential means for the development of intelligent monitoring and construction technology in agricultural and geotechnical engineering.
- soil moisture content,
- deep learning,
- convolutional neural network (CNN),
- intelligent monitoring,
- intelligent construction,
- engineering geology

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References(54)

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Moisture Content Recognition Model of Unsaturated Soil Based on Convolutional Neural Networks

doi: 10.3799/dqkx.2023.043

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