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| Identifying the water content interval of muck based on the image of belt slag |
| SU Guojun1, GONG Qiuming1*, ZHOU Xiaoxiong2, WU Weifeng3, CHEN Peixin3
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1. Key Laboratory of Urban Security and Disaster Engineering of China Ministry of Education, Beijing University of Technology, Beijing 100124, China;
2. School of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, China;
3. Shanghai Tunnel Engineering Co., Ltd., Shanghai 200032, China |
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Abstract In order to identify the soil water content in real time, the improved muck with three kinds of fine sand with initial water content were prepared by adding foam with different foam injection ratios, the slag experiment was carried out through the belt slag test platform, the muck images on the belt were obtained, the muck samples were collected accordingly to determine the water content, the water content interval was marked at 1% intervals, and the data set of muck images and water content intervals was established. Through image preprocessing, the texture features of the main image and the edge image of the muck were extracted by using the method of simplified local intensity order pattern combined with completed local binary pattern, and the support vector machine model of particle swarm optimization was selected as the base model, and the integrated learning model for the recognition of water content of the muck was further constructed, which improved the recognition accuracy, and the recognition error of the water content was ±1%.
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Published: 20 September 2024
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