Prediction of the Stability of the Loaded Rock Based on the Acoustic Emission Characteristics of the Loaded Rock Based on Data Mining
The rock masses that occur in nature are damaged and unstable due to the impact of rock burst, coal and gas outbursts, and other human mining activities, posing a major threat to human life and safety. In the light of the early warning of the danger of the loaded rock mass, this paper adopts acousti...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2021/4249957 |
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| author | Mengyao Li Chang Su Guolong Li |
| author_facet | Mengyao Li Chang Su Guolong Li |
| author_sort | Mengyao Li |
| collection | DOAJ |
| description | The rock masses that occur in nature are damaged and unstable due to the impact of rock burst, coal and gas outbursts, and other human mining activities, posing a major threat to human life and safety. In the light of the early warning of the danger of the loaded rock mass, this paper adopts acoustic emission (AE) device to analyze the AE signal characteristics and damage laws of the loaded rock under different stress levels. Then, based on the AE signal characteristics of the loaded rock, data mining technology is used to construct a model to predict the failure and instability of the loaded rock mass and, finally, verify the reliability of the prediction model based on data mining. The results show that the AE signal characteristics of red sandstone under uniaxial load are related to the magnitude of the bearing stress. Before the plastic deformation stage, the AE energy and the cumulative count per second are both small. After the loaded rock enters the plastic deformation stage, the AE energy and the cumulative count per second both increase sharply. After the AE energy is greater than 500 mV∗ms and the cumulative count per second is greater than 150, the loaded rock mass will issue an early warning signal. The research results can provide a reference value for the safe production of the project site and the dangerous early warning of the loaded rock mass. |
| format | Article |
| id | doaj-art-41de8e3fd4dd4d488f22bf0cf8c5de99 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-41de8e3fd4dd4d488f22bf0cf8c5de992025-02-03T01:04:31ZengWileyShock and Vibration1070-96221875-92032021-01-01202110.1155/2021/42499574249957Prediction of the Stability of the Loaded Rock Based on the Acoustic Emission Characteristics of the Loaded Rock Based on Data MiningMengyao Li0Chang Su1Guolong Li2State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, Anhui University of Science and Technology, Huainan 232001, Anhui, ChinaState Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, Anhui University of Science and Technology, Huainan 232001, Anhui, ChinaShandong Energy Zaozhuang Mining Group Co., Ltd., Zaozhuang 277099, Shandong, ChinaThe rock masses that occur in nature are damaged and unstable due to the impact of rock burst, coal and gas outbursts, and other human mining activities, posing a major threat to human life and safety. In the light of the early warning of the danger of the loaded rock mass, this paper adopts acoustic emission (AE) device to analyze the AE signal characteristics and damage laws of the loaded rock under different stress levels. Then, based on the AE signal characteristics of the loaded rock, data mining technology is used to construct a model to predict the failure and instability of the loaded rock mass and, finally, verify the reliability of the prediction model based on data mining. The results show that the AE signal characteristics of red sandstone under uniaxial load are related to the magnitude of the bearing stress. Before the plastic deformation stage, the AE energy and the cumulative count per second are both small. After the loaded rock enters the plastic deformation stage, the AE energy and the cumulative count per second both increase sharply. After the AE energy is greater than 500 mV∗ms and the cumulative count per second is greater than 150, the loaded rock mass will issue an early warning signal. The research results can provide a reference value for the safe production of the project site and the dangerous early warning of the loaded rock mass.http://dx.doi.org/10.1155/2021/4249957 |
| spellingShingle | Mengyao Li Chang Su Guolong Li Prediction of the Stability of the Loaded Rock Based on the Acoustic Emission Characteristics of the Loaded Rock Based on Data Mining Shock and Vibration |
| title | Prediction of the Stability of the Loaded Rock Based on the Acoustic Emission Characteristics of the Loaded Rock Based on Data Mining |
| title_full | Prediction of the Stability of the Loaded Rock Based on the Acoustic Emission Characteristics of the Loaded Rock Based on Data Mining |
| title_fullStr | Prediction of the Stability of the Loaded Rock Based on the Acoustic Emission Characteristics of the Loaded Rock Based on Data Mining |
| title_full_unstemmed | Prediction of the Stability of the Loaded Rock Based on the Acoustic Emission Characteristics of the Loaded Rock Based on Data Mining |
| title_short | Prediction of the Stability of the Loaded Rock Based on the Acoustic Emission Characteristics of the Loaded Rock Based on Data Mining |
| title_sort | prediction of the stability of the loaded rock based on the acoustic emission characteristics of the loaded rock based on data mining |
| url | http://dx.doi.org/10.1155/2021/4249957 |
| work_keys_str_mv | AT mengyaoli predictionofthestabilityoftheloadedrockbasedontheacousticemissioncharacteristicsoftheloadedrockbasedondatamining AT changsu predictionofthestabilityoftheloadedrockbasedontheacousticemissioncharacteristicsoftheloadedrockbasedondatamining AT guolongli predictionofthestabilityoftheloadedrockbasedontheacousticemissioncharacteristicsoftheloadedrockbasedondatamining |