ORCID Profile
0000-0001-8478-0240
Current Organisation
Ministère des Affaires Étrangères
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Systems engineering | Civil engineering | Fire safety engineering | Functional materials
Publisher: MDPI AG
Date: 14-04-2020
DOI: 10.3390/EN13081928
Abstract: Coal burst occurrences are affected by a range of mining and geological factors. Excessive slipping between the strata layers may release a considerable amount of strain energy, which can be destructive. A competent strata is also more vulnerable to riveting a large amount of strain energy. If the stored energy in the rigid roof reaches a certain level, it will be released suddenly which can create a serious dynamic reaction leading to coal burst incidents. In this paper, a new damage model based on the modified thermomechanical continuum constitutive model in coal mass and the contact layers between the rock and coal mass is proposed. The original continuum constitutive model was initially developed for the cemented granular materials. The application of the modified continuum constitutive model is the key aspect to understand the momentum energy between the coal–rock interactions. The transformed energy between the coal mass and different strata layers will be analytically demonstrated as a function of the rock/joint quality interaction conditions. The failure and post failure in the coal mass and coal–rock joint interaction will be classified by the coal mass crushing, coal–rock interaction damage and fragment reorganisation. The outcomes of this paper will help to forecast the possibility of the coal burst occurrence based on the interaction between the coal mass and the strata layers in a coal mine.
Publisher: Elsevier BV
Date: 06-2018
Publisher: Springer Science and Business Media LLC
Date: 14-03-2019
Publisher: Springer Science and Business Media LLC
Date: 29-08-2023
DOI: 10.1007/S11440-023-02011-2
Abstract: Rock quality designation (RQD), as a well-accepted and appliable rock quality index, is crucial in geotechnical engineering. Current RQD estimation mainly relies on either manual statistics or the image binarisation method, while the former approach surrenders high labour intensity and low efficiency and the latter one is constrained by image acquisition. Considering the above-mentioned limitations in RQD estimation, this study proposed a novel convolutional neural network (CNN) approach to automatically perform core recognition and RQD cataloguing with significant improvement in accuracy and efficiency. Firstly, the proposed neural network automatically identified the prefabricated round markers to distinct drilling rounds. To maximumly strengthen the engineering capability of CNN without losing generality, we considered image inversion, rotation, noise addition, and RGB conversion of 200 core box s les in total. Secondly, replacing the unstable image binarisation method, the advanced YOLO V2 object detection model, a single-stage real-time object detection model, was adopted in this study. We also proposed the modified four-layer downs ling structure as our CNN, and then developed an automatic recognition approach for both cores and the round markers, resulting in a 93.1% accuracy according to the validation set. Thirdly, this study proposed an auto-ranking algorithm to sequence the core s le according to the confidence of core recognition by the CNN and row-scanning results for subsequent RQD cataloguing. In addition, the optimal scan width was proved to be 1.33 times larger than the average core width. Finally, a quick cataloguing platform for drill cores was developed. Compared with manual measurement and visual statistics, intelligent RQD cataloguing is characterised by its unparalleled accuracy and efficiency, which is merited by the low relative error (1.84%) and fast processing time (around 0.2 s). Moreover, the application presented in this paper is applicable to most geotechnical engineering scenarios. This is attributed to its low requirements in image acquisition, high efficiency, precise recognition, and robustness.
Publisher: Springer Science and Business Media LLC
Date: 06-08-2021
Publisher: Elsevier BV
Date: 09-2018
Publisher: Informa UK Limited
Date: 21-02-2017
Publisher: International Hellenic University
Date: 2017
Publisher: Springer Science and Business Media LLC
Date: 29-10-2015
Publisher: Springer Science and Business Media LLC
Date: 27-04-2019
Publisher: Elsevier BV
Date: 2017
Publisher: Elsevier BV
Date: 05-2023
Publisher: MDPI AG
Date: 24-01-2018
DOI: 10.3390/EN11020285
Abstract: In underground mining, it is not currently feasible to forecast a coal burst incident. A coal burst usually includes suddenly abrupt energy release in line with the significant deformed shape in a coal mass as well as coal ejection. The major source of the released energy is the energy stored in the coal. The effect of geological characteristics in the coal on the possible released energy due to material and joint d ing is classified as a current silent issue. Therefore, innovative research is needed to understand the influence of coal’s joint and cleat characters (directions and densities) on the possible energy release and/or dissipation. A simple and novel analytical solution is developed in this paper to calculate the amount of released energy due to varying joint density. A broad validation is conducted by comparing the outcomes of the developed analytical model with the results of a three-dimensional numerical simulation using the commercial discrete element package 3DEC. An appropriate agreement has been observed between the results from the numerical modelling and the suggested closed form solution. The paper derives a novel analytical solution to calculate the amount of released energy in coal with different joint densities.
Publisher: Informa UK Limited
Date: 09-2013
Publisher: Elsevier BV
Date: 03-2019
Publisher: Elsevier BV
Date: 03-2018
Start Date: 04-2024
End Date: 04-2029
Amount: $4,999,700.00
Funder: Australian Research Council
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