Abstract
Accurately predicting the extent of the plastic zone around the underground powerhouse cavern is an important basis for the excavation and support design of underground space, which is essential to ensure the stability and construction safety of the cavern. Considering the difficulty of measuring plastic zones surrounding underground powerhouse caverns in situ and the limitations of existing methods, an attempt is made to apply machine learning to solve it. Therefore, three models were developed to predict plastic zones by optimizing the random forest (RF) with the Yin-Yang-Pair optimization, black widow optimization algorithm, and slime mould algorithm. The models utilized metaheuristics to optimize two hyperparameters in the RF model. Set up a database of 1920 samples, including 12 input parameters: unit weight of rock (γ), Poisson ratio (ν), rock mass modulus (E), coefficient of lateral stress (K), rock mass rating (RMR), tensile strength (σt), compression strength (σc), cohesion (C), friction angle (φ), overburden depth (H), pillar width (B), and crown level difference (Z). The 5 output variables were the plastic zones at different locations in the cavern (the roof midpoint, floor midpoint, left sidewall midpoint, right sidewall midpoint, and key point). The prediction ability of the proposed models was evaluated by the root mean square error, mean absolute error, coefficient of determination, and variance accounted for. The results showed that all three hybrid models had excellent prediction performance, but in general, the SMA-RF model had the best overall performance among all prediction models. According to the sensitivity analysis, the coefficient of lateral stress (K) and overburden depth (H) were the two most important parameters.
Article highlights
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We concentrated on the prediction of plastic zones surrounding underground powerhouse caverns.
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Three new hybrid metaheuristic algorithms-RF were developed to predict plastic zones surrounding underground powerhouse caverns.
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The SMA-RF model performed better than other models in predicting plastic zones around underground powerhouse caverns.
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Acknowledgements
This research is partially supported by the National Natural Science Foundation Project of China (42177164), the Distinguished Youth Science Foundation of Hunan Province of China (2022JJ10073) and the Innovation-Driven Project of Central South University (2020CX040).
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Zhou, J., Chen, Y. & Yong, W. Performance evaluation of hybrid YYPO-RF, BWOA-RF and SMA-RF models to predict plastic zones around underground powerhouse caverns. Geomech. Geophys. Geo-energ. Geo-resour. 8, 179 (2022). https://doi.org/10.1007/s40948-022-00496-x
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DOI: https://doi.org/10.1007/s40948-022-00496-x