Abstract
This paper presents a soft computing of integrating artificial neural networks (ANNs) and genetic algorithms (GAs) to back analyze the earth stress field based on hydraulic fracturing. In this method, the ANN model is employed to map the relationship between the earth stress parameters and hydraulic fracturing behavior instead of numerical computation, and the advantage of this work is that it can conveniently conduct the integration of ANN and optimization algorithm and effectively reduce the workload of numerical computation by using directly the field-measured information to build learning samples. In addition, this can also improve accuracy of earth stress determination from field test data sets for ANN model. The GA is applied to implement multi-objective earth stress parameters optimization on the basis of the objective function. The field monitoring information in a practical project of hydropower engineering is used to verify the proposed soft computing in this study. Investigation results demonstrate that the proposed methodology is capable and valuable in addressing geomechanical parameters determination in hydropower engineering.
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Acknowledgements
The authors express their sincere gratitude to the editor and anonymous reviewers for their insightful comments. This study was supported by project funded by National Natural Science Foundation of China (51678536), Natural Science Foundation of Henan Province (182300410160), Science and Technology Research Planning Project of Henan Province (182102310804, 182102310763), Training Project for Young Scholar of Institutions of High Education of Henan Province (2018GGJS122), Key Research Project of Institution of Higher Learning in Henan Province (20B560002) and Anyang Science and Technology Research Planning Project (Anke[2018]66).
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Zhang, S., Yuan, Y., Fang, H. et al. An application of soft computing for the earth stress analysis in hydropower engineering. Soft Comput 24, 4739–4749 (2020). https://doi.org/10.1007/s00500-019-04542-x
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DOI: https://doi.org/10.1007/s00500-019-04542-x