Performance Comparison of Particle Swarm Optimization and Genetic Algorithm for Back-analysis of Soil Layer Geotechnical Parameters | ||
| Journal of Mining and Environment | ||
| مقاله 13، دوره 14، شماره 1، فروردین 2023، صفحه 217-232 اصل مقاله (8.59 M) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jme.2023.12452.2260 | ||
| نویسندگان | ||
| Leila Nikakhtar1؛ Shokroallah Zare* 1؛ Hossein Mirzaei2 | ||
| 1Shahrood University of Technology, Faculty of Mining, Geophysics and Petroleum Engineering, Shahrood, Iran | ||
| 2Department of Mining Engineering, Sahand University of Technology,Tabriz, Iran | ||
| چکیده | ||
| Surface settlement induced by tunneling is one of the most crucial problems in urban environments. Hence, accurate prediction of soil geotechnical properties is an important prerequisite in the minimization of it. In this research work, the amount of surface settlement is predicted using three-dimensional numerical simulation in the finite difference method and Artificial Neural Network (ANN). In order to determine the real geotechnical properties of soil layers around the tunnel; back-analysis is carried out using the optimization algorithm and monitoring data. Among the different optimization methods, genetic algorithm (GA) and particle swarm optimization (PSO) are selected, and their performance is compared. The results obtained show that the artificial neural network has a high ability with the amounts of R=0.99, RMSE=0.0117, and MSE= 0.000138 in predicting the surface settlement obtained from 150 simulations from randomly generated data. Comparing the results of back-analysis using the optimization algorithm, the genetic algorithm shows less error than the particle swarm algorithm in different initial populations. In all cases of analysis, the calculation time for both algorithms lasts about 5 minutes, which indicates the applicability of both algorithms in optimizing the parameters in mechanized tunneling in a short time. | ||
| کلیدواژهها | ||
| Back analysis؛ FLAC3D؛ artificial neural network؛ genetic algorithm؛ Particle swarm optimization | ||
| مراجع | ||
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