The traditional reliability analysis of rock and soil slopes often uses Monte Carlo method to randomly sample a large number of random parameter samples,and then obtains the slope probability instability results through a large number of analysis and calculation,which requires a lot of manpower and material resources.Aiming at this problem,this study aims to explore the importance of active agent learning model in reliability analysis of rock and soil slopes.Firstly,the batch automatic calculation program of Monte Carlo-Flac3D model is compiled with Matlab software to realize the analysis of 10000 sets of Flac3D numerical models.Secondly,the computational efficiency of 10000 groups of Monte Carlo-Flac3D models and active agent learning models is compared.Among them,the time spent on 10000 numerical analysis is 34 man-hours,while the total time spent on the active agent learning model is only dozens of learning sample construction time,and the computational efficiency has been improved by hundreds of times.Finally,the calculation results show that the numerical simulation is highly similar to the analysis results of the active agent learning model,and the calculation results of tens of thousands of Flac3D calculations can be approximated only by dozens of numerical calculations. In summary,in terms of computational efficiency and accuracy,the active agent learning model has obvious advantages,highlighting its importance in slope reliability analysis.In the future,the active agent learning model can be used to replace the complex physical model calculation method in the risk assessment of slope engineering,which can provide reliable support for the quantitative risk assessment of slope instability.
维普
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