Gaussian Process Model For Forecasting And Identifying Nonlinear Behavior Of Underground Engineering Rock Mass And Dynamic Intelligent Feedback Analysis

With the rapid development of economy, the scale of underground engineering is more and more big, the construction environment of underground engineering is more and more complicated, and the uncertainties of underground engineering is more and more various. It is required that the new theory and analysis methods are proposed to face new challenges. In view of this, rounding the theme that forecasting and identifying nonlinear behavior of undenrground engineering rock mass and dynamic intelligent feedback analysis, the underground engineering nonlinear system prediction model is proposed based on gaussian process (GP) machine learning. Then a novel hybrid optimization algorithm based on particle swarm optimization (PSO) combined GP is proposed. For convenience, the proposed algorithm will be called as PSO-GP that is used for intelligent optimization back analysis. Adopting dynamic intelligent feedback ideas, basing on numerical method combined with intelligent optimization technology, a new method of dynamic intelligent feedback analysis for large underground caverns is proposed. This method puts forward scientific proof for large underground caverns of reasonable excavation supporting design. In sum, the main works and results are listed as follows:1. Aiming to the fact that it is still difficult to reasonably identify and forecast rock mass mechanics nonlinear system behavior in underground engineering, the model based on GP is proposed for identifying and forecasting rock mass mechanics nonlinear system behavior. This provides a new route for accurately and reliably identifying and forecasting rock mass mechanics nonlinear system behavior in underground engineering. 2. Because of the complexity of the rock mass, the back analysis optimization problem is a typical complex, nonlinear global optimization problem. The traditional analytical optimization method based on gradient information only can obtain the local optimal solution. Using stochastic global optimization algorithm, in order to evaluate the quality of random solution, often needs thousands of times fitness evaluation by three-dimensional elaborate simulation, that is a time-consuming and high computational cost problem. Aiming to the problem, the intelligent optimization back analysis method based on GP and PSO is proposed for obtaining rock mechanics parameters in underground engineering. This provides a new route for reasonably obtaining rock mechanics parameters in underground engineering.3. The surrounding rock pressure release situation after excavation directly reflects the degree of rock mass stress unloading. In engineering practice, based on new austrian tunneling method, understanding the degree of surrounding rock pressure release situation can bring into play the surrounding rock bearing capacity and help judge the stability of surrounding rock for applying support. Therefore, a cooperative optimization algorithm based on PSO and GP for back analysis is proposed, then combined the FLAC3D develops a new method called PSO-GP-FLAC3D for joint back analysing of mechanical parameters of rock mass and rock pressure release rate. This provides a new method for high efficiently and low costly obtaining rock mechanics parameters and rock pressure release rate.4. The monitored sections are always assembled behind working face excavation. The displacement induced during this period is called loss displacement that has great significance for reasonably evaluating the stability of surrounding rock. In addition, the construction safety would be affected if you ignore the loss of displacement information, with causing the surrounding rock back analysis results appeared large errors. So, it is a critical problem that how to obtain the loss displacement for tunnel surrounding rock stability assessment and the safety of the construction. Therefore, the method based on PSO-GP-FLAC3D is proposed for obtaining the loss displacemen. This provides a new route for reasonably obtaining the loss of displacemen.5. Dynamic intelligent feedback analysis of large underground caverns is more complicated space and dimensional increase, including numerical model optimization and supporting scheme optimization that is a global optimization problem. The optimization process is a complicated nonlinear problem, with multiple decision variable and optimization indicators, and the relationship between the variables and optimization index is difficult to exactly express. In addition, in order to ensure the accuracy of the calculation, large-scale elaborately three-dimensional simulation is imperative that will cause a huge amount of calculation combining many excavation supporting combination schemes that is a very expensive and time consuming process. Moreover, the current dynamic feedback analysis method predicts the later excavation using the current optimized parameters and not calculates to guide the local reinforcement. At the same time, the existing methods only focus on the optimized effect, ignoring the former information. In addition, how to make simple, intuitive judgment for the high wall in the process of dynamic feedback, so as to provide reference for the construction, is also a problem to be solved in the process of underground cavern group construction. To the above problems, large underground caverns dynamic intelligent feedback analysis method based on GP is proposed that provides an effective route for large underground cavern group safety construction.6. Finally, applying the research fruits mentioned above to the dynamic intelligent feedback analysis of the large underground caverns of certain power station for the stability of surrounding rock in underground analysis and support optimization. Numerical model and support program are timely optimized by monitoring feedback information to achieve feedback analysis process for underground cavern group during construction. The results show that the large underground dynamic intelligent feedback analysis method is feasible that has provided guidance for rational design and safe construction of the large underground caverns.