Design And Optimization Of The Quality And Energy Efficiency Analysis Model Of Cement Grouting Construction Based On Data Mining

Cement grouting construction is currently an important measure to ensure the stability and long-term operation of dams and hydraulic structures.Due to the construction features of cement grouting,it has concealment and unpredictability,and the geological and geomorphological environment of different construction sites is not the same.The diffusion,permeation,and filling of cement slurry in the stratum are all in an unknown state,resulting in the inability to directly judge the quality of grouting construction.If there are situations such as under-grouting or leakage during the grouting process,it will cause the construction unit to need re-grouting,not only extending the construction period but also causing the cost of raw materials and labor to rise sharply,reducing the efficiency of grouting.At present,research on the energy efficiency of cement grouting is mostly focused on the amount of grouting and the amount of cement injection,while there is less research on the construction quality and grouting effect during the grouting process.In the existing research on grouting quality and effect,there is no exploration and analysis of various data in the grouting project,and the related intelligent algorithms for data mining are relatively single.Therefore,it is of great significance to control the construction quality during the grouting process by mining and analyzing the data related to the grouting project.This paper focuses on the above problems,using the grouting construction data of a pumped-storage power station dam curtain as the basis,and applying data mining algorithms to study the quality of the grouting project through staged modeling.The main work completed in this study is as follows.(1)Introduce the construction process of cement grouting.The grouting process mainly includes overall construction and single-hole construction,the former is divided into pregrouting preparation and grouting construction,and the latter is an elaboration of the specific steps and related details of single-hole construction.Secondly,briefly introduce the characteristics of grouting construction,with concealment and unpredictability as its main features.(2)Analyze the grouting data table and preprocess the grouting data to prepare data for model construction.Summarize and organize the relevant data tables generated during the grouting process,and analyze multiple data tables in conjunction to explore the intrinsic connections between data.Compare the structural differences of different data tables,and extract grouting process data and segment statistical data as raw data for modeling.At the same time,use SPSS software to analyze the factors affecting the quality of grouting construction and determine the inputs and outputs of the energy efficiency analysis model.(3)Establish a staged energy efficiency analysis model.Use the BP neural network algorithm to establish energy efficiency analysis models for grouting process data and segment statistical data,respectively,and compare the performance differences of the models.Experiments show that the energy efficiency analysis model established using segment statistical data has better performance.Compare the energy efficiency analysis model established using the SVR algorithm with the energy efficiency analysis model established using the BP neural network algorithm.The results show that the former model performs better than the latter model.(4)Optimize the staged energy efficiency analysis model.Combine the Grey Wolf optimization algorithm and its improved algorithm to optimize the parameters of the SVR algorithm and establish an energy efficiency analysis model.The experimental results show that the performance of the SVR model using the improved gray wolf optimization algorithm is superior.The new grouting data is introduced to test the energy efficiency analysis model,and the results show that the energy efficiency analysis model can effectively predict the grouting quality of each sequence.(5)Design a visual interface for energy efficiency analysis.Combined with the energy efficiency analysis model,the corresponding visual interface is designed using the Py Qt5 module based on the Python language,so that users can use the energy efficiency analysis model conveniently and intuitively.The test results show that the visual interface can present the prediction results of grouting quality friendly based on the energy efficiency analysis model.