Research And Application Of Key Technologies For Intelligent Supervisory Of Filling And Rolling Construction Quality

At present,the quality control methods of “dual controls” are mainly adopted in the quality management of earth-rock filling and rolling construction,of which one is manually controlling the roller compaction parameters including rolling times and driving speed of compaction machines,thickness of filling layer and smoothness of storehouse surface,the other is inspecting the test holes sampled manually in the working surface.However,the conventional method is difficult to ensure construction quality,because it is hard to accurately control these roller compaction parameters,interfered by human factors and extensive managements.Therefore,domestic and foreign scholars have used modern advanced spatial positioning technology,Internet of Things technology and other technologies to develop some construction quality supervisory systems for filling and rolling,which can supervise the filling and rolling construction process and feedback control.So that,the quality of rolling construction is always in a real controlled state.However,the research of these supervisory systems is mainly focused on data acquisition and system integration,and there is less in-depth research on how to calculate the rolling quality control parameter information from the acquired monitoring data more quickly,accurately and in more detail.This thesis aims to lay a theoretical and practical foundation for the evolution of filling and rolling construction to intelligent and unmanned,and systematically and deeply study the calculation of control parameters in the quality control of rolling construction.Aiming at rolling times,which is the important parameter in rolling construction,a fast calculation method of rolling times based on image processing is proposed.Considering the actual characteristics of the roller compaction surface and the characteristics of the monitoring data,a radial basis function neural network surface fitting method considering the gross error is proposed,and a high-precision digital elevation model of the roller compaction surface is established.The mathematical model between rolling parameters and compacting quality is established by using gradient boosting regression algorithm,and a evaluation method of rolling construction quality of the whole warehouse surface is realized.The main work and contributions of this thesis are as follows:(1)In order to overcome the shortcomings of the existing calculation methods of rolling times,a new calculation method of rolling times based on the Alpha hybrid algorithm is put forward.The experimental results show that the accuracy and accuracy of the calculation results of the new method are higher than that of the grid method.At the same time,the calculation speed of the new method is very fast.In order to obtain the calculation results of rolling times with the same resolution,the calculation time of the new method is only about 1/50 of that of the grid method.(2)In order to meet the calculation needs of roller compaction layer thickness and compaction value,the problem of coverage and update of elevation data during the construction process is analyzed,and a method for preprocessing monitoring measurement data to obtain surface elevation data of roller compaction is proposed.Considering the actual characteristics of the roller compaction surface,a radial basis function neural network surface fitting method is proposed.The experimental results show that the external coincidence accuracy of the roller compaction surface elevation fitted by the radial basis function neural network method is ±0.023 m,which is better than some common methods such as polynomial regression analysis method,inverse distance weighting method,and Kriging interpolation method.(3)Aiming at the characteristic that the radial basis function neural network is more sensitive to the gross errors of the training sample points,a robust radial basis function neural network fitting method based on the Gaussian difference extracting feature points is proposed.The experimental results show that the fitting mean error by radial basis function neural network when the data contains gross error is ±0.189 m,which is much lower than that the data does not contain gross errors..At the same time,the fitting mean error by the robust radial basis function neural network algorithm is ±0.027 m,which is close to that when the data does not contain gross errors.(4)A method based on the digital elevation model of the roller compaction surface to calculate the compaction value of each point on the surface is proposed.The experimental results show that although the precision of the calculation results is not very ideal,the correlation coefficient between the compaction value and dry density is-0.54.The smaller the settlement value,the higher the dry density,indicating that the better the compaction quality.(5)In order to solve the accuracy problem caused by using a limited number of discrete test pit test samples to evaluate the quality of silo surface compaction,after experimental comparison and analysis,a mathematical model between the rolling parameters and the compaction quality indicators based on the gradient boosting regression algorithm was established.On this basis,a evaluation method of rolling construction quality of the whole warehouse surface is further proposed,which provides a solution for the rolling supervisory system to achieve "final parameter control".(6)Based on the research results of this paper,a series of software for dam filling and rolling construction quality monitoring and a series of software for highway subgrade rolling quality monitoring have been developed and successfully applied.