Research On Detection Method Of Compaction Degree Based On Laser Image Of Soil

The degree of compaction of the soil is the most important indicator of quality ofconstruction foundation construction.However,as the testing work is heavy and needs to beaccurate, fast, convenient and real-time, therefore the research of non-destructive testing ofthe degree of compaction of the soil becomes a hot issue.In this paper, the non-destructivetesting detection method of the degree of compaction of the soil which is based on the laserimage will be researched by using the photovoltaic technology and computer technology. Themain tasks are as follows:First, in this paper, the current research situation and the main problems of testingtechnology of the degree of compaction of soil and laser imaging technology are summedup, and the shortcomings of the method of testing soil compaction are also pointed out. Theevaluation method of soil compaction and compaction theory are discussed from the aspectsof measurements of soil moisture content, optimum moisture content and maximum drydensity and the main factors of soil compaction are systematically analysed.And similaritiesand differences of the organizational structure of indigenous organizations and biological arecomparatively analysed. laser propagation in the soil organization and laser imaging arestudied by drawing on the experience of the theory of laser transmission rule and laserimaging in biological tissue. The paper also analyses the optical properties of the soilorganization and photonic radiative transfer equation in the organization and uses the epitaxialboundary conditions diffusion approximation theory for solving the transport equation.According to the law of the transmission of the laser in the soil organization and its imagingtheory and through soil tissue laser image, the optical parameters of the soil organizationsrelated to their degree of compaction and the change rate of laser image gray value can befound out.The basic principles of the laser image-based detection systems of soil compactiondegree, system components and the key technologies of testing are studied and analyzed.Secondly, I did some research about whether the diffusion approximation theory can beapplied to the transfer equation solving and obtain the optical parameters of soil. The paperanalysed Monte Carlo method and simulation, then used the Monte Carlo method to simulatethe propagation of light in the soil organization,and finally analysed and compared thesimulation results with the diffusion approximation theory results.The result of thecomparison shows: the maximum average absolute error is0.0497and the maximum average relative error is10.44%between two. The results show that using the radiation propagationtheory and diffusion theory to obtain the optical properties of soil is feasible.Then, the paper studied the test and results of the laser image on the soil tissue.According to the soil tissue sample obtained through the compaction test, and in accordancewith the law of the transmission of the laser in the soil organizations and imaging theory,the laser image is collected by using the laser image detection system through the soilorganizations;The location of the center of the image can be found out through existingimage processing method. aimed at the problems about the processing steps and slow-speedof existing image processing method, the paper put forward the Matlab GUI interfaceinteraction approach,which reduces the processing steps as well as improve the speed. And thedistribution of diffuse light on the surface of the soil tissue of different moisture content isalso analysed. The experimental results show that the scattering and distribution of light in thesoil tissues is mainly decided by the optical characteristics of the soil organization; theabsorption and scattering coefficients of the soil tissue can be calculated by using thediffusion theory equation and the least-squares method. And then the change rate of thediffuse reflectance of the soil tissue, absorption coefficient, scattering coefficient, The changerate of the laser image gray value and correlation of soil compaction degree are analysed,Diffuse reflectance change rate increases with the increase of the degree of compaction,Grayrate of change has a downward trend with the rise of the degree of compaction.Absorptioncoefficientu atends to decrease as the degree of compaction increases.Scattering coefficientu shas an increasing trend as the degree of compaction increases. At the same time,the existingtexture features algorithm of the laser image is analysed and new image texture featuresalgorithm is put forward.The result is that the uniformity of image texture, energy, the3rdorder moment value, related and inverse difference moment have a tendency to increase withthe increase of the degree of compaction.However,small gradient strengths, gradientadvantage, the uneven distribution of gray, gray average, average gradient gray variance,gradient variance, inertia correlation, contrast, average brightness, average contrast,smoothness, consistent and entropy value decrease with the increase of the degree ofcompaction trend.Finally, the paper studies how to use the BP neural network to predict the degree ofcompaction and test verification.Because of the so many characteristics of the degree of soil compaction,therefore, the neural network prediction and evaluation of the degree ofcompaction is raised. We selected all characteristics related to the degree of compaction asinput variables of the neural network model. The diffuse reflectance rate of change, the graymean rate of change, the absorption coefficient, scattering coefficient, contrast, an averageluminance of consistency, uniformity, energy, correlation, the third order moment value, theaverage contrast, smoothness and entropy are treated as the first set of original variables.Thediffuse reflectance rate of change the gray mean rate of change, the absorption coefficient,scattering coefficient, related deficit moment, the advantages of small gradients, gradientstrengths, the uneven distribution of gray, gray average gradient average grayvariance,gradient variance and inertia are treated as a second set of the original variables.And throughthe principal component analysis (PCA), the original data was reduced to five principalcomponents factor.The contribution rate of the main component factors of characteristic datain the first group is97.3%,The contribution rate of the main component factors ofcharacteristic data in the second group is98.6%, and then respectively use these two sets offeature data to establish BP neural network prediction model to predict the degree ofcompaction of soil samples and compare the predicted results with those of Cutting Ringmethod.The average absolute error of the predicted value of characteristic data in the firstgroup was0.0937and the average relative error is10.08%.The average absolute error of thepredicted value of characteristic data in the second group is0.0714and the average relativeerror is7.71%. the predicted value of characteristic data in the second group is moreaccurate than those of the first set.So,this paper uses the characterization data in the secondset to establish prediction model.And the prediction accuracy shows that using the BP neuralnetwork model to predict soil compaction is feasible.The BP neural network prediction modelwas also verified by a different soil data.The verifIied average absolute error is0.0862and theaverage relative error of8.76%,which proves that using laser imaging to detect soilcompaction is feasible.