【摘要】：煤炭開采引起地表沉陷變形,其中最大下沉值是衡量地表沉陷變形的關(guān)鍵指標(biāo)之一。在開采沉陷中采深、采高、煤層傾角、覆巖硬度和工作面傾向和走向長度等因素對最大下沉值有重要影響。黃土礦區(qū)的地表最大下沉值還須顧及黃土層厚度與特性等的影響。這些因素與地表最大下沉之間存在復(fù)雜的非線性關(guān)系,已有的最大下沉預(yù)計函數(shù)模型無法準(zhǔn)確地反映這種復(fù)雜的非線性特征,因而預(yù)計精度和適用范圍均具有很大的局限性。支持向量機(jī)可通過核函數(shù)將樣本空間維數(shù)映射到一個維數(shù)足夠高的特征空間中,把復(fù)雜的非線性問題變成線性問題。支持向量機(jī)還有懲罰機(jī)制對粗差數(shù)據(jù)進(jìn)行自動剔除,保證了模型的精度和可靠性,可為本文建立黃土礦區(qū)最大下沉預(yù)計模型提供有效手段。本文根據(jù)支持向量機(jī)原理及特點歸納出最大下沉預(yù)計模型的構(gòu)建步驟,依據(jù)該步驟先采用交叉驗證法確定了模型的懲罰參數(shù)和核函數(shù)參數(shù);再用迭代方法篩選樣本,取得最佳樣本,用該樣本進(jìn)行模型訓(xùn)練,確定預(yù)計模型的支持向量個數(shù)及系數(shù)等參數(shù),最終構(gòu)建了支持向量機(jī)回歸模型。再根據(jù)建模流程,基于MATLAB平臺的腳本語言開發(fā)了可視化程序,實現(xiàn)了模型訓(xùn)練與應(yīng)用過程的封裝,以簡潔、易懂的界面形式供用戶操作。將支持向量機(jī)回歸模型與已有的最大下沉預(yù)計模型進(jìn)行了對比分析,發(fā)現(xiàn)支持向量機(jī)回歸模型的精度和可靠性優(yōu)于其它函數(shù)模型。為進(jìn)一步揭示模型變量間的變化規(guī)律,分別取輸入變量的不同值,研究它們與最大下沉值之間的關(guān)系。結(jié)果表明:最大下沉值對采高、巖層硬度和煤層傾角這三個輸入變量的敏感性要高于其它變量。另外,對預(yù)計精度高的和預(yù)計偏差較大的樣本進(jìn)行了分析,發(fā)現(xiàn)預(yù)計偏差較大的樣本其寬深比和土厚比分別都小于0.3和0.35。反之,預(yù)計精度高的樣本的寬深比和土厚比至少有一個在0.4-0.5 以上。本文構(gòu)建的支持向量機(jī)回歸模型可用于黃土礦區(qū)地表最大下沉值的定量預(yù)計,具有一定的推廣應(yīng)用價值。
[Abstract]:Coal mining causes surface subsidence deformation, in which the maximum subsidence value is one of the key indexes to measure the surface subsidence deformation. The factors such as mining depth, mining height, coal seam inclination, overburden hardness, working face tendency and strike length have an important influence on the maximum subsidence value in the mining subsidence. The influence of the thickness and characteristics of loess soil layer should be taken into account in the maximum subsidence value of loess mining area. There is a complex nonlinear relationship between these factors and the maximum subsidence of the earth surface. The existing models of the maximum subsidence prediction function can not accurately reflect the complex nonlinear characteristics, so the prediction accuracy and the scope of application have great limitations. Support vector machines (SVM) can map the dimension of sample space to a feature space with high dimension by kernel function, and turn the complex nonlinear problem into a linear problem. Support vector machine (SVM) also has the penalty mechanism to eliminate the gross error data automatically, which ensures the accuracy and reliability of the model, and can provide an effective means for establishing the maximum subsidence prediction model of loess mining area in this paper. In this paper, according to the principle and characteristics of support vector machine, the construction steps of the maximum subsidence prediction model are summarized. According to this step, the penalty parameters and kernel function parameters of the model are determined by means of cross-validation. Then the optimal sample is selected by iterative method. The model is trained to determine the number of support vectors and the coefficients of the predicted model. Finally, the support vector machine regression model is constructed. According to the modeling flow, the visual program is developed based on the script language of MATLAB platform, which realizes the encapsulation of model training and application process, and provides users with simple and easy-to-understand interface form. The support vector machine regression model is compared with the existing maximum subsidence prediction model. It is found that the accuracy and reliability of the support vector machine regression model is better than other function models. In order to reveal the variation rule of the model variables, the relationship between the input variables and the maximum subsidence value is studied by taking the different values of the input variables respectively. The results show that the sensitivity of the maximum subsidence value to the three input variables, mining height, rock hardness and coal seam inclination, is higher than that of other variables. In addition, it is found that the ratio of width to depth and the ratio of soil thickness to depth are less than 0.3 and 0.35, respectively, for the samples with high prediction accuracy and large prediction deviation. On the contrary, the aspect-depth ratio and soil-thickness ratio of the samples with high accuracy are at least 0.4 脳 0.5. The support vector machine regression model constructed in this paper can be used for quantitative prediction of the maximum subsidence value of the loess mining area, and has a certain value of popularization and application.
【學(xué)位授予單位】：西安科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TD327

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本文編號：2440017