本文關(guān)鍵詞： 巖溶 軟弱土層 土洞 概率　出處：《綜合運輸》2016年12期 　論文類型：期刊論文

【摘要】：本文首先介紹基于歸一化的錐尖阻力Q_t和歸一化的摩阻比F_R等觸探參數(shù)的Robertson分類圖及其解析表達(dá)形式,再根據(jù)研究區(qū)域的土層分布狀況,簡化Robertson分類圖和貝葉斯模型。最后收集江西省某高速公路沿線溶洞上覆土層89個CPT和鉆孔取樣資料,分別采用最大似然法和貝葉斯法對Robertson分類圖中的邊界進(jìn)行修正和比較,發(fā)現(xiàn)變異系數(shù)COV越小,先驗分布越接近Robertson土壤分類圖,預(yù)測結(jié)果越接近于Robertson土壤分類圖,準(zhǔn)確率也較以往的70%有所提高。變異系數(shù)COV越大,先驗分布越為含糊不清,預(yù)測結(jié)果越接近于最大似然法的結(jié)果,但由于考慮了先驗分布,準(zhǔn)確率仍高于最大似然法。而在樣本數(shù)量有限的情況下,最大似然法計算結(jié)果與Robertson分類圖存在較大差別,準(zhǔn)確率較差,應(yīng)謹(jǐn)慎使用。
[Abstract]:This paper first introduces the Robertson classification diagram of penetration parameters such as normalized cone tip resistance Q _ t and normalized friction ratio _ F _ R, and its analytical expression, and then according to the distribution of soil layer in the study area, The Robertson classification map and Bayesian model are simplified. Finally, 89 CPT and borehole sampling data are collected from the overlying soil layer of a karst cave along a highway in Jiangxi Province, and the boundary of the Robertson classification map is revised and compared by using the maximum likelihood method and Bayesian method, respectively. It was found that the smaller the coefficient of variation (COV) was, the closer the prior distribution was to the Robertson soil classification map, the closer the predicted result was to the Robertson soil classification map, and the higher the accuracy was compared with the previous 70%. The larger the coefficient of variation COV was, the more ambiguous the prior distribution was. The prediction results are closer to those of the maximum likelihood method, but the accuracy is still higher than that of the maximum likelihood method because the prior distribution is taken into account. In the case of limited number of samples, the results of the maximum likelihood method are quite different from that of the Robertson classification map. The accuracy is poor, should be used carefully.
【作者單位】： 江西交通咨詢公司;江西省交通科學(xué)研究院;
【基金】：國家自然科學(xué)基金(51508246) 交通運輸部重點科技項目(2013318780290) 江西省交通運輸廳科技項目(2015C0022)
【分類號】：U412.22
，

本文編號：1531545