岩土力学
巖土力學
암토역학
ROCK AND SOIL MECHANICS
2013年
8期
2393-2400
,共8页
左自波%张璐璐%程演%王建华%何晔
左自波%張璐璐%程縯%王建華%何曄
좌자파%장로로%정연%왕건화%하엽
非饱和土%渗流%蒙特卡罗方法%Metropolis 算法%反分析
非飽和土%滲流%矇特卡囉方法%Metropolis 算法%反分析
비포화토%삼류%몽특잡라방법%Metropolis 산법%반분석
unsaturated soil%seepage%Monte Carlo method%Metropolis algorithm%back analysis
基于贝叶斯理论，以马尔可夫链蒙特卡罗方法（Markov chain Monte Carlo Simulation, MCMC 法）的自适应差分演化 Metropolis 算法为参数后验分布抽样计算方法，建立利用时变测试数据的参数随机反分析及模型预测方法。以香港东涌某天然坡地降雨入渗测试为算例，采用自适应差分演化 Metropolis 算法对时变降雨条件下非饱和土一维渗流模型参数进行随机反分析，研究参数后验分布的统计特性，并分别对校准期和验证期内模型预测孔压和实测值进行比较。研究结果表明，DREAM算法得到的各随机变量后验分布标准差较先验分布均显著减小；经过实测孔压数据的校准，模型计算精度很高，校准期内95%总置信区间的覆盖率达到0.964；验证期第2～4个阶段95%总置信区间的覆盖率分别为0.52、0.79和0.79，模型预测结果与实测值吻合程度较高。
基于貝葉斯理論，以馬爾可伕鏈矇特卡囉方法（Markov chain Monte Carlo Simulation, MCMC 法）的自適應差分縯化 Metropolis 算法為參數後驗分佈抽樣計算方法，建立利用時變測試數據的參數隨機反分析及模型預測方法。以香港東湧某天然坡地降雨入滲測試為算例，採用自適應差分縯化 Metropolis 算法對時變降雨條件下非飽和土一維滲流模型參數進行隨機反分析，研究參數後驗分佈的統計特性，併分彆對校準期和驗證期內模型預測孔壓和實測值進行比較。研究結果錶明，DREAM算法得到的各隨機變量後驗分佈標準差較先驗分佈均顯著減小；經過實測孔壓數據的校準，模型計算精度很高，校準期內95%總置信區間的覆蓋率達到0.964；驗證期第2～4箇階段95%總置信區間的覆蓋率分彆為0.52、0.79和0.79，模型預測結果與實測值吻閤程度較高。
기우패협사이론，이마이가부련몽특잡라방법（Markov chain Monte Carlo Simulation, MCMC 법）적자괄응차분연화 Metropolis 산법위삼수후험분포추양계산방법，건립이용시변측시수거적삼수수궤반분석급모형예측방법。이향항동용모천연파지강우입삼측시위산례，채용자괄응차분연화 Metropolis 산법대시변강우조건하비포화토일유삼류모형삼수진행수궤반분석，연구삼수후험분포적통계특성，병분별대교준기화험증기내모형예측공압화실측치진행비교。연구결과표명，DREAM산법득도적각수궤변량후험분포표준차교선험분포균현저감소；경과실측공압수거적교준，모형계산정도흔고，교준기내95%총치신구간적복개솔체도0.964；험증기제2～4개계단95%총치신구간적복개솔분별위0.52、0.79화0.79，모형예측결과여실측치문합정도교고。
Based on the Bayesian theory, a probabilistic back analysis method using time-varying measurement data is established. The back calculated posterior distributions are determined using the Markov chain Monte Carlo method (MCMC) with the differential evolution adaptive Metropolis algorithm. In this paper, a case study of a well instrumented natural terrain is presented. The deterministic model for pore-water pressure evaluation is an analytical model. Field measurements of pore-water pressure are used to calibrate the unsaturated parameters of the deterministic model. Statistical properties of the posterior distributions are presented and discussed. It is found that the posterior standard deviations of the six parameters are all greatly reduced. The predicted and measured pore-water pressures during the calibration period and the validation period are compared. The coverage of the 95%total uncertainty bounds is estimated to be 0.964 for the calibration period, during which the field measured pore pressures are used to back analyze the input parameters. For periods 2 to 4 of the validation period, the coverage by the 95% total uncertainty bounds are 0.52, 0.79 and 0.79, respectively. These results indicate an overall good performance by the calibrated model.
