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AN INVERSE AND DECOMPOSITIONAL ANALYSIS OF CHAIN TRIGGER FACTORS FOR SLOPE FAILURE HAZARD ZONATION

机译:斜坡故障危害区划区划触发因子的逆和分解分析

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This paper presents an inverse- and decompositional-analysis of unobserved "chain-trigger factors" according to slope failure, based on Structural Equation Modeling (SEM). Quantitative prediction models for slope failures generally elucidate the relationship between past slope failures and causal factors (e.g. geology, soil, slope, aspect, etc.). Due to the difficulties of obtaining pixel-based observations on the trigger factors (e.g. rainfall, earthquake, weathering, etc.), the trigger factors as explanatory variables are substituted for some of the causal factors in constructing prediction models, on the assumption that there are some correlations between causal and trigger factors. As a measure, we had tackled to construct a Trigger Factor Inverse analysis model (TFI model) in which the relationship between past slope failures (i.e. endogenous variables), causal factors (i.e. explanatory variables), and trigger factors (i.e. unobserved variables) are delineated on the path diagram in SEM approach. In the TFI model, through the "measurement equation" defined between the causal factors (i.e. observed variables) and the trigger factors (i.e. unobserved latent variable), the trigger factor can be inversely estimated. As the subsequent subjects for the previous studies, in this contribution, we have tried to decompose trigger factors into the "1st trigger factor" and the "2nd trigger factor" with respect to slope failures, which had been induced by Niigata Heavy Rainfall (Jul. 13, 2004:Case1) and Niigata Chuetsu Earthquake (Oct. 23, 2004:Case 2). The analytical procedure consists of the following steps. (1) Step 1: The 1st and the 2nd Trigger Factor Influence maps (TFI map) are produced according to Case1 and Case 2, respectively. (2) Step 2: The differences in these TFI maps are delineated on a "difference (DIF) maps," which are also summarized on the "pair-wise comparative table." (3) Step 3: Through the Hayashi's quantification method of the fourth type, the scatter-diagram is delineated with respect to items corresponding to each TFI map. By using those scatter-diagram jointly with the pair-wise comparative table, the effective and efficient analysis on the "chaintrigger factors" can be achieved with respect to slope failures, simultaneously.
机译:基于结构方程模型(SEM),本文提出了根据斜坡故障的不观察室“链触发因子”的反相和分解分析。用于坡度故障的定量预测模型通常阐明过去斜率故障与因果因子之间的关系(例如地质,土壤,斜坡,方面等)。由于在触发因子(例如降雨,地震,风化等)上获得基于像素的观察的困难,触发因子是解释变量的替代为构建预测模型的一些因果因素,在那里因果和触发因素之间是一些相关性。作为一种措施,我们已经解决了构建触发因子逆分析模型(TFI模型),其中过去斜率故障(即内源变量)之间的关系,因果区(即解释变量)和触发因子(即未观察的变量)是在SEM方法的路径图上划定。在TFI模型中,通过在因果区(观察变量)和触发因子之间定义的“测量方程”(即,未观察到的潜变量),可以估计触发因子。作为前一项研究的后续主题,在这一贡献中,我们试图将触发因子分解为“第1触发因子”和“第二触发因子”,相对于斜坡故障,这是由NiiGata大雨诱导的(JUL 。13,2004:案例1)和Niigata Chuetsu地震(2004年10月23日:案例2)。分析程序包括以下步骤。 (1)步骤1:第1和第2触发因子影响图(TFI MAP)分别根据壳体1和壳体2产生。 (2)步骤2:这些TFI地图的差异在“差异(DIF)地图”上划算,其中还概述了“对比较表”。 (3)步骤3:通过Hayashi的第四种定量方法,散射图相对于对应于每个TFI地图的物品描绘。通过使用与成对比较表共同的那些散射图,同时可以实现对“粉轴因子”的有效和有效的分析。

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