Hirohito Kojima

An inverse analysis of unobserved trigger factor for slope stability evaluation

Abstract This paper presents an inverse analysis of unobserved trigger factors for slope failures and landslides, based on structural equation modeling (SEM). Quantitative prediction models generally elucidate the relationship between past slope failures and causal factors (e.g. lithology, soil, slope, aspect, etc.), but do not consider trigger factors (e.g. rail fall, earthquake, weathering, etc.), due to difficulties in pixel-by-pixel observation of trigger factors. To overcome these, an inverse analysis algorithm on trigger factors is proposed, according to the following steps: • Step 1: The relationship between past slope failures (i.e. the endogenous variables), causal factors and trigger factors (i.e. the exogenous variables) are delineated on the path diagram used in SEM. • Step 2: The regression weights in the path diagram are estimated to minimize errors between the observed and reemerged ‘variance–covariance matrix’ by the model. • Step 3: As an inverse estimation, through the measurement equation in SEM between causal and trigger factors, a trigger factor influence (TFI) map is proposed. As an application, TFI maps are produced with respect to ‘slope failures’ and ‘landslides’, separately. As a final outcome, the differences in these TFI maps are delineated on a ‘difference’ (DIF) map. The DIF map and its interpretation are useful, not only for assessing danger areas affected by trigger factors, but also for ‘heuristic information’ in locating field measuring systems.