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Rainfall is an important trigger for a large number of landslides. Physics-based quantitative risk assessment of rainfall-induced landslides requires modelling of rainfall uncertainty in addition to soil parameters. Explicit modelling of rainfall uncertainty should consider the uncertainties in rainfall intensity, duration, as well as the occurrence of rainfall events. Towards this aspect, this study proposes a stochastic rainfall model, where the joint distribution of rainfall duration and intensity is constructed using copula theory and the occurrence of rainfall events is described using Poisson process. Based on the proposed stochastic rainfall model, a computationally efficient reliability method with a machine learning-based surrogate model is developed for assessing the failure probability of a slope subjected to rainfall infiltration within a given time period. An illustrative example with real rainfall data from Singapore is utilised to demonstrate the proposed approach. The results suggest that the slope failure probability is less sensitive to the uncertainty in rainfall occurrence, but highly sensitive to the adopted inter-event time definition for characterisation of the rainfall event. Overall, this study provides a useful stochastic rainfall model and some practical guidelines for quantitative risk assessment of rainfall-induced landslides. [ABSTRACT FROM AUTHOR]

Specific surface area (SSA) and cation exchange capacity (CEC) are two fundamental clay properties. However, the determination of CEC and SSA is challenging due to inherent uncertainties and difficulty in experimental measurement. A popular approach is to employ transformation models for their estimation. However, most of the existing models were developed on limited sample sizes and quantification of uncertainty associated with the estimate is not possible. Therefore, this study proposes a multivariate probabilistic approach for estimation of CEC and SSA. First, a five-dimensional database (278 * 5) for the parameters liquid limit (LL), plasticity index (PI), clay fraction (CF), CEC, and SSA (labelled as CLAY/C-S/5/278) is developed. Thereafter, multivariate distribution for the five parameters in the database is constructed using the vine copula approach. Implementation of the proposed approach is demonstrated by updating the prior-unconditional probability density functions (PDFs) of CEC and SSA given single or multiple clay parameters using Bayes' rule. The posterior or conditional PDFs of CEC and SSA are also summarized as practitioner- friendly analytical expressions. Two geotechnical application examples are shown as well. In the proposed approach, CEC and SSA are characterized by their complete joint distribution and therefore this approach is superior to the popular deterministic transformation approach in literature. Key words: cation exchange capacity, specific surface area, CLAY/C-S/5/278, multivariate distribution, vine copula. Les deux proprietes fondamentales de l'argile sont la surface specifique (SSA) et la capacite d'echange cationique (CEC). La determination de la CEC et de la SSA est cependant difficile en raison des incertitudes inherentes et de la difficulte de la mesure experimentale. L'approche la plus courante consiste a utiliser des modeles de transformation pour son estimation. Neanmoins, pour la plupart, les modeles existants ont ete developpes sur des echantillons de taille limitee, et la quantification de l'incertitude associee a l'estimation n'est pas possible. C'est pourquoi cette etude propose une approche probabiliste multivariee pour l'estimation de la CEC et de la SSA. Tout d'abord, on developpe une base de donnees en cinq dimensions (278 * 5) pour les parametres limite liquide (LL), indice de plasticite (PI), fraction d'argile (CF), CEC et SSA (etiquetee CLAY/C- S/5/278). Par la suite, la distribution multivariee pour les cinq parametres de la base de donnees est construite en utilisant l'approche de la copule de la vigne. On demontre la mise en reuvre de l'approche proposee en mettant a jour la fonction de densite de probabilite (PDF) anterieureinconditionnelle de la CEC et de la SSA, en fonction de parametres d'argile simples ou multiples, a l'aide de la regle de Bayes. Les PDF posterieure ou conditionnelle de la CEC et de l'ASS sont egalement resumees sous forme d'expressions analytiques conviviales pour le praticien. Deux exemples d'applications geotechniques sont egalement presentes. Dans l'approche proposee, la CEC et la SSA se caracterisent notamment par leur distribution conjointe complete, et est donc superieure a l'approche de transformation deterministe populaire dans la litterature. [Traduit par la Redaction] Mots-cles: capacite d'echange cationique, surface specifique, CLAY/C-S/5/278, distribution numerique, copule de vigne., 1. Introduction Surface charge plays a crucial role in influencing the physical, chemical, and engineering behaviour of fine-grained soils. Cation exchange capacity (CEC) and specific surface area (SSA) are the [...]

Geotechnical site characterization using multivariate, limited (sparse), and missing (incomplete) data is an important but challenging task, particularly in high dimensions. Toward this problem, this study proposes a Bayesian vine algorithm. In the proposed algorithm, the task of Bayesian update in higher dimensions is translated into a series of lower-dimensional (usually ≤2) update tasks using conditional correlation vine. This feature of the proposed algorithm makes it scalable and computationally efficient in higher dimensions. Multiple examples using two-dimensional (2D), five-dimensional (5D), 10-dimensional (10D), 20-dimensional (20D), 50-dimensional (50D), and 100-dimensional (100D) data are shown to demonstrate the capability of the proposed algorithm. The results suggest that the proposed algorithm can be used successfully for geotechnical site characterization. Even an ultrahigh 50D joint distribution with >1,000 parameters (1,325) can be estimated in around 20 min. The proposed algorithm can handle multivariate data sets with limited and missing values and can also handle non-Gaussian multivariate joint distributions. The proposed algorithm only considers cross-correlation in the site data and doesn't take into account spatial correlation. [ABSTRACT FROM AUTHOR]