Probabilistic Analysis of the Obstruction of Water Sources Due to the Occurrence of Rainfall-Triggered Mass Movements

This paper presents a methodology for the probabilistic estimation of the obstruction of water streams generated by shallow mass movements triggered by rainfall. The study focuses on the Ovejas River, a tributary stream of the Medellín River, in the jurisdiction of the municipality of San Vicente in the department of Antioquia (Colombia). The occurrence of a mass movements was evaluated by deterministic and probabilistic methods based on the automation of processes of Geographic Information Systems (GIS) and spatial modeling. The spatial distribution of the mass movement hazard was estimated in terms of Factor of Safety (FoS) values by the deterministic method with physical basis SLIDE (Slope - Infiltration - Distributed Equilibrium), which allows the hazard zonation by calculating a FoS for rainfall-induced mass movements with different return periods. The rainfall regimes of the study area are estimated by means of a simple scaling Log Normal Model. On the other hand, the Probability of Failure (PF) analysis was performed under Rosenblueth’s punctual estimates method (PEM), which allows incorporating the uncertainty of the soil parameters. Subsequently, the resulting zones with high hazard that could detach and reach the Ovejas River channel are identified as sources for runout modeling by means of the Flow R model, thus estimating the extent of mass movement in probabilistic terms. In all the analyzed scenarios, the sliding material from the critical stability zones has a high probability of spreading to the riverbed of the main river. This analysis makes possible to identify those areas of the riverbed that should be analyzed with more detail and require possible intervention for the protection of the riverbed.

Lateral Seismic Fragility Assessment of Cable-Stayed Bridge with Diamond-Shaped Concrete Pylons

The cable-stayed bridge with diamond-shaped pylons is one of the most popular bridges because of its obvious advantages such as aesthetical appearance and smaller foundation. However, the diamond-shaped pylons have both inward and outward inclinations, which may result in complicated seismic behavior when subjected to lateral earthquake excitations. To end this, the finite element model of a cable-stayed bridge with diamond concrete pylon is developed firstly. Four limit states and corresponding damage index are defined for each critical section. Finally, the lateral seismic fragility of the components and system of CSB was carried out. Based on the result of probabilistic estimation of lateral seismic responses, the order of the damage probability in all four damage states for each component of bridge is given. The fragility curves of bridge system on the lower bound and upper bound are studied. Moreover, the system fragility of the entire bridge is compared with that of each component.

Neural Relation Inference for Multi-dimensional Temporal Point Processes via Message Passing Graph

Relation discovery for multi-dimensional temporal point processes (MTPP) has received increasing interest for its importance in prediction and interpretability of the underlying dynamics. Traditional statistical MTPP models like Hawkes Process have difficulty in capturing complex relation due to their limited parametric form of the intensity function. While recent neural-network-based models suffer poor interpretability. In this paper, we propose a neural relation inference model namely TPP-NRI. Given MTPP data, it adopts a variational inference framework to model the posterior relation of MTPP data for probabilistic estimation. Specifically, assuming the prior of the relation is known, the conditional probability of the MTPP conditional on a sampled relation is captured by a message passing graph neural network (GNN) based MTPP model. A variational distribution is introduced to approximate the true posterior. Experiments on synthetic and real-world data show that our model outperforms baseline methods on both inference capability and scalability for high-dimensional data.