An Integrated Bayesian Risk Model for Coastal Flow Slides Using 3-D Hydrodynamic Transport and Monte Carlo Simulation

Abstract

The literature suggests two forms of flow slides: breaching and liquefaction. Both forms of failure have comparable ultimate circumstances, but the progression and sand movement mechanisms of breaching failure diverge from those of liquefaction. The first type, breaching, occurs in densely packed sand and is characterized by slow sand grain discharge throughout the dilation of the failing soil particles and negative excess pore pressures. The latter form, known as liquefaction, is the process by which a mass of soil abruptly begins to behave like a flowing liquid, and as a result, it can flow out across overly mild slopes. The process begins in compacted sand and is linked to positive surplus pore water pressures that are caused by the compaction of the sand. Despite the available literature on flow slide failures, our understanding of the mechanisms involved remains limited. Since flow slides often begin below the water surface, they can go undetected until the collapse reaches the bank above ground. The complexity of flow slides requires the use of cutting-edge technological instruments, diving equipment, advanced risk assessment, and a variety of noteworthy probabilistic and sensitivity analyses. Hence, we developed a new sensitivity index to identify the risk of breach failure and vulnerable coastal areas to this risk. In addition, we developed a sophisticated hybrid model that allows for all possibilities of flow slides in sync with random variables used in this new sensitivity index. In this new hybrid model, three distinctive models exist. The 3D Hydrodynamic Model addresses waves, wind, current, climate change, and sediment transport. The Monte Carlo Simulation is responsible for sensitivity analysis, and the Bayesian Network focuses on joint probabilities of coastal flow slide parameters of this new index that incorporates all environmental parameters, including climate change. With the assistance of these three models, researchers aim to: (a) expand the application scope by presenting a method on coastal flow slides; (b) consider different particle diameters corresponding to critical angle slope failure; (c) analyze variables that can play a pivotal role in the flow slides; and (d) present a methodology for coupling coastal flow slide projections with reliable outcomes. The hybrid model incorporates random variables of retrogressive breach failures, and the new risk index considers their ranges to control the simulation. The use of such a hybrid model and risk index offers a robust and computationally efficient approach to evaluating coastal flow slides.

Unleashing the Potential of a Hybrid 3D Hydrodynamic Monte Carlo Risk Model for Maritime Structures’ Design in the Imminent Climate Change Era

“…The model is implemented in the pipeline project of Burdur Lake by incorporating various sub-models to consider the critical environmental conditions at the limit state, such as wind, waves, current, storms, and climate change. These variables are modeled by the three-dimensional hydrodynamic model Hydrotam-3D [3,12,13].…”

“…where ρ is the density in gr/cm 3 . The following equations are used to determine the density as a function of temperature and salinity:…”

“…The decline in the surface area of Burdur Lake is one of the most obvious examples of the effects of the global warming era. The lake had a maximum water level (857.5 m from sea datum) in 1970 with a surface area of 215.7 km 2 and a volume of 6.826 hm 3 (Figure 8). The part of the bathymetry for the pipeline is illustrated in Figure 9, and the variation in the lake area (1984-2019) is shown in Figure 10.…”

“…A probabilistic model for submarine pipelines was developed by [3] to integrate environmental uncertainties by adopting an MCS-based approach. Ref.…”

How Visualizing Seafloor Data Improves Coastal Management Strategies

“…These maps provide planners the opportunity to evaluate the viability of coastal development projects and guarantee that the projects are in line with environmental and sustainability goals (Langton et al, 2008;Lidz, 2011;Oliver et al, 2012) In addition, coastal planning may be improved with the use of bathymetry data by locating locations that are more likely to be affected by coastal hazards, such as storm surges and rising sea levels, both of which are essential factors in coastal planning (Cuthbertson et al, 2018;Lee & Brimblecombe, 2016;Nicholls & Cazenave, 2010). Data from bathymetry may also be used to better understand the consequences of coastal erosion and sediment movement on the coastline (Durap et al, 2023;Käyhkö et al, 2019;Simmons et al, 2017;Vásquez Lavín et al, 2016). Bathymetric data enhances wave runup prediction with machine learning, improving accuracy and reliability (Durap, 2023).…”