System reliability of slopes using multimodal optimisation

Abstract

Many engineered and natural slopes have complex geometries and are multi-layered. For these slopes traditional stability analyses will tend to predict critical failure surfaces in layers with the lowest mean strength. A move toward probabilistic analyses allows a designer to account for uncertainties with respect to input parameters that allow for a more complete understanding of risk. Railway slopes, which in some cases were built more than 150 years ago, form important assets on the European rail network. Many of these structures were built at slope angles significantly higher than those allowed in modern design codes. Depending on the local geotechnical conditions these slopes may be susceptible to deepseated failure; however, a significant number of failures each year occur as shallow translational slips that develop during periods of high rainfall. Thus, for a given slope, two potential failure mechanisms might exist with very similar probabilities of failure. In this paper a novel multimodal optimisation algorithm ('Slips') that is capable of detecting all feasible probabilistic slip surfaces simultaneously is presented. The system reliability analysis is applied using polar co-ordinates, as this approach has been shown to be less sensitive to local numerical instabilities, which can develop due to discontinuities on the limit state surface. The approach is applied to two example slopes where the complexity in terms of stratification and slope geometry is varied. In addition the methodology is validated using a real-life case study involving failure of a complex slope.

A Multiobjective Decision-Making Model for Risk-Based Maintenance Scheduling of Railway Earthworks

“…The probability of failure is the area of the curve where demand exceeds capacity and the reliability index β is defined as the distance by which the mean or expected value of the performance function exceeds zero in units of its standard deviation, σ (Equation (4). As entire distributions are being used as inputs, reliability can either be determined through brute force computation using Monte Carlo or else approximated using mathematical optimization approaches such as First Order Reliability Methods [33], Second Order Reliability Methods [34], Genetic Algorithm [29], Particle Swarm Optimisation [35,36], etc. This paper utilizes a genetic algorithm approach, as recommended by Xue and Gavin [29], to determine the reliability index.…”

Probabilistic examination of the change in eigenfrequencies of an offshore wind turbine under progressive scour incorporating soil spatial variability

“…In an effort to eradicate such gross oversimplifications, probabilistic techniques have come to the fore for geotechnical applications [30][31][32][33][34]. Such approaches utilise all of the available data from a soil layer in the form of a probability distribution.…”

Rainfall thresholds as a landslide indicator for engineered slopes on the Irish Rail network

“…In reality, not every exceedance of threshold will result in a landslide, because slope stability largely depends on factors other than rainfall, such as the topographical, geotechnical, morphological and saturation characteristics of the slopes in question (Aleotti and Chowdhury, 1999). This is especially visible at rail earthwork slopes in Ireland, where a significant number are stable despite being built N150 years ago (Reale et al, 2016), while some have suffered multiple failures. In finding the frequency of landslide events, some researchers also consider the conditional probability of occurrence of a landslide given that the threshold has been exceeded, P{L|R N R T } (Jaiswal and van Westen, 2009;Berti et al, 2012).…”