Target reliability-based optimisation for internal seismic stability of reinforced soil structures

In this paper, an approach for target component and system reliability-based design optimisation (RBDO) to evaluate safety for the internal seismic stability of geosynthetic-reinforced soil (GRS) structures is presented. Three modes of failure are considered: tension failure of the bottom-most layer of reinforcement, pullout failure of the topmost layer of reinforcement, and total pullout failure of all reinforcement layers. The analysis is performed by treating backfill properties, geometric and strength properties of reinforcement as random variables. The optimum number of reinforcement layers and optimum pullout length needed to maintain stability against tension failure, pullout failure and total pullout failure for different coefficients of variation of friction angle of the backfill, design strength of the reinforcement and horizontal seismic acceleration coefficients by targeting various system reliability indices are proposed. The results provide guidelines for the total length of reinforcement required, considering the variability of backfill as well as seismic coefficients. One illustrative example is presented to explain the evaluation of reliability for internal stability of reinforced soil structures using the proposed approach. In the second illustration (the stability of five walls), the Kushiro wall subjected to the Kushiro-Oki earthquake, the Seiken wall subjected to the Chiba-ken Toho-Oki earthquake, the Ta Kung wall subjected to the Ji-Ji earthquake, and the Gould and Valencia walls subjected to Northridge earthquake are re-examined.

Reliability-based load and resistance factor design approach for external seismic stability of reinforced soil walls

Load and resistance factor design (LRFD) approach for the design of reinforced soil walls is presented to produce designs with consistent and uniform levels of risk for the whole range of design applications. The evaluation of load and resistance factors for the reinforced soil walls based on reliability theory is presented. A first order reliability method (FORM) is used to determine appropriate ranges for the values of the load and resistance factors. Using pseudo-static limit equilibrium method, analysis is conducted to evaluate the external stability of reinforced soil walls subjected to earthquake loading. The potential failure mechanisms considered in the analysis are sliding failure, eccentricity failure of resultant force (or overturning failure) and bearing capacity failure. The proposed procedure includes the variability associated with reinforced backfill, retained backfill, foundation soil, horizontal seismic acceleration and surcharge load acting on the wall. Partial factors needed to maintain the stability against three modes of failure by targeting component reliability index of 3.0 are obtained for various values of coefficients of variation (COV) of friction angle of backfill and foundation soil, distributed dead load surcharge, cohesion of the foundation soil and horizontal seismic acceleration. A comparative study between LRFD and allowable stress design (ASD) is also presented with a design example. (C) 2014 Elsevier Ltd. All rights reserved.

Holistic Measures for Evaluating Prediction Models in Smart Grids

The performance of prediction models is often based on ``abstract metrics'' that estimate the model's ability to limit residual errors between the observed and predicted values. However, meaningful evaluation and selection of prediction models for end-user domains requires holistic and application-sensitive performance measures. Inspired by energy consumption prediction models used in the emerging ``big data'' domain of Smart Power Grids, we propose a suite of performance measures to rationally compare models along the dimensions of scale independence, reliability, volatility and cost. We include both application independent and dependent measures, the latter parameterized to allow customization by domain experts to fit their scenario. While our measures are generalizable to other domains, we offer an empirical analysis using real energy use data for three Smart Grid applications: planning, customer education and demand response, which are relevant for energy sustainability. Our results underscore the value of the proposed measures to offer a deeper insight into models' behavior and their impact on real applications, which benefit both data mining researchers and practitioners.

Markov chain splitting methods in structural reliability integral estimation

Monte Carlo simulation methods involving splitting of Markov chains have been used in evaluation of multi-fold integrals in different application areas. We examine in this paper the performance of these methods in the context of evaluation of reliability integrals from the point of view of characterizing the sampling fluctuations. The methods discussed include the Au-Beck subset simulation, Holmes-Diaconis-Ross method, and generalized splitting algorithm. A few improvisations based on first order reliability method are suggested to select algorithmic parameters of the latter two methods. The bias and sampling variance of the alternative estimators are discussed. Also, an approximation to the sampling distribution of some of these estimators is obtained. Illustrative examples involving component and series system reliability analyses are presented with a view to bring out the relative merits of alternative methods. (C) 2015 Elsevier Ltd. All rights reserved.