Deepthi Mary Dilip

System Reliability Analysis of Flexible Pavements

The uncertainty in material properties and traffic characterization in the design of flexible pavements has led to significant efforts in recent years to incorporate reliability methods and probabilistic design procedures for the design, rehabilitation, and maintenance of pavements. In the mechanistic-empirical (ME) design of pavements, despite the fact that there are multiple failure modes, the design criteria applied in the majority of analytical pavement design methods guard only against fatigue cracking and subgrade rutting, which are usually considered as independent failure events. This study carries out the reliability analysis for a flexible pavement section for these failure criteria based on the first-order reliability method (FORM) and the second-order reliability method (SORM) techniques and the crude Monte Carlo simulation. Through a sensitivity analysis, the most critical parameter affecting the design reliability for both fatigue and rutting failure criteria was identified as the surface layer thickness. However, reliability analysis in pavement design is most useful if it can be efficiently and accurately applied to components of pavement design and the combination of these components in an overall system analysis. The study shows that for the pavement section considered, there is a high degree of dependence between the two failure modes, and demonstrates that the probability of simultaneous occurrence of failures can be almost as high as the probability of component failures. Thus, the need to consider the system reliability in the pavement analysis is highlighted, and the study indicates that the improvement of pavement performance should be tackled in the light of reducing this undesirable event of simultaneous failure and not merely the consideration of the more critical failure mode. Furthermore, this probability of simultaneous occurrence of failures is seen to increase considerably with small increments in the mean traffic loads, which also results in wider system reliability bounds. The study also advocates the use of narrow bounds to the probability of failure, which provides a better estimate of the probability of failure, as validated from the results obtained from Monte Carlo simulation (MCS).

Influence of Spatial Variability on Pavement Responses Using Latin Hypercube Sampling on Two-Dimensional Random Fields

Although uncertainties in material properties have been addressed in the design of flexible pavements, most current modeling techniques assume that pavement layers are homogeneous. The paper addresses the influence of the spatial variability of the resilient moduli of pavement layers by evaluating the effect of the variance and correlation length on the pavement responses to loading. The integration of the spatially varying log-normal random field with the finite-difference method has been achieved through an exponential autocorrelation function. The variation in the correlation length was found to have a marginal effect on the mean values of the critical strains and a noticeable effect on the standard deviation which decreases with decreases in correlation length. This reduction in the variance arises because of the spatial averaging phenomenon over the softer and stiffer zones generated because of spatial variability. The increase in the mean value of critical strains with decreasing correlation length, although minor, illustrates that pavement performance is adversely affected by the presence of spatially varying layers. The study also confirmed that the higher the variability in the pavement layer moduli, introduced through a higher value of coefficient of variation (COV), the higher the variability in the pavement response. The study concludes that ignoring spatial variability by modeling the pavement layers as homogeneous that have very short correlation lengths can result in the underestimation of the critical strains and thus an inaccurate assessment of the pavement performance.

Time-dependent reliability analysis for radionuclide migration in groundwater in near surface disposal facility using the enhanced Monte Carlo method

ABSTRACT In the present study, reliability analysis of near surface disposal facility is performed, by assessing the probability of sequential failure of the multi barrier system using the contaminant transport model. The concentration and dose rate of the radionuclide evolve with time hence there is a need for time dependent reliability analysis. Due to the low values of expected probabilities of failure, an enhanced Monte Carlo (EMC) method and Subset simulation is employed. The Result of the analysis show that, the EMC method is useful to evaluate the probability of failure associated with the barrier system which has low probability of failure.

Influence of Anisotropy on Pavement Responses Using Adaptive Sparse Polynomial Chaos Expansion

Modeling the spatial variability that exists in pavement systems can be conveniently represented by means of random fields; in this study, a probabilistic analysis that considers the spatial variability, including the anisotropic nature of the pavement layer properties, is presented. The integration of the spatially varying log-normal random fields into a linear-elastic finite difference analysis has been achieved through the expansion optimal linear estimation method. For the estimation of the critical pavement responses, metamodels based on polynomial chaos expansion (PCE) are developed to replace the computationally expensive finite-difference model. The sparse polynomial chaos expansion based on an adaptive regression-based algorithm, and enhanced by the combined use of the global sensitivity analysis (GSA) is used, with significant savings in computational effort. The effect of anisotropy in each layer on the pavement responses was studied separately, and an effort is made to identify the pavement layer wherein the introduction of anisotropic characteristics results in the most significant impact on the critical strains. It is observed that the anisotropy in the base layer has a significant but diverse effect on both critical strains. While the compressive strain tends to be considerably higher than that observed for the isotropic section, the tensile strains show a decrease in the mean value with the introduction of base-layer anisotropy. Furthermore, asphalt-layer anisotropy also tends to decrease the critical tensile strain while having little effect on the critical compressive strain

Time-Dependent Reliability Analysis of the Contaminant Migration of Radioactive Waste in Groundwater

Nuclear power is an efficient source of power generation with low carbon emissions. But one of the serious problems encountered in the nuclear industry is the development of proper disposal facilities for radioactive waste. The safety of a waste disposal facility depends on the efficiency of its design against contaminant migration. The transport mechanisms of the contaminant are complex processes which involves advection, diffusion and dispersion considering radioactive decay. The heterogeneity of the aquifers and the existence of uncertainty (data and model uncertainty) affect greatly the predictive ability of groundwater flow and contaminant transport models. As a result, in a complex structural system like a near surface disposal facility (NSDF) where low and intermediate level wastes are disposed, the amount of radionuclide released into groundwater post closure is a major concern for the design. It is essential to know the probability of the concentration of radionuclide in the drinking water pathway exceeding the permissible value (probability of failure). Further, the long timescales considered in NSDF are a key feature making treatment of uncertainties more challenging and since the parameters influencing the contaminant process evolve in time, there is a need for a time dependent reliability analysis in this regard. In the present study, the contaminant migration is modelled as a convolution of the repository failure and the transport of the contaminant to groundwater through advection and dispersion. The uncertainties are modelled by considering variability in parameters and the time dependent probability of failure is estimated using Monte Carlo simulations.

Methodology for Global Sensitivity Analysis of Flexible Pavements in a Bayesian Back-Analysis Framework

AbstractProbabilistic sensitivity analysis is a crucial tool in the uncertainty analysis of systems, which allows the understanding of how the uncertainty in the output response can be apportioned to different sources of uncertainty in the input parameters. Sobol’s method is a widely accepted global sensitivity analysis (GSA) technique that has been applied to rank the input design parameters, based on their respective impact on the response randomness. Although this variance-based technique is highly efficient when the design parameters are independent, the estimation of Sobol indices in the presence of correlation has not been sufficiently documented. This paper addresses this shortcoming through the development of a generalized method for GSA in the Bayesian back-analysis framework, in which the Kullback-Leibler (K-L) entropy measure serves as the measure of sensitivity. The methodology has been explored in the context of design of flexible pavements in the mechanistic-empirical (M-E) framework, in whi...