Probability Of Unsatisfactory Performance Research Articles

Extending Normalized Gaussian Adaptive Hyperspherical Integration for Fast Computation of Accurate Unsatisfactory Performance Probability for Hydraulic Retaining Structures

AbstractThe probability of unsatisfactory performance (PUP) is taken into consideration during the design of the US Army Corps of Engineers hydraulic structures retaining earth. These structures po...

A framework for the use of reliability methods in deep urban excavations analysis

Deep excavations in urban areas impose deformation to adjacent structures; hence the reliability of deformation analysis for the real deep excavation projects is very important to be assessed. In this study a framework is presented for the use of reliability methods in deformation analysis of deep urban excavations. The suggested framework is applied for 5 real deep excavation projects implemented during last 10 years. All studied cases were recognized as projects of high importance in urban areas, and were monitored during the excavation process. A non-probabilistic reliability analysis procedure, Random set method, in combination with finite element numerical modeling is applied to obtain the probability of unsatisfactory performance for each case. The reliability analysis results are confirmed by field observations and measurements. Typical results for the probability of analytical deformations exceeding the acceptable values along with the site observations and measured displacements for 5 real deep excavation projects show that the reliability analysis could be a beneficial tool for designer. It is concluded that applying the suggested framework in the design stage of deep excavation projects may lead to design more appropriate systems compared to common deterministic design methods.

The influence of experimental and sampling uncertainties on the probability of unsatisfactory performance in geotechnical applications

The influence of experimental and sampling uncertainties on the probability of unsatisfactory performance in geotechnical applications

Probabilistic Modeling of Dynamic Modulus Master Curves for Hot-Mix Asphalt Mixtures

Since the introduction of the dynamic modulus E* concept in the recent Mechanistic–Empirical Pavement Design Guide, there has been considerable interest in establishing reliable prediction models for E*. An investigation of the effectiveness of commonly used predictive models shows that E* predictions exhibit significant scatter around the measured values, with percentage of errors reaching about 6200%. A need exists for characterizing the uncertainties that are inherent in E* to serve as input to any future robust reliability analysis that aims at properly determining the probability of unsatisfactory performance of asphalt pavement systems. The primary objective of this study was to present a probabilistic model that would allow the user to determine a priori probability distribution for E* given knowledge about temperature and frequency. The seven-parameter model was based on the sigmoidal function and the shift factor that related reduced frequency to real frequency and temperature. The model was calibrated on the basis of a well-known published database that included 7,400 laboratory measurements of E* for 346 asphalt mixes. Monte Carlo simulations were used to propagate the uncertainties in the seven model parameters and determine realistic estimates of the mean, coefficient of variation, and probability distribution of E* at different frequencies and temperatures. Results showed that E* could be modeled by using a lognormal distribution with a mean that was estimated from the mean values of the parameters and a coefficient of variation that varied from a minimum of 0.55 for high values of reduced frequency to a maximum of 1.55 for lower values of reduced frequency.

Lodalen slide: a probabilistic assessment

Conventional slope practice, based on the deterministic factor of safety, cannot address the uncertainty in the input parameters of slope analyses in any explicit way. It relies entirely on the subjective judgment of the designer, which varies substantially among geotechnical engineers. Probabilistic techniques are powerful tools that can be used to quantify and incorporate uncertainty into slope analysis and design. A probabilistic slope analysis methodology based on Monte Carlo simulation using Microsoft® Excel and @Risk software is applied to investigate the Lodalen slide that occurred in Norway in 1954. Starting with field and laboratory data, the study demonstrates the techniques used in quantifying the uncertainties in soil properties and pore-water pressure, conducting a probabilistic assessment, and estimating the probability of unsatisfactory performance. The probability of unsatisfactory performance of the Lodalen slope is estimated to be 0.70, indicating that failure was imminent. The inclination of the Lodalen slope is then flattened, hypothetically, to different angles and the relationships between the slope angle, the factor of safety, and the probability of unsatisfactory performance are investigated.Key words: probabilistic analysis, slope stability, Monte Carlo simulation, spatial variability, Lodalen slide.

Probabilistic assessment of stability of a cut slope in residual soil

A probabilistic slope analysis methodology based on Monte Carlo simulation using Microsoft Excel and @Risk software is applied to investigate the failure of the Shek Kip Mei cut in Hong Kong. The study demonstrates the techniques used in quantifying uncertainties in shear strength of granitic soils based on a large database of triaxial tests. Probabilistic back-analyses of the failure are applied to estimate the probability distribution of the pore water pressure. Using the back-calculated pore pressure, the inclination of the Shek Kip Mei slope is redesigned to a flatter inclination, and the probability of unsatisfactory performance and reliability index are estimated.

Reply to the discussion by J.M. Duncan, M. Navin, and T.F. Wolff on "Probabilistic slope stability analysis for practice"

El-Ramly et al. The authors wish to thank the discussers for their interest in the paper and their valuable comments. Our response takes up the important issues raised and provides more details of our methodology. The response considers the four issues raised in their original sequence. Variance reduction due to spatial averaging Since the performance of earth slopes is controlled, in most cases, by the average material properties and pore pressures over the area of the slip surface, it is logical to consider the variability of the average values (rather than the variability between discrete points) in any probabilistic slope assessment. As demonstrated in the paper (El-Ramly et al. 2002), the variance of the spatial average of a property over an area or volume can be significantly less than that of the measured data. While this concept has been recognized for some time (Anderson et al. 1984; Li and Lumb 1987; Baecher 1987), for simplicity, several studies have used the variance of measured data without any reduction. The underlying assumption is that the operational shear strength, for example, controlling the stability of the slope is constant at all locations within the domain of the problem and can assume any value within the measured minimum–maximum range. There are two main consequences of this assumption. First, the probability of unsatisfactory performance can be grossly overestimated and second, the probability of unsatisfactory performance becomes detached from the scale at which failure takes place. To illustrate the above points, El-Ramly (2001) analysed a hypothetical slope configuration, Fig. D1, using soil data from an actual failure of the cut slope behind housing block

Reliability Assessment of Pile-Supported Structural Systems

A performance-mode (PM)-based evaluation procedure is proposed to determine the probability of unsatisfactory performance (UP) for two-dimensional (2-D) and three-dimensional (3-D) systems involving pile-supported dams. This methodology includes ductile behavior of individual piles for the strength mode. It also examines the 3-D effects on the bounds for the series-parallel system. The procedure is an extension of the reliability method presented to compute the system probability of UP of pile-supported structures using brittle-element behavior. Lateral and vertical deflections are the limit states of the pile cap that provide the system-level probability of UP for the serviceability limit state. The element-level probability of UP for strength and system-level probability of UP for serviceability are combined to evaluate the bounds for the load-case specific system probability of UP. Finally, the overall system probability of UP is evaluated considering the contributions of all load cases combined. The procedure is illustrated with examples, and the bounds for the system probability of UP are evaluated. The results indicate that the bounds of probability of UP for the example are narrower for ductile behavior than for brittle behavior. A 3-D problem correctly recognizes a number of significant PM that are absent in the 2-D approximation.