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Showing 3 results for Reliability Index

Sung-Hoon An, Hunhee Cho, Ung-Kyun Lee,
Volume 9, Issue 1 (3-2011)
Abstract

In the early stages of a construction project, the reliability and accuracy of conceptual cost estimates are major concerns for clients and cost engineers. Previous studies applied scoring methods and established common rules or mathematical methods to assess the quality of cost estimates. However, those approaches have some limitations in adapting to real-world projects or require understanding of sophisticated statistical techniques. We propose a Conceptual Cost Estimate Reliability Index (CCERI), a simple, easy-to-use, and easy-to-understand tool that incorporates weights for 20 factors influencing the quality of conceptual cost estimates. The weights were obtained by eliciting experts’ experience and knowledge. Cost data from 71 building projects were used in the analysis and validation of the CCERI. The analysis reveals that a conceptual cost estimate with a CCERI score of less than 3000 has a high probability of exceeding 10% error, and such conceptual cost estimates are unlikely to be reliable. With the CCERI score, a decision maker or a client can recognize the reliability of the conceptual cost estimates and the score can thus support decision making using conceptual cost estimates. In addition, with the CCERI and the relative importance weights of factors affecting the conceptual cost estimates, the estimator can find ways to modify a conceptual cost estimate and reestimate it. These alternatives can decrease the risk in the conceptual estimated cost and assist in the successful management of a construction project.
R. Jamshidi Chenari, P. Pishgah ,
Volume 12, Issue 2 (4-2014)
Abstract

In this technical note, a methodology is introduced for reliability calculation of consolidation settlement based on cone penetration test (CPT) data. The present study considers inherent soil variability which influences consolidation settlements results. To proceed reliability analysis, the measured data of a sample corrected cone tip resistance (􀝍􀯧) is detrended using a quadratic trend and the residuals are assumed to be lognormally distributed random field. Realizations of 􀝍􀯧 is generated by using spatial variability of residuals including standard deviation and the scale of fluctuation. The quadratic trend and the generated residuals are then combined to correlate shear and bulk modulus as input consolidation properties for coupled analysis and subsequently consolidation settlement was calculated by using finite difference method adopted in Monte Carlo simulations. The results of reliability analysis are presented describing the range of possible settlements by considering characteristics of uncertainties involved at the particular site. Number of realizations rendering settlements smaller than the allowable settlement must be such that guarantee proper performance or acceptable reliability index.
Khaled Farah, Mounir Ltifi, Tarek Abichou, Hedi Hassis,
Volume 12, Issue 3 (7-2014)
Abstract

The purpose of this study is to compare the results of different probabilistic methods such as the perturbation method, Stochastic Finite Element Method (SFEM) and Monte Carlo Method. These methods were used to study the convergence of direct approach for slope stability analysis and are developed for a linear soil behavior. In this study, two dimensional random fields are used and both the First Order Reliability Method (FORM) and Limited Step Length Iteration Method (LSLIM) have been adopted to evaluate the reliability index. The study found that the perturbation method of the second order is easy to apply using the field’s theory because accuracy is reached even with different coefficients of variation of input variables, while the spectral finite element method yields accurate results only for high levels of solution development.

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