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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.

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Since the 1990’s, network reliability has been considered as a new index for evaluating transportation networks under uncertainty. A large number of studies have been revealed in the literature in this field, which are mostly dedicated to developing relevant measures that can be utilized for the evaluation of vulnerable networks under different sources of uncertainty, such as daily traffic flow fluctuations, natural disasters, weather conditions, and so fourth. This paper addresses the resource allocation problem in vulnerable transportation networks, in which multiple performance reliability measures should be met at their desired levels, while the overall cost of upgrading links’ performances should be minimized simultaneously. For this purpose, a new approach has been considered to formulate the two well-known performance measures, connectivity and capacity reliability, along with their application in a bi-objective nonlinear mixed integer goal programming model. In order to take into account the uncertain conditions of supply, links’ capacities have been assumed to be random variables and follow normal distribution functions. A computationally efficient method has been developed that allows calculating the network-wise performance indices simply by means of a set of functions of links’ performance reliabilities. Using this approach, as the performance reliability of links are themselves functions of the random links’ capacities, they can be simply calculated through numerical integration. To achieve desirable levels for both connectivity reliability and capacity reliability (as network-wise performance reliability measures) two distinct objectives have been considered. One of the objectives seeks to maximize each of the measures regardless of what is happening to the other objective function which minimizes the budget. Since optimization models with two conflicting objectives cannot be solved directly, the well-known goal attainment multi-objective decision-making (MODM) approach has been adapted to formulate the model as a single objective model. Then the resultant single objective model has been solved through the generalized gradient method, which is a straightforward solution algorithm coded in existing commercial software such as MATLAB programming software. To show the applicability of the proposed model, numerical results are provided for a simple network. Also, to show the sensitiveness of the model to decision maker’s direction weights, the results of sensitivity analysis are presented..

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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.