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In the last decade, the state departments of transportation in the US have experienced an increase in their construction projects, while the level of their in-house inspection staff and resources has either remained the same or declined. Previous studies have found that one strategy that may reduce the inspection workload is prioritizing construction activities for inspection. However, reducing the number of inspections also has its risks, such as functional failures and reduced design life. Thus, available inspection resources should be allocated to the activities with significant risk consequences if inspection is reduced. The objective of this paper is to develop a risk-based inspection protocol to facilitate efficient allocation of available inspection resources to minimize the risks associated with reduced inspection. First, the risk consequences associated with reduced inspection are identified for various construction activities linked to transportation projects. Based on data collected from 23 state departments of transportation, 58 engineers and inspectors from the Indiana Department of Transportation and 20 inspection consultants in the Midwest, the subjective perceived probabilities associated with the occurrence of each risk consequence are encoded by using fuzzy analysis, from which the risk impacts due to reduced inspection are obtained. The construction activities are prioritized based on the risk impacts associated with reduced inspection into five priority levels. The greater the risk impacts are as a result of reduced inspection, the higher the priority would be for inspection of that activity. Thus, the proposed protocol can be used for resource allocation based on risk impacts. The proposed list of prioritized construction activities can assist project and program managers in state departments of transportation in better allocating their limited inspection resources while reducing the risks due to reduced inspection. [ABSTRACT FROM AUTHOR]

A model for project budgeting and scheduling with uncertainty is developed. The traditional critical-path method (CPM) misleads because there are few, if any, real-life deterministic situations for which CPM is a great match; program evaluation and review technique (PERT) has been seen to have its problems, too (e.g., merge bias, unavailability of data, difficulty of implementation by practitioners). A dual focus on the distributions of the possible errors in the time and cost estimates and on the reliability of the estimates used as planned values suggests an approach for developing reliable schedules and budgets with buffers for time and cost. This method for budgeting and scheduling is executed through either simulation or a simple analytical approximation. The dynamic buffers provide much-needed flexibility, accounting for the errors in cost and duration estimates associated with planning any real project, thus providing a realistic, practical, and dynamic approach to planning and scheduling. [ABSTRACT FROM AUTHOR]

The statistical characteristics of cost overruns experienced from contract award in 276 Australian construction and engineering projects were analyzed. The skewness and kurtosis values of the cost overruns are computed to determine if the empirical distribution of the data follows a normal distribution. The Kolmogorov-Smirnov, Anderson-Darling, and chi-squared nonparametric tests are used to determine the goodness of fit of the selected probability distributions. A three-parameter Frechet probability function is found to describe the behavior of cost overruns and provide the best overall distribution fit. The Frechet distribution is then used to calculate the probability of a cost overrun being experienced. The statistical characteristics of contract size and cost overruns were also analyzed. The Cauchy (), Wakeby (A$1 to 10 million, ) and four-parameter Burr (A$11 to 50 million) tests were found to provide the best distribution fits and used to calculate cost overrun probabilities by contract size. Ascertaining the best fit probability distribution from an empirical distribution at contract award can produce realistic probabilities of cost overruns, which should then be incorporated into a construction cost contingency. [ABSTRACT FROM AUTHOR]

Acceptance procedures are usually defined in terms of the fraction of elements that fall outside certain specification limits, such as the data dispersion around a central tendency. In contrast, design methods usually consider measures of a central tendency or location (i.e., mean) or, occasionally, of a central tendency together with measures of data dispersion. Price adjustments can be derived from data analysis using empirical models or algorithms based on life cycle cost analysis. As a consequence, there is no common logical framework for designing, analyzing, testing, and accepting road and airport pavements. This study analyzes the dependence of pay adjustment on hot mix asphalt air void characteristics using several acceptance procedures. The results demonstrate that the transition from location-based to dispersion-based systems depends on the probability density function describing a given quality characteristic. The drawbacks of several methods, brought to light in the analysis and discussion presented here, informed a new model in which both location and dispersion indicators are considered. The model was applied to a case study, and practical applications are outlined. [ABSTRACT FROM AUTHOR]

The low-bid method, typically used for competitive bidding in the United States, may result in a contract with a firm that submits either accidentally or deliberately an unrealistically low-bid price. Such an occurrence hurts both the owner and the contractor by promoting disputes, increased costs, and schedule delays. To address this problem, other countries have adopted bidding methods based on the average of the bids submitted. One such approach is the below-average method where the winning bid is closest to but below the average of all bids. A competitive bidding model for the below-average-bid method is presented and its merits relative to the average-bid method and the low-bid method are explored. The below-average-bid process is investigated analytically and through Monte Carlo simulation. The results of bidding models for the below-average, the average, and the low-bid methods are presented in four easy-to-use nomograms which allow contractors to determine the optimal lump-sum bid price for each method without the need for complicated analysis. A comparison of the three methods provides information and insights to help owners with the difficult choice of a suitable bidding method for the project at hand. [ABSTRACT FROM AUTHOR]

This paper presents a mathematical model for calculating the budgetary impact of increasing the required confidence level in a probabilistic risk assessment for a portfolio of projects. The model provides a rational approach for establishing a probabilistic budget for an individual project in such a way that the budget for a portfolio of projects will meet a required confidence level. The use of probabilistic risk assessment in major infrastructure projects is increasing to cope with major cost overruns and schedule delays. The outcome of the probabilistic risk assessment is often a distribution for project costs. The question is at what level of confidence (i.e., the probability that budget would be sufficient given the cost distribution) should be used for establishing the budget. The proposed method looks at a portfolio of such projects being funded by the same owner. The owner can establish a target probability with respect to its annual budget. The model can help the owner establish confidence level for individual projects and also examine the effect of changing the confidence level of the portfolio budget on the budget and the confidence level of individual projects. The paper is relevant to practitioners because it provides a methodology for establishing confidence levels by the owner agencies in the emerging field of cost risk assessment for infrastructure projects. A numerical example is provided using actual transit project data to demonstrate the model application. [ABSTRACT FROM AUTHOR]

The duration of a construction project is a key factor to consider before starting a new project, as it can determine project success or failure. Despite the high level of uncertainty and risk involved in construction, current construction planning relies on traditional deterministic scheduling methods that cannot clearly ascertain the level of uncertainty involved in a project. This, subsequently, can prolong a project’s duration, particularly when that project is high-rise structural work, which is not yet a common project type in Korea. Indeed, among construction processes, structural work is notable, as it is basically performed outdoors. Thus, no matter how precisely a schedule is developed, such projects can easily fail due to unexpected events that are beyond the planner’s control, such as changes in weather conditions. Therefore, in this study, to cope with the uncertainties involved in high-rise building projects, a probabilistic duration estimation model is developed in which both weather conditions and work cycle time for unit work are considered to predict structural work duration. According to the proposed estimation model, weather variables are divided into two types: weather conditions that result in nonworking days and weather conditions that result in work productivity rate (WPR) change. Obtained from actual previous data, the WPR is used with relevant nonworking day weather conditions to modify the actual number of working days per calendar days. Furthermore, on the basis of previous research results, the cycle time of the unit work area is assumed to follow the β probability distribution function. Thus, the probabilistic duration model is valid for 95% probability. Finally, a case study is conducted that confirms the model can be practically used to estimate more reliable and applicable probabilistic durations of structural work. Indeed, this model can assist schedulers and site workers by alerting them, at the beginning of a project, to project uncertainties that specifically pertain to structural work and the weather. Thus, the proposed model can enable personnel to easily amend, and increase the reliability of, the construction schedule at hand. [ABSTRACT FROM AUTHOR]

This study introduces a new probabilistic project control concept to assure an acceptable forecast of final project performance, in terms of not exceeding planned budget and schedule risk levels. This concept consists in the implementation of performance control limit curves for both actual cost and elapsed time, obtained with a probabilistic approach and a graphical representation referred to as Stochastic S curves (SS curves). In order to facilitate the project control process, control limit curves can be used to display and evaluate actual project performance status without the need of actualizing at completion performance forecasts. Three different approaches (quality, benchmarking, and incremental variance) are proposed in this paper for obtaining the project performance control limit curves. In order to find the control limit curve definition with more conservative acceptable performance variations, these approaches are tested in an example project. A further managerial advantage is found in the recommended approach, as it allows monitoring the use of both cost and scheduling contingencies, along the project execution. [ABSTRACT FROM AUTHOR]

This paper describes a stochastic simulation-based scheduling system (S3) that: (1) integrates the deterministic critical path method (CPM), the probabilistic program evaluation and review technique (PERT), and the stochastic discrete event simulation (DES) approaches into a single system and lets the scheduler make an informed decision as to which method is better suited to the company’s risk-taking culture; (2) automatically determines the minimum number of simulation runs in DES mode and therefore optimizes the simulation process; and (3) provides a terminal method that tests the statistical significance of the differences between simulations, hence eliminating outliers and therefore increasing the accuracy of the DES process. The system is based on an earlier version of the system called stochastic project scheduling simulation and makes use of all the capabilities of this system. The study is of value to practitioners because S3 produces a realistic prediction of the probability of completing a project in a specified time. The study is also of relevance to researchers in that it allows researchers to compare the outcome of CPM, PERT, and DES under different conditions such as different variability or skewness in the activity duration data, the configuration of the network, or the distribution of the activity durations. [ABSTRACT FROM AUTHOR]