Statistical Analysis on the Cost and Duration of Public Building Projects

A probabilistic model is proposed to predict the risk effects on time and cost of public building projects. The research goal is to utilize a real history data in estimating project cost and duration. The model results can be used to adjust floats and budgets of the planning schedule before project commencement. Statistical regression models and sample tests are developed using real data of 113 public projects. The model outputs can be used by project managers in the planning phase to validate the schedule critical path time and project budget. The comparison of means analysis for project cost and time performance indicated that the sample projects tend to finish over budget and almost on schedule. Regression models were developed to model project cost and time. The regression analysis showed that the project budgeted cost and planned project duration provide a good basis for estimating the cost and duration. The regression model results were validated by estimating the prediction error in percent and through conducting out-of-sample tests. In conclusion, the models were validated at a probability of 95%, at which the proposed models predict the project cost and duration at an error margin of ±0.035% of the actual cost and time.     

Multiple Simulation Analysis for Probabilistic Cost and Schedule Integration

One of the major goals of the construction industry today is the quantification and minimization of the risk associated with construction engineering performance. When specifically considering the planning of construction projects, one way to control risk is through the development of reliable project cost estimates and schedules. Two techniques available for achieving this goal are range estimating and probabilistic scheduling. This paper looks at the integration of these techniques as a means of further controlling the risk inherent in the undertaking of construction projects. Least-squares linear regression is first considered as a means of relating the data obtained from the application of these techniques. However, because of various limitations, the application of linear regression was not considered the most appropriate means of relating the results of range estimating and probabilistic scheduling. Integration of these techniques was, therefore, achieved through the development of a new procedure called the multiple simulation analysis technique. This new procedure combines the results of range estimating and probabilistic scheduling in order to quantify the relationship existing between them. Having the ability to accurately quantify this relationship enables the selection of high percentile level values for the project cost estimate and schedule simultaneously.     

Probabilistic Approach for Budgeting in Portfolio of Projects

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.     

Probabilistic Model for Cost Contingency

This paper proposes a probabilistic model for the calculation of project cost contingency by considering the expected number of changes and the average cost of change. The model assumes a Poisson arrival pattern for change orders and independent random variables for various change orders. The probability of cost overrun for a given contingency level is calculated. Typical input values to the model are estimated by reviewing several U.S. Army Corps of Engineers project logs, and numerical values of contingency are calculated and presented. The effect of various parameters on the contingency is discussed in detail.     

Probabilistic Control of Project Performance Using Control Limit Curves

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.     

Probabilistic Optimal-Cost Scheduling

During the planning and execution of construction projects, it often becomes necessary to shorten the duration of the project. A widely used technique for reducing the duration of a project is commonly referred to as least-cost scheduling. This procedure is based on deterministically arriving at the shortest project duration for the minimum cost possible. There is, however, one major problem with the typical application of this technique. It does not address the variability inherent in the duration and cost of the project activities. Thus, the resulting compressed schedule value cannot be applied with any stated level of statistical confidence. This paper presents a new procedure that addresses some of the major shortcomings of least-cost scheduling. It does so by accounting for the variability inherent in the duration and cost of the scheduled activities by simultaneously applying range estimating and probabilistic scheduling to the historical data. The resulting data set is then analyzed to provide a compressed schedule duration and cost estimate that have a higher overall confidence of being achieved.     

Stochastic Modeling for Pavement Warranty Cost Estimation

This paper presents a cost estimation model for long-term pavement warranties with multiple distress indicators. One application area for such warranties involves performance-based specifications (PBSs). In contrast to traditional approaches, PBS gives contractors the flexibility to select construction methods, materials, and even design. However, the contractors then must warrant the performance of their work for a specified period of time. Therefore, an accurate estimation of the risks associated with the warranty is a significant cost issue for any contractor to cover potential risks while still being competitive in bidding. Quantitative evaluation of the cost of risk incurred by the warranty has several difficulties. The deterioration of a highway project is a complex process, which is affected by pavement structure, material, traffic load, and weather conditions. Based on a probabilistic risk analysis of failures of performance indicators, the resulting model can estimate the warranty cost at a detailed level. The application of the model has been demonstrated via a numerical case study using long-term pavement performance data.     

Managing Construction Projects Using the Advanced Programmatic Risk Analysis and Management Model

Risk management is an important part of construction management, yet the risk-based decision support tools available to construction managers fail to adequately address risks relating to cost, schedule, and quality together in a coherent framework. This paper demonstrates the usefulness of the Advanced Programmatic Risk Analysis and Management Model (APRAM) originally developed for the aerospace industry, for managing schedule, cost, and quality risks in the construction industry. The usefulness of APRAM for construction projects is demonstrated by implementing APRAM for an example based on an actual building construction project and comparing the results with other risk analysis techniques. The results show that APRAM simultaneously addresses cost, schedule, and quality risk together in a coherent, probabilistic framework that provides the information needed to support decision making in allocating scarce project resources.     

Probabilistic Duration Estimation Model for High-Rise Structural Work

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.     

Fuzzy Numbers in Cost Range Estimating

Range estimating is a simple form of simulating a project estimate by breaking the project into work packages and approximating the variables in each package using statistical distributions. This paper explores an alternate approach to range estimating that is grounded in fuzzy set theory. The approach addresses two shortcomings of Monte Carlo simulation. The first is related to the analytical difficulty associated with fitting statistical distributions to subjective data, and the second relates to the required number of simulation runs to establish a meaningful estimate of a given parameter at the end of the simulation. For applications in cost estimating, the paper demonstrates that comparable results to Monte Carlo simulation can be achieved using the fuzzy set theory approach. It presents a methodology for extracting fuzzy numbers from experts and processing the information in fuzzy range estimating analysis. It is of relevance to industry and practitioners as it provides an approach to range estimating that more closely resembles the way in which experts express themselves, making it practically easy to apply an approach.     

SERC—A Model for Estimating Construction Inputs

One of the main problems in the process of design and management of construction projects is obtaining accurate information for preliminary estimates. This information is crucial for the development of integrated systems for construction management because of the relationship between construction input data and subjects such as estimating, cost control, scheduling, resource management, etc. Existing methods for estimating input that originated in industrial engineering are inadequate for the unique conditions of the construction industry. The model described in this paper applies statistical analysis of data from past projects, and enables the user to estimate the data needed for the construction of a new project. The model is based on the following components: Project items and their quantities; inputs needed to produce those items; and factors that affect inputs of a specific project. The model equation was calculated using multiple regression techniques. The paper concludes with a case study of a construction input configuration for a concrete structure.     

Construction Management Control with Microcomputers

The development of microcomputers is changing the nature of common construction management functions. The estimating function can now be used with greater ease to simultaneously evaluate the time required to perform different project activities and keep cost data related to cost control. While this procedure was time consuming for manual computation, it could be done in a reasonable amount of time when using microcomputers. This paper discusses the formation of a microcomputer system which can perform the functions of estimating, cost control, and scheduling at the same time. The procedure makes use of productivity of a crew of particular size, the materials and the equipment needed, to generate time data related to scheduling and cost data related to estimating and cost control. The software needed for implementing the system is an electronic spreadsheet program, a data base management program and a time management program available for most microcomputers at a relatively inexpensive cost.     

Probabilistic Monitoring of Project Performance Using SS-Curves

The purpose of this study was to develop a new concept of project control. This new concept uses stochastic S-curves (SS-Curves) as an alternative to using the deterministic S-curve technique commonly employed in professional practice. SS-curves are developed by determining the activity level variability in cost and duration. Simulation is the recommended approach for obtaining SS-curves, similar to the way that stochastic schedules are currently developed. SS-curves provide probability distributions for expected cost and duration for a given percentage of work completed. Monitoring project performance is performed by comparing the most likely budget and duration values, obtained from respective probability distributions for actual progress, with the project's actual data and cumulative cost. By using this method, an evaluation of actual project performance can be developed that appropriately considers the natural variability of construction costs and duration, rather than utilizing only one possible deterministic outcome. Given the probabilistic characteristics of SS-curves, additional benefits are presented that enable a more comprehensive project control methodology.     

Simplified Spreadsheet Solutions.?I: Subcontractor Information System

This paper presents a subcontractor information system (SIS) to support the estimating and project control functions of subcontractors and small∕medium-size contractors. For the proposed SIS to be simple and practical, it was developed in a spreadsheet program designed to maintain information related to resources and projects and to generate important business reports. Resource data are stored in six worksheets for labor, equipment, crews, material, subcontractors, and alternative methods of construction for various tasks. In addition, a separate worksheet is designed for each project to be used for estimating and control purposes. The latter worksheet allows the user to specify the work breakdown structure and optional methods for construction. As such, it represents a transparent estimating model that allows for quick what-if analysis regarding time and cost. In addition, the reporting worksheet provides information related to time, cost, and resource use at the individual and the multiproject levels. In a companion paper, the use of the SIS as basis for overall schedule optimization is described.     

Fast and Accurate Risk Evaluation for Scheduling Large-Scale Construction Projects

This paper presents the development of a novel probabilistic scheduling model that enables fast and accurate risk evaluation for large-scale construction projects. The model is designed to overcome the limitations of existing probabilistic scheduling methods, including the inaccuracy of the program evaluation and review technique (PERT) and the long computational time of the Monte Carlo simulation method. The model consists of three main modules: PERT model; fast and accurate multivariate normal integral method; and a newly developed approximation method. The new approximation method is designed to focus the risk analysis on the most significant paths in the project network by identifying and removing insignificant paths that are either highly correlated or have high probability of completion time. The performance of the new model is analyzed using an application example. The results of this analysis illustrate that the new model was able to reduce the computational time for a large-scale construction project by more than 94% while keeping the error of its probability estimates to less than 3%, compared with Monte Carlo Simulation methods.     

Delay Time Analysis in Microtunneling Projects

Delay in microtunneling projects is a complex multivariate problem. Delay in microtunneling is defined as the nonworking time of a microtunneling project due to any reason other than scheduled stops. There are many reasons for delay such as mechanical failure of system components, leakage of hydraulic hoses, blockage of slurry pipes, and waiting time for excavated materials hauling equipment. Delay time increases the project duration and consequently the project cost. Delay data were collected from 35 microtunneling projects. Collected delay data were delay duration, delay reason, time, and location from the start to the stopping point. Five categories of delay causes were used in the analysis. Prediction of delay time will enhance the estimation accuracy of microtunneling project duration. A predictive model using a probabilistic approach was selected to represent the delay time. Based on data characteristics, a Weibull distribution was determined to best represent the overall delay duration in microtunneling projects. Using “regression with life data,” expected overall delay in a microtunneling project could be predicted as a function of driven length. The model will help contractors to estimate total project time with reasonable accuracy. Knowing the anticipated delay time will allow contractors to have a point of comparison for actual performance.     

Defining Triangular Probability Distributions from Historical Cost Data

During the development of an automated cost estimating system, several factors led to the selection of the triangular probability-density function to model historical construction costs. The triangular-density function is customarily used when function parameters are directly estimated by experts. A typical example is for estimating activity durations by identifying a minimum value, a most likely value, and a maximum value. These values are then used to construct triangular-density functions to represent uncertain activity durations. For this work, however, it was necessary to estimate parameters of the triangular-density function using historical cost data. A methodology was developed to generate test data and compare three methods of parameter estimation—maximum likelihood, moment matching, and least-squares curve-fitting techniques. It is concluded that optimized moment matching and least-squares techniques produce more accurate parameter estimates, while maximum likelihood estimation yields less accurate results. It is further concluded that the least-squares minimization method always performed as well as or better than the optimized moment matching technique and was therefore selected as the method of choice for the project.     

Labor Performance Analysis for Microtunneling Projects

Microtunneling projects have special process characteristics. The production time of microtunneling projects is needed for project estimating and scheduling. The production time has three basic components: (1) preparation times for pipe segments; (2) preparation times; and (3) delay times. Real project data were collected from 35 projects to predict the time needed for pipe segment preparation. A probabilistic distribution technique was used to predict the preparation times for pipe segments. Various numbers of probabilistic distributions were tested against the data. A log-logistic distribution was selected to represent the preparation time’s probability for a pipe in microtunneling projects. Preparation times for high, medium, and low performance contractors were predicted. By enhancing the estimation process of the microtunneling projects, contractors can use this paper in predicting the preparation time for pipe segments, which represents the crew (or crew’s) productivity.     

Validation and Application of Empirical Liquefaction Models

Empirical liquefaction models (ELMs) are the standard approach for predicting the occurrence of soil liquefaction. These models are typically based on in situ index tests, such as the standard penetration test (SPT) and cone penetration test (CPT), and are broadly classified as deterministic and probabilistic models. No objective and quantitative comparison of these models have been published. Similarly, no rigorous procedure has been published for choosing the threshold required for probabilistic models. This paper provides (1) a quantitative comparison of the predictive performance of ELMs; (2) a reproducible method for choosing the threshold that is needed to apply the probabilistic ELMs; and (3) an alternative deterministic and probabilistic ELM based on the machine learning algorithm, known as support vector machine (SVM). Deterministic and probabilistic ELMs have been developed for SPT and CPT data. For deterministic ELMs, we compare the “simplified procedure,” the Bayesian updating method, and the SVM models for both SPT and CPT data. For probabilistic ELMs, we compare the Bayesian updating method with the SVM models. We compare these different approaches within a quantitative validation framework. This framework includes validation metrics developed within the statistics and artificial intelligence fields that are not common in the geotechnical literature. We incorporate estimated costs associated with risk as well as with risk mitigation. We conclude that (1) the best performing ELM depends on the associated costs; (2) the unique costs associated with an individual project directly determine the optimal threshold for the probabilistic ELMs; and (3) the more recent ELMs only marginally improve prediction accuracy; thus, efforts should focus on improving data collection.