Cost-Effectiveness Evaluation of Warranty Pavement Projects

Warranty contracts are expected to enhance product quality and service life at lower life-cycle cost. Recognizing that such benefits could be offset by the generally higher costs of warranties, an assessment of the relative cost-effectiveness of warranty contracts compared to their traditional counterparts is currently of great interest. Using pavement data from Indiana, this paper evaluates the costs, effectiveness, and cost-effectiveness of warranty and traditional contracts. Effectiveness was measured in terms of pavement condition and treatment service life. The paper determined that the warranty contracts generally had higher agency costs but produced pavements with superior condition and service life, and lower construction periods and work-zone user costs, compared to their traditional counterparts. Over a 5-year period, the warranty contracts were found to be 27–30% less cost-effective than their traditional counterparts. Over the long term, the warranty contracts were found to be 70–90% more cost-effective on the basis of service life and 58–65% more cost-effective on the basis of both service life and pavement condition. The study results suggest that the higher long-term cost-effectiveness of warranty contracts is more perceptible when both cost and effectiveness are viewed over the entire life of the pavement treatment and when both agency and user cost are used in the cost analysis. The paper discusses certain aspects of such comparative evaluation studies that could lead to biased inferences and calls for careful screening of warranty and traditional contracts for such studies.     

Estimation of Cost Contingency for Air Force Construction Projects

Risk and associated cost overruns are critical problems for construction projects, yet the most common practice for dealing with them is the assignment of an arbitrary flat percentage of the construction budget as a contingency fund. Therefore, our goal was to identify significant variables that may influence, or serve as indicators of, potential cost overruns. We analyzed data from 203 Air Force construction projects over a full range of project types and scopes using multiple linear regression to develop a model to predict the amount of required contingency funds. The proposed model uses only data that would be available prior to the award of a construction contract. The variables in the model were categorized as project characteristics, design performance metrics, and contract award process influences. Based on the performance metric used, the model captures 44% of actual cost overruns versus the 20% captured by the current practice. Furthermore, application of the model reduces the average contingency budgeting error from 11.2 to only 0.3%.     

Modeling Cost Escalation in Large Infrastructure Projects

Cost overruns in large infrastructure projects have been commonplace in the past decades. Budgeting for cost escalation is a major issue in the planning phase of these projects. In this paper, we first review various methods of forecasting escalation factor and study the changes in construction costs in the past 25 years by analyzing movements of a cost index. We then introduce a system for modeling the escalation uncertainty in large multiyear construction projects. The system uses a Monte Carlo simulation approach and considers variability of project component durations and the uncertainty of escalation factor during the project lifetime and calculates the distribution for the cost. System application is demonstrated using a numerical example. The system can be used by planners and cost estimators for budgeting the effect of cost escalation in large projects with multiyear schedules.     

Spectral Analysis and Parametric Study of Stochastic Pavement Loads

This paper investigates the effects of vehicle parameters, speed, and surface roughness on the power spectral density (PSD) of stochastic pavement loads. Pavement surface roughness is modeled as a zero-mean stationary random field. A quarter-vehicle model is established to simulate the vibrations of heavy and passenger vehicles with typical parameters. Tire damping is also included in the consideration of stochastic pavement loads; this was assumed to be zero in many previous investigations. The PSD roughness proposed by the ISO is adopted in the simulation of the loads. An important indicator of the stochastic loads, the so-called energy cumulative distribution function, is introduced to describe the frequency distribution of load energy. The results show that passenger vehicles produce more high-frequency loads than heavy vehicles, while more of the loads generated by heavy vehicle are primarily distributed in the low-frequency region. It is also found that the effect of tire damping on stochastic pavement loads is not negligible especially if the loads of interest are concentrated in the high-frequency region. The results of the study may be useful in optimum design of vehicle suspensions and prediction of dynamic pavement response.     

Decision Tree Modeling Using Integrated Multilevel Stochastic Networks

Decision trees (DTs) have proven to be valuable tools for decision making. The common approach for using DTs is calculating the expected value (EV) based on single-number estimates, but the single-number EV method has limited the DTs’ real-life applications to a narrow scope of decision problems. This paper introduces the stochastic multilevel decision tree (MLDT) modeling approach, which is useful for analyzing decision problems characterized by uncertainty and complexity. The MLDT’s advantages are shown through a computer simulation program: the Decision Support Simulation System (DSSS). The DSSS allows users to model probabilistic linear graph networks and provides a hierarchical modeling method for modeling decision trees to present uncertainties more accurately. It consists of three modules: tree analysis networks (TANs), the shortest and longest path dynamic programming analysis network, and cost time analysis networks. The paper only discusses the TAN module by presenting the MLDT concept under the TAN of the DSSS computer application. The content of the paper includes the modeling approach, its advantages, and examples that can be used in modeling stochastic trees. The DT-DSSS was verified by conducting several tests and validated by using it extensively for undergraduate courses in civil engineering at the University of Calgary for the last two academic years.     

Evaluation of Construction Practices That Influence the Bond Strength at the Interface between Pavement Layers

This study investigated the influence of several construction practices on the bond strength at the interface between pavement layers. These practices included the surface treatment, curing time, residual application rate, and equipment tracking. Three tests were performed for estimating the bond strength between an existing hot mix asphalt (HMA) and a newly constructed HMA overlay, namely the Florida Dept. of Transportation shear tester, the University of Texas at El Paso pull off test, and the torque bond test. Testing involved a CSS-1 type emulsion as the tack coat. The results from the three tests were statistically analyzed. Generally, milling provided a significantly better bond at the interface between the existing surface and the new overlay. Curing time had a minimal effect on the bond strength. The results indicated that the absence of tack coat did not significantly affect the bond strength at the interface for the milled sections, whereas it severely decreased the strength for the nonmilled sections. The results also showed that increasing the residual rate of tack coat did not generally affect the bond strength at the interface.     

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.     

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.     

Integrating Neurofuzzy System with Conceptual Cost Estimation to Discover Cost-Related Knowledge from Residential Construction Projects

Cost estimation during early stage of a building construction project plays an important role for feasibility analysis in the planning and design phase. Traditional knowledge-based approaches suffer an essential difficulty due to resource price fluctuation in the market. This paper presents a hybrid method that integrates the principal items ratio estimation method with the adaptive neurofuzzy inference system for mining of cost estimation data. The proposed method provides exceptional capability for mining estimation knowledge that is difficult to be discovered by traditional knowledge-based approaches. A case study of residential building projects in China is conducted to demonstrate the proposed method. The testing results show that the proposed method does not only achieve high estimation accuracy, but also provide desirable features for estimators, such as explicit fuzzy decision rules and graphical presentations.     

Unified DSC Constitutive Model for Pavement Materials with Numerical Implementation

Need for unified and mechanistic constitutive models for pavement materials for evaluation of various distresses has been recognized; however, such models are not yet available. There have been efforts to develop unified models; however, they have been based usually on ad hoc combinations of models for special properties such as elastic, plastic, creep and fracture, often without appropriate connections to various coupled responses of bound and unbound materials, they may result and in a large number of parameters, often without physical meanings. The disturbed state concept (DSC) provides a modeling approach that includes various responses such as elastic, plastic, creep, microcracking and fracture, softening and healing under mechanical and environmental (thermal, moisture, etc.) within a single unified and coupled framework. A brief review is presented to identify the advantages of the DSC compared to other available models. The DSC has been validated and applied to a wide range of materials: geologic, asphalt, concrete, ceramic, metal alloys, and silicon. It allows for evaluation of various distresses such as permanent deformations (rutting), microcracking and fracture, reflection cracking, thermal cracking, and healing. The DSC is implemented in two- and three-dimensional finite-element (FE) procedures, which allow static, repetitive, and dynamic loads including elastic, plastic, creep, microcracking leading to fracture and failure. A number of examples are solved for various distresses considering flexible (asphalt) pavements; however, the DSC model is applicable to rigid (concrete) pavements also. It is felt that the DSC and the FE computer programs provide unique and novel approaches for pavement engineering. It is desirable to perform further research and applications including validation with respect to simulated and field behavior of pavements.     

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.     

Time Series Analysis of ENR Construction Cost Index

Every month, Engineering News-Record (ENR) publishes the construction cost index (CCI), which is a weighted aggregate index of the 20-city average prices of construction activities. Although CCI increases over the long term, it is subject to considerable short-term variations, which make it problematic for cost estimators to prepare accurate bids for contractors or engineering estimates for owner organizations. The ability to predict construction cost trends can result in more-accurate bids and avoid under- or overestimation. This paper summarizes and compares the applicability and predictability of various univariate time series approach for in-sample and out-of-sample forecastings of CCI. It is shown that the seasonal autoregressive integrated moving-average model is the most-accurate time series approach for in-sample forecasting of CCI, while the Holt-Winters exponential smoothing model is the most-accurate time series approach for out-of-sample forecasting of CCI. It is also shown that several time series models provide more-accurate out-of-sample forecasts than the ENR’s subject matter experts’ CCI forecast. Cost estimators can benefit from CCI forecasting by incorporating predicted price variations in their estimates and preparing more-accurate bids for contractors and budgets for owners. Owners and contractors can use CCI forecasting in reducing construction costs by better-timed project execution.     

Improving Cost Estimates of Construction Projects Using Phased Cost Factors

Central to cost-based competition is the capability to accurately predict the cost of delivering a project. Most literature on cost estimation focuses on specific estimation methods as generic techniques and little attention has been paid to the unique requirements at each project stage. This note attempts to identify the critical factors for effective estimation at various stages of typical construction projects. Drawing from organization control theory and cost estimating literature, this note develops a theoretical framework that identifies the critical factors for effective cost estimation during each project phase of a conventional construction project. The underlying logic is that as a cost estimating effort progresses, both task programmability and output measurability improve. As a result, control effort will shift from input-oriented control to a combination of output and behavior control.     

Stochastic Time–Cost Optimization Model Incorporating Fuzzy Sets Theory and Nonreplaceable Front

In a real construction project, the duration and cost of each activity could change dynamically as a result of many uncertain variables, such as weather, resource availability, productivity, etc. Managers/planners must take these uncertainties into account and provide an optimal balance of time and cost based on their own experience and knowledge. In this paper, fuzzy sets theory is applied to model the managers’ behavior in predicting time and cost pertinent to a specific option within an activity. Genetic algorithms are used as a searching mechanism to establish the optimal time–cost profiles under different risk levels. In addition, the nonreplaceable front concept is proposed to assist managers in recognizing promising solutions from numerous candidates on the Pareto front. Economic analysis skills, such as the utility theory and opportunity cost, are integrated into the new model to mimic the decision making process of human experts. A simple case study is used for testing the new model developed. In comparison with the previous models, the new model provides managers with greater flexibility to analyze their decisions in a more realistic manner. The results also indicate that greater robustness may be achieved by taking some risks. This research is relevant to both industry practitioners and researchers. By incorporating the concept of fuzzy sets, managers can represent the range of possible time–cost values as well as their associated degree of belief. The model presented in this paper can, therefore, support decision makers in analyzing their time–cost optimization decision in a more flexible and realistic manner. Many novel ideas have also been incorporated in this paper to benefit the research community. Examples of these include the use of fuzzy sets theory, nonreplaceable front concept, utility theory, opportunity cost, etc. With suitable modifications, these concepts can be applied to model to other similar optimization problems in construction.     

International Comparison of Cost for the Construction Sector: Purchasing Power Parity

The importance of the construction sector in national economies around the globe and the global nature of the industry require a prudent international comparison of construction costs. From the view of international construction ventures, cost comparisons have generally been accomplished using published currency exchange rates. Global organizations dealing with development aid and the comparison of the gross domestic product (GDP) of nations have used an approach that has its roots in established econometric theories. This approach is based on the Casselian purchasing power parity (PPP) doctrine that essentially conducts the comparison based on the local purchasing power of currencies, as opposed to exchange rates. The World Bank, which conducts the GDP comparison, uses the PPP-based approach to compare construction sector output. This paper provides an overview of the background and application of PPP and its use for international cost comparisons conducted for various nations. Methods currently used for construction cost comparisons are reviewed. A critical review of domestic construction cost comparison approaches is provided with the intent to identify the key differences between temporal and spatial comparisons. Case studies of construction cost factors are used to demonstrate the importance of PPP-based cost comparisons for construction economics.     

Preliminary Cost Estimation Model Using Case-Based Reasoning and Genetic Algorithms

This study proposes a preliminary cost estimation model using case-based reasoning (CBR) and genetic algorithm (GA). In measuring similarity and retrieving similar cases from a case base for minimum prediction error, it is a key process in determining the factors with the greatest weight among the attributes of cases in the case base. Previous approaches using experience, gradient search, fuzzy numbers, and analytic hierarchy process are limited in their provision of optimal solutions. This study therefore investigates a GA for weight generation and applies it to real project data. When compared to a conventional construction cost estimation model, the accuracy of the CBR- and GA-based construction cost estimation model was verified. It is expected that a more reliable construction cost estimation model could be designed in the early stages by using a weight estimation technique in the development of a construction cost estimation model.     

Stochastic Priority Model for Aggregate Blending

Aggregate blending models that incorporate the optimization of two objectives with their priority levels are presented. The two objectives include the minimization of the mean deviation (or mean absolute deviation) from the midpoint of specification limits and the minimization of the unit cost of the blend. The models are applicable to any number of aggregates and can be used to provide the optimum proportions corresponding to a given priority level or to establish trade‐off curves between mean deviation and cost. The stochastic elements of aggregate gradations are formulated and incorporated into the models. Both the deterministic and stochastic models are applied to a numerical aggregate blending problem, and extensions of the models to accommodate some practical cases are presented.     

Measuring the Impact of Rework on Construction Cost Performance

Rework continues to affect both cost and schedule performance throughout the construction industry. The direct costs alone often tally to 5% of the total construction costs. Using the data obtained from 359 construction projects in the Construction Industry Institute database, this paper assesses the impacts of rework on construction cost performance for projects in various categories. In addition, it identifies the sources of this rework, permitting further analyses and the development of rework reduction initiatives. The results of this study establish that the impacts of rework differ according to project characteristics and that the sources of rework having the greatest impact are not significantly different among project categories. By recognizing the impacts of rework and its sources, the construction industry can reduce rework and ultimately improve project cost performance.     

Optimal Strategy Modeling for Price-Time Biparameter Construction Bidding

In the price-time biparameter construction bidding system, each contractor submits a bid price and construction time to complete the project, which are then aggregated to a total combined bid (TCB) by the client, and the contractor with the lowest TCB is awarded the project. Since bid price can be set as construction cost plus an appropriate markup and construction cost usually depends on construction time, TCB can be expressed as a function of time. By minimizing such a TCB function, the optimal construction time can be obtained, from which the optimal construction cost and bid price and hence the optimal price-time bidding strategy can be sequentially decided. While examining the whole optimal bidding process, this paper focuses on three aspects to enhance the key ideas: discussing the properties of the general and the quadratic time-cost functions, deducing the optimal bidding formulas with quadratic time-cost relationship, and illustrating the procedures for estimating the quadratic time-cost function using a few experience data and the linear regression method. The in-depth examination of the price-time biparameter bidding model in the paper can suggest ideas and methodologies to construction bidding or contractor selection with more criteria.     

Diffusion Wave Model for Simulating Storm-Water Runoff on Highway Pavement Surfaces at Superelevation Transition

On a curved section of highway, the cross slope of the road is often designed to be superelevated to balance the centrifugal force and gravity applied on vehicles. The accumulation of storm-water runoff (sheet flow) near superelevation transitions may significantly increase due to the extended flow path and converging flow lines. A two-dimensional finite-volume-based diffusion wave model is developed to simulate the sheet flow on these geometrically complex surfaces. Both Dirichlet- and Neumann-type boundary conditions are developed for open boundaries based on kinematic wave theory. Results show that the distribution of sheet flow is closely related to the cross slope, longitudinal slope, rainfall intensity, and the width of the road. The analysis of sheet flow characteristics on superelevation transition areas suggests that the optimal longitudinal slope in the range of 0.3–0.4% minimizes the depth of storm-water runoff on the road surface.