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.     

Predicting Accuracy of Early Cost Estimates Based on Estimate Quality

The accuracy of an estimate is measured by how well the estimated cost compares to the actual total installed cost. The accuracy of an early estimate depends on four determinants: (1) who was involved in preparing the estimate; (2) how the estimate was prepared; (3) what was known about the project; and (4) other factors considered while preparing the estimate. This paper presents results of a research effort that developed an estimate scoring system to measure the impact of these four determinants on estimate accuracy. The estimate scoring system consists of 45 elements and is organized into 4 divisions. Data were collected from 67 projects, representing $5.6 billion in total installed costs, and used to correlate the estimate scores with estimated versus actual costs. Statistical analyses determined the relative influence of the 45 elements, based on collected project data. The results showed a significant correlation between the estimate score and the accuracy of the estimate. Computer software, the Estimate Score Program (ESP), was developed to automate the scoring procedure, assess estimate accuracy, and predict contingency, based on historical cost data. The estimator can enter the base estimate into ESP and then rate the estimate, relative to each of the 45 elements. ESP automatically calculates the estimate score, as the user rates each element. The user can query the ESP historical database to view the estimate scores and estimate accuracy of similar projects. A cumulative probability S-curve, generated by ESP, is based on projects selected in the query and the estimate score of interest. The user can also predict the cost range—upper and lower limits—of a desired confidence level. ESP can be used to “check” the amount of contingency determined by other methods, as well as a method of predicting its own contingency.     

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.     

Impact of Change Orders on Labor Efficiency for Electrical Construction

Change orders are a source of many disputes in today's construction industry. The issue at hand is whether or not the execution of change orders work has a negative impact on overall labor efficiency on a construction project. Previous literature demonstrates evidence that change orders affect labor efficiency. Attempts have been made to quantify these impacts by many researchers, with limited success. Using the electrical construction industry, a research study has been conducted to quantify the impacts of change orders on labor efficiency. In this paper, results of hypothesis testing and regression analysis are presented. A linear regression model that estimates the loss of efficiency, based on a number of independent variables, is also presented. The independent variables used in this model are (1) qualitative and quantitative criteria used to determine whether projects are impacted by changes or not; (2) the estimate of change order hours for the project as a percentage of the original estimate of work hours; (3) the estimate of change order hours for the project; and (4) the total number of years that the project manager had worked in the construction industry. Additional projects were used to validate the model, with an average error rate of 5%. The results of this research study are useful for owners, construction managers, general contractors, and electrical specialty contractors, because they provide a means to estimate the impact of a change order under certain project conditions. This research also identifies factors, which, when understood and effectively managed, may be used to mitigate the impact of a change order on project costs and efficiency.     

Design and Build Project Success Factors: Multivariate Analysis

This study aimed to identify a set of project success factors for design and build (D&B) projects and examine the relative importance of these factors on project outcome. Six project success factors (project team commitment, contractor's competencies, risk and liability assessment, client's competencies, end-users' needs, and constraints imposed by end-users) were extracted from factor analysis of data provided by 53 participants of public-sector D&B projects through a questionnaire survey. Project team commitment, client's competencies, and contractor's competencies were found to be important to bring successful project outcome from the multiple regression findings. Contractor's competencies also contributed to project time performance. Project team members should also recognize that time and cost performance as well as quality of design and workmanship represent the key elements of overall success of D&B projects. Practitioners are advised to focus on teamwork and partnering for successful project completion. More research should be conducted to further explore the relationship between procurement method and project success factors.     

Evaluation of Risk Factors Leading to Cost Overrun in Delivery of Highway Construction Projects

Accurate owner budget estimates are critical to the initial decision-to-build process for highway construction projects. However, transportation projects have historically experienced significant construction cost overruns from the time the decision to build has been taken by the owner. This paper addresses the problem of why highway projects overrun their predicted costs. It identifies the owner risk variables that contribute to significant cost overrun and then uses factor analysis, expert elicitation, and the nominal group technique to establish groups of importance ranked owner risks. Stepwise multivariate regression analysis is also used to investigate any correlation of the percentage of cost overrun with risks, together with attributes such as highway project type, indexed cost, geographic location, and project delivery method. The research results indicate a correlation between the reciprocal of project budget size and percentage cost overrun. This can be useful for owners in determining more realistic decision-to-build highway budget estimates by taking into account the economies of scale associated with larger projects.     

Data Preprocessing–Based Parametric Cost Model for Building Projects: Case Studies of Korean Construction Projects

For construction to progress smoothly, effective cost estimation is vital, particularly in the conceptual and schematic design stages. In these early phases, despite the fact that initial estimates are highly sensitive to changes in project scope, owners require accurate forecasts which reflect their supplying information. Thus, cost estimators need reliable estimation strategies. In practice, parametric cost estimation, which utilizes historical cost data, is the most commonly used method in these initial phases. Therefore, compilation of historical data pertaining to appropriate cost variance governing parameters is a prime requirement. However, data mining (data preprocessing) for denoising internal errors or abnormal values must be performed before this compilation. To address this issue, this research proposes a statistical methodology for data preprocessing. Moreover, a statistically preprocessed data–based parametric (SPBP) cost model is developed based on multiple regression equations. Case studies of Korean construction projects verify that the model enhances cost estimate accuracy and reliability than conventional cost models.     

Estimating Project S-Curves Using Polynomial Function and Neural Networks

The S-curve is a graphical representation of a construction project’s cumulative progress from start to finish. While S-curves for project control during construction should be estimated analytically based on a schedule of activity times, empirical estimation methods using various mathematical S-curve formulas have been developed for initial planning at predesign stages, with the mean for past similar projects often used as the basis of prediction. In an attempt to make an improvement, a succinct cubic polynomial function for generalizing S-curves is proposed and a comparison with existing formulas shows its advantages of accuracy and simplicity. Based on an analysis of the attributes and actual progress of 101 projects, four factors, i.e., contract amount, duration, type of work, and location, are then used as the inputs of a model developed for estimating S-curves as represented by the polynomial parameters. For model development, it is proposed to use neural networks for their ability to perform complex nonlinear mapping. The neural network model is compared with statistical models with respect to modeling and testing accuracy. The results show that the presented methodology can achieve error reduction consistently, thereby being potentially useful for owners and contractors in early financial planning and checking schedule-based estimates.     

Selection Model for Delivery Methods for Multifamily-Housing Construction Projects

Selecting an appropriate delivery method that will achieve a project’s objectives and characteristics is one of the most critical factors for the project’s success. A selection model for this study was developed by using actual construction case data in quantitative data analysis methods such as logistic regression, factor analysis, and correlation analysis. The model was developed on the basis of the design-build and design-bid-build methods from various project delivery methods. To validate the developed model, comparative tests were conducted on the selection of the delivery method for multifamily-housing construction projects, which showed that the model resulted in 95.0% accuracy. It is expected that the developed selection model will enable owners to select delivery methods that accurately meet their needs characteristics, project characteristics, and external environments.     

Relating Cost Growth from the Initial Estimate to Design Fee for Transportation Projects

Intuitively, there should be a relationship between the size of the design fee for a transportation project and the quality of the resulting design. This study sought that relationship by looking at the fee expressed as a percentage of the construction cost and the final construction cost growth from the engineer’s initial estimate of the construction cost at the time the design contract was awarded. The research team analyzed 31 projects from the Oklahoma Turnpike Authority with a total construction value of $90 million. The projects were divided into road and bridge projects. Based on the results of the analysis, it seems that as the design fee decreases, the absolute percentage of construction cost growth from the engineer’s early estimate increases. The relationship is strongest for bridge projects, which tend to be more technically complex to design than roadway projects. This confirms for U.S. projects the result of an earlier study in Saudi Arabia. This paper concludes that the design fee should be viewed as an investment at a point in time where the ability to impact the project is the highest and can accrue the benefit of reduced cost growth.     

Monte Carlo Simulation Approach to Support Alliance Team Selection

The alliance concept is similar to the design build project delivery system. However, it is denoted by a special form of partnership between the owner and the design-build team, where the owner is very involved in the project. This type of delivery systems is gaining popularity as many infrastructure projects require the owner to order materials ahead of time, before engaging the design-build team in the project. As in design-build, the selection of the engineer-procure-construct team depends not only on the price but also on qualitative factors. This paper lays out the framework that facilitates selecting the best alliance team for a project by quantifying the evaluation factors and combining them into a single score. Using a Monte Carlo simulation and varying all the factors relevant to the decision problem can reveal biases present in the evaluation to assist in making the best possible decision. A case study dealing with a large utility project illustrates this methodology.     

Application of Support Vector Machines in Assessing Conceptual Cost Estimates

Total conceptual cost estimates and the assessment of the quality of these estimates are critical in the early stages of a building construction project. In this study, the support vector machine (SVM) model for assessing the quality of conceptual cost estimates is proposed, and the application of SVM in construction areas is investigated. The results show that the SVM model assessed the quality of conceptual cost estimates slightly more accurately than the discriminant analysis model. This shows that using the SVM has potential in construction areas. In addition, the SVM model can assist clients in their evaluation of the quality of the estimated cost and the probability of exceeding the target cost, and in their decision on whether or not it is necessary to seek a more accurate estimate in the early stages of a project.     

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.     

Predicting Performance of Design-Build and Design-Bid-Build Projects

Design-build (DB) and design-bid-build (DBB) are two principal project delivery systems used in many countries. This paper reports on models constructed to predict performance of DB and DBB projects on 11 areas, using project-specific data collected from 87 building projects. The study included collecting, checking, and validating industry data, and the statistical development of multivariate linear regression models for predicting project performance. Robust models are developed to predict construction and delivery speeds of DB and DBB projects. Gross floor area of the project is the most significant factor affecting speed. Besides this, for DBB projects, contractors’ design ability, and adequacy of plant and equipment would ensure speedy completion of the projects. For DB projects, if the contract period is allowed to vary during tender evaluation, this would slow down the project. Robust models to predict turnover and system quality of DB projects are also constructed. A DB contractor’s track record is an important variable. They must have completed past projects to acceptable quality and have ability in financial, health and safety management.     

Using Benchmarks and Metrics to Evaluate Project Delivery of Environmental Restoration Programs

The United States is in the middle of a large environmental restoration effort that is hampered by a lack of knowledge on how to measure the performance of the project delivery process. This study evaluates one environmental restoration program’s ability to deliver projects: the Environmental Management Program (EMP), a federally sponsored program managed by the U.S. Army Corps of Engineers. Project performance metrics are compiled and used to measure two types of program improvements made in project delivery: trend improvements over time, and the ability to reach established benchmarks. The benchmarks come from both agency guidance and construction industry benchmarks. The metrics measure the program’s ability to accurately estimate the required resources (time and money) to accomplish the project, estimate the cost to operate and maintain the project, and meet the customers’ design requirements. To build the metrics, estimates from the project planning documents are compared against the actual results. Currently, the Corps of Engineers has established some benchmarks and does evaluate projects for design success, but the benchmarks do not include all aspects of project delivery and are not universally applied. Analysis of the metrics shows that the Corps has made improvements in the delivery of projects, but some major components of the process should be improved. Establishing benchmarks would provide the Corps with information to improve the project delivery of the EMP and other environmental restoration programs across the country. This study provides an example of applying business principles to a governmental program.     

Cash Flow Projections for Selected TxDoT Highway Projects

In planning for contractor payments, an owner with multiple projects needs to estimate the amount of money to be paid to contractors in coming months. For an owner as large as the Texas Department of Transportation (TxDoT), one is faced with the problem of organizing the budget to ensure that there are sufficient funds for all projects. This investigation was requested by TxDoT to provide a tool to forecast future project payments. Recent account summaries of TxDoT projects from 2001 to 2003 were analyzed for creating mathematical models representing monthly payments for various projects. The data were organized into categories of project types and subcategories of project contract amount. A fourth degree polynomial regression analysis was run on the data and the regression curve, when statistically significant, was taken to be the forecast payment curve. Finally, a computer program was developed to implement the results of the investigation for TxDoT’s needs. The methodology provided will help other highway agencies to create their own equations to better predict project cash flows and trends. This investigation might also benefit researchers in projecting cash flows and trends, while also allowing for improvements.     

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.     

Predicting Profit Performance for Selecting Candidate International Construction Projects

International projects are inherently exposed to unpredictable and complicated risk scenarios. To minimize possible losses due to these risk exposures, construction firms have their own procedures or basic tools for selecting potential projects, but they are usually based on the experience and knowledge of the firm’s engineers and decision makers that are often very subjective and lack scientific basis. This paper presents a quantitative profit prediction model for the early stage of an international project as a systematic risk-screening tool that involves the processes of defining, analyzing, and evaluating various profit-influencing risk variables. Various successful and unsuccessful international project cases with respect to profit levels are collected. Then, a scale-based profit prediction model to select candidate overseas projects is developed through factor analysis and a multiple regression analysis. Finally, this paper provides implications for global project management and lessons learned from case studies to improve profitability for international projects.     

Risk Assessment Model of Tendering for Chinese Building Projects

This paper presents a risk assessment model for tendering of Chinese building projects on the basis of identification and evaluation of the major risk events in the Chinese construction market, investigations and interviews from which the factors inducing the risk events were determined, questionnaires on building projects within China’s borders, and the logistic regression method. The findings show that, to a certain extent, the risk of tendering for projects and the risk of a contracted project can be assessed through analysis of factors such as owner type, source of project financing, existence or lack of past cooperation between contractors and owners, the intensity of competition for tendering, the reasonableness of the bid price, and the degree of support from the contracting company to its projects. The model can serve as a supplementary tool for Chinese contractors in making decisions for project tendering within Chinese borders. At the same time, it is of reference significance for international contractors, enabling them to further understand the risks in the contract market for Chinese building projects.     

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.