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.     

Cost Information Model for Managing Multiple Projects

Construction companies must deal with several projects at once, but a system to manage multiple projects is not fully developed yet. The first step towards developing such system is to design an information model that is suitable for managing multiple projects. This paper presents the cost-based project modeling (CBPM) method in contrast to the traditional activity-based project modeling methods. The CBPM uses cost as a core of the model along with other project information organized around it. The CBPM serves as a platform for integrating project information from multiple projects. Various types of construction costs are hierarchically modeled to generate corporate-wide information such as project performances, cash flows, and other predictive indicators. Based on the information model, an object-oriented database was developed to contain cost data across several projects. In the model, a module that connects to external systems is built into the model to enhance interactivity with the legacy systems and the industry standards. A prototype system was developed and tested with actual project data to validate the information processing capabilities of the model. The findings from the test indicate construction cost can be an excellent medium that can organize various types of information of multiple projects.     

Discriminant Analysis for Predicting Ranges of Cost Variance in International Construction Projects

Unanticipated market conditions as well as project-related risks can easily lead to cost overruns in international construction projects. For a contractor to be financially successful in international projects, a careful examination of the project is a prerequisite to understanding the cost variance characteristics. Based on the reasonably accurate characterization of the cost performance, the markup or contingency amount is determined to ensure both a decent level of profit and a good chance of winning the contract. This paper presents a classification model to categorize international construction projects, particularly faced by Korean contractors, into five cost-variation classes: extreme cost overrun, moderate cost overrun, neutral, moderate cost saving, and extreme cost saving. The model is able to characterize an international project for its cost performance prediction in comparison to the contractor’s initial cost estimate. A linear discriminant analysis is utilized to develop the predictive classification model with the support of the bootstrap method. Tests show that the proposed model is able to help cost estimators determine a proper level of cost contingency before bidding on an international project.     

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.     

Models for Predicting Project Performance in China Using Project Management Practices Adopted by Foreign AEC Firms

China is a new market to many international architectural, engineering, and construction (AEC) firms and it is not known what would be the likely project outcomes, based on different project management (PM) practices adopted. This research developed and tested five models to predict the likely project success levels, based on PM practices adopted by foreign AEC firms in China. Based on data obtained from 33 projects, multiple linear regression (MLR) models for predicting the performance of foreign managed projects in China were constructed. The models were tested against 13 new cases, and the results show that they are able to predict project outcomes with some level of accuracy. The models show that certain scope management practices can be used to predict owner satisfaction, profit margin, and cost and quality performance of the project. Construction industry practitioners who are managing projects in China may benefit from the findings by focusing more on upstream management, like managing project scope, in order to ensure project success. It is recommended that construction industry practitioners use the MLR models to make preliminary assessment of the possibility of project success based on the type of PM practices they intend to adopt in China. From the results, they can then decide if they should change their practices or abort the project.     

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.     

Model for Quantifying the Impact of Change Orders on Project Cost for U.S. Roadwork Construction

Change orders are very common in almost every construction project nowadays, often resulting in increases of 5–10% in the contract price. Understanding the consequences of such trends, several studies have attempted to quantify the impact of change orders on the project cost. Most of the studies aimed at the quantification of the change orders were sponsored by contractors’ organizations, where statistical models used to quantify the impact of the change orders on the project cost were based on data supplied by the contractors; a situation that can lead to owner-contractor disagreements related to the quantification method used. In addition, most of the studies tackled commercial and electromechanical work, and very rare studies tackled the field of heavy construction; a field that suffers from change orders because of errors and omissions, scope of work changes, or changes because of unforeseen conditions. This study addresses the need for a statistical model to quantify the increase of the contract price due to change orders in heavy construction projects in Florida. The model is based on data collected from 16 Florida DOT projects with contract values that ranged between $10–$25 million, and that encountered an increase in the contract price from 0.01 to 15%. Eleven variables were analyzed to test their impact on the cost of the change orders. The study concluded that most significant variables that impact the value of the change order, which are (1) the timing of the change order and (2) when the reason for issuing the change order is unforeseen conditions. Two regression models are developed and validated as follows: (1) a model to quantify the percentage increase in the contract price due to the change orders that increase the contract price from 0.01 to 5% and (2) a model to quantify the percentage increase in the contract price due to the change orders that increase the contract price from 5 to 15%. Those models will provide the owner with a retrospective or forward pricing of the change orders, and hence, allow the owner to estimate and utilize contingency amounts.     

Resource Management of Bridge Deck Rehabilitation: Jacques Cartier Bridge Case Study

The condition and performance of bridges vary widely across North America. The large amount of expenditures on bridges needs significant efforts to optimize budget and resource allocation and to select the best rehabilitation or replacement method, which reduces project cost and duration. Simulation has been widely used in the construction area to optimize productivity and resource allocation. Current research optimizes resource combination for bridge deck rehabilitation projects using discrete event simulation. The Jacques Cartier Bridge redecking project is selected as a case study. Data related to productivity and duration of different activities were collected from the project. Probability distributions are fitted, which show the robustness of normal distribution to fit most variables. A simulation model is developed for this project in order to experiment with and perform sensitivity analysis. Based on the simulation results, an optimum resource combination of deck rehabilitation is obtained, which is [five teams, two saws, three old section trucks, and five new panel trucks] TSON 5235 with the unit (panel) cost of $747/h (direct cost only). The model developed is tested against real productivity where it shows reasonable results. The present research is relevant to both researchers and practitioners. It provides bridge redecking researchers with a real case study, a simulation model, and an approach to analyze projects. It also provides practitioners with an approach to optimize the usage of their resources considering direct project cost.     

Project Performance versus Use of Technologies at Project and Phase Levels

Lack of information regarding technology benefits along with uncertain competitive advantage from new technology have resulted in industry reluctance to implement new technologies. An industry-wide survey was used to collect project data from more than 200 capital facility projects on the issue of technology usage and overall project success. Twenty-two research hypotheses are presented and analyzed according to five different data class variables: industry sector, total installed cost, public versus private, greenfield versus expansion versus renovation, and typical versus advanced projects. Findings pertaining to associations between project success and technology usage at the project and phase level are discussed. The results of this research indicate that several technologies may contribute significantly to project performance in terms of cost and schedule success, particularly for certain types of projects. In addition, project schedule success is more closely associated with technology utilization than is project cost success. Findings from this study can provide companies with information on technology benefits and whether to use certain technologies.     

Applying a Genetic Algorithm-Based Multiobjective Approach for Time-Cost Optimization

Reducing both project cost and time (duration) is critical in a competitive environment. However, a trade-off between project time and cost is required. This in turn requires contracting organizations to carefully evaluate various approaches to attaining an optimal time-cost equilibrium. Although several analytical models have been developed for time-cost optimization (TCO), they mainly focus on projects where the contract duration is fixed. The optimization objective in those cases is therefore restricted to identifying the minimum total cost only. With the increasing popularity of alternative project delivery systems, clients and contractors are targeting the increased benefits and opportunities of seeking an earlier project completion. The multiobjective model for TCO proposed in this paper is powered by techniques using genetic algorithms (GAs). The proposed model integrates the adaptive weights derived from previous generations, and induces a search pressure toward an ideal point. The concept of the GA-based multiobjective TCO model is illustrated through a simple manual simulation, and the results indicate that the model could assist decision-makers in concurrently arriving at an optimal project duration and total cost.     

Project Performance Control in Reconstruction Projects

Cost, schedule, and quality are the main indicators of performance in construction projects. These indicators are highly interrelated and require some balance and trade-off among them to achieve efficient overall control over project performance. Focusing on these performance indicators, the primary objective of this study is to investigate the use of conventional control techniques in projects involving reconstruction of occupied buildings. To facilitate this analysis, performance data have been collected, using a questionnaire survey, from 25 reconstruction and 15 new construction projects. The survey was followed by structured interviews with construction practitioners and project participants to elicit success-related factors and to identify some of the unique problems affecting the control of reconstruction projects. Using the collected data, performance comparison was conducted between new and reconstruction projects along with a detailed analysis of the suitability of existing techniques for the control of the cost, schedule, and quality in reconstruction projects.     

Construction Project Scheduling with Time, Cost, and Material Restrictions Using Fuzzy Mathematical Models and Critical Path Method

This article evaluates the viability of using fuzzy mathematical models for determining construction schedules and for evaluating the contingencies created by schedule compression and delays due to unforeseen material shortages. Networks were analyzed using three methods: manual critical path method scheduling calculations, Primavera Project Management software (P5), and mathematical models using the Optimization Programming Language software. Fuzzy mathematical models that allow the multiobjective optimization of project schedules considering constraints such as time, cost, and unexpected materials shortages were used to verify commonly used methodologies for finding the minimum completion time for projects. The research also used a heuristic procedure for material allocation and sensitivity analysis to test five cases of material shortage, which increase the cost of construction and delay the completion time of projects. From the results obtained during the research investigation, it was determined that it is not just whether there is a shortage of a material but rather the way materials are allocated to different activities that affect project durations. It is important to give higher priority to activities that have minimum float values, instead of merely allocating materials to activities that are immediately ready to start.     

Reasons for Cost and Schedule Increase for Engineering Design Projects

Cost and schedule increases are common in engineering design projects. Some research has studied factors associated with better design performance, but the reasons for cost and schedule increases are not formally investigated. This paper identifies the reasons bottom up from four case project documents and further quantifies their contributions to cost and schedule increases. These reasons are complete and can be used to analyze the cause-effect relationship, trace responsibility, and improve performance for engineering design projects.     

Quantitative Definition of Projects Impacted by Change Orders

Change, defined as any event that results in a modification of the original scope, execution time, or cost of work, is inevitable on most construction projects due to the uniqueness of each project and the limited resources of time and money available for planning. Change may occur on a project for a number of reasons, such as design errors, design changes, additions to the scope, or unknown conditions. For each change, contractors are entitled to an equitable adjustment to the base contract price and schedule for all productivity impacts associated with the change. Changes may or may not have an impact on labor productivity. Existing literature uses subjective evaluation to determine whether the project is impacted. Projects impacted by change cause the contractor to achieve a lower productivity level than planned. The focus of this paper is to quantify whether an electrical or mechanical project is impacted by a change order. Through statistical hypothesis testing, groups of factors that correlate with whether a project is impacted by change orders were identified and used to develop a quantitative definition of impact. Logistic regression techniques were used to develop models that predict the probability of a project being impacted. The results of this research show that percent change, type of trade, estimated and actual peak manpower, processing time of change, overtime, overmanning, and percent change related to design issues are the main factors contributing to the project impact.     

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.     

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%.     

Ontology for Relating Risk and Vulnerability to Cost Overrun in International Projects

Risk management is about identifying risks, assessing their impacts, and developing mitigation strategies to ensure project success. The difference between the expected and actual project outcomes is usually attributed to risk events and how they are managed throughout the project. Although there are several reference frameworks that explain how risks can be managed in construction projects, a major bottleneck is the lack of a common vocabulary for risk-related concepts. Poor definition of risk and patterns of risk propagation in a project decrease the reliability of risk models that are constructed to simulate project outcomes under different risk occurrence scenarios. This study aims to extend previous studies in risk management by presenting an ontology for relating risk-related concepts to cost overrun. The major idea is that cost overrun depends on causal relations between various risk sources (namely, risk paths) and sources of vulnerability that interfere with these paths. Ontology is used to develop a database system that represents risk event histories of international construction projects and to construct a model for estimation of cost overrun. It will form the basis of a multiagent system that can be used to simulate the negotiation process among project participants about sharing of costs considering the risk allocation clauses in the contract, sources of vulnerability, and causal relations between risk events and their impacts. The ontology is constructed by interaction with Turkish contractors working in international markets and extensive literature review on risk-related concepts. The validation test results provide evidence that the ontology is fairly effective to help Turkish contractors to assess cost overrun by considering sources of vulnerability and risk in international construction projects.     

Optimization of Earthmoving Operations in Heavy Civil Engineering Projects

Large scale earthmoving operations require the use of heavy and costly construction equipment. Optimum utilization of equipment is a crucial task for the project management team. It can result in substantial savings in both time and cost of earthmoving operations. This paper presents optimization model for earthmoving operations in heavy civil engineering projects. The developed model is designed to assist general contractor in optimizing planning of earthmoving operations. The model utilizes genetic algorithm, linear programming, and geographic information systems to support its management functions. The model assists in planning earthmoving operations; taking into consideration: (1) availability of resources to contractors; (2) project budget and/or time constraints, if any; (3) scope of work; (4) construction site conditions; (5) soil type; (6) project indirect cost; and (7) equipment characteristics. The model also determines the quantities of earth to be moved from different borrow pits and those to be placed at different landfill sites to meet optimization objective set by the user and to meet project constraints. The model has been implemented in prototype software, using object-oriented programming. Two numerical example projects are presented to validate and demonstrate the use of the developed model in optimizing earthmoving operations.     

Life-Cycle Management of Construction Projects Based on Virtual Prototyping Technology

Life-cycle management (LCM) has been employed in the management of construction projects for many years in order to reduce whole life cost, time, risk and improve the service to owners. However, owing to lack of an effective information sharing platform, the current LCM of construction projects is not effectively used in the construction industry. Based upon the analysis of the information flow of LCM, a virtual prototyping (VP)-based communication and collaboration information platform is proposed. Following this, the platform is customized using DASSAULT software. The whole process of implementing the VP-based LCM of projects is analyzed via the application to a real-life construction project. The advantages of implementing a VP-based LCM are also discussed and, from a simple case study, it is demonstrated that the VP-based communication and collaboration information platform is an effective tool to support the LCM of construction 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.