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.     

Risk Planning and Management for the Panama Canal Expansion Program

In April 2006, the Panama Canal Authority formally proposed a major expansion of the canal to increase its capacity and make it more productive, safe, and efficient. This proposal included cost and schedule estimates for completing the expansion and was supported overwhelmingly by the citizens of Panama in an October 2006 public referendum. Given the conceptual level of design at the time of the proposal and the inherent uncertainty in a project of this magnitude at the early stages of engineering, a comprehensive risk analysis was performed to develop a contingency model for the total expansion program cost and schedule. This contingency model is based on a Monte Carlo simulation of the cost and schedule estimates, taking into account the most significant risks identified for the project. The resulting model provides contingency assessments for duration and total cost and sensitivity analysis of the risks; it also allows for multiple scenario planning and ultimately supports overall risk management. This paper presents a project case study that focuses on the contingency model development and the resulting risk management and contingency resolution processes.     

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.     

Optimal Schedule Planning for Multiple, Repetitive Construction Process

This study attempts to develop a construction scheduling model using a conceptual approach to improve the efficiency of construction resources for a multiple, repetitive construction process (MRCP). It is important for a project manager to arrange the number of horizontal repetitive work areas by each crew group to reduce the work interruption period in MRCP. This study suggests some equations for estimating the optimal number of horizontal repetitive work areas for a crew group and pursued a conceptual model for MRCP, which can reduce the loss in manpower and use of construction equipment that is caused by work interruption periods. A computer program developed for the analysis of MRCP shows an appropriate performance through the application to a case study based on the proposed procedure model in this study. Supposing that there is time to spare in the project completion date, the result of the study shows that cost loss could be greatly reduced by the proposed methodology.     

Cost and Schedule Monitoring of Industrial Building Projects: Case Study

Performance measurement is a helpful tool for taking corrective actions and controlling a project as far as this enables accurate time and cost forecasts during the first stages of the construction effort when the management team still has opportunities to make adjustments. This paper overcomes the dilemma of practicability and predictability of traditional estimates at completion based on early progress measurement by presenting the empirical results from the construction project of an industrial facility. The case may be a reference practice for assessing time and cost performance measurement of any building, whose layout can be reasonably partitioned into repeatable portions. In such circumstances, an effectively-managed traditional earned value method and appropriate metrics for computing performance provide project managers with accurate forecasts as useful tools for successful project management and control.     

Probabilistic Estimating: Mathematics and Applications

In response to the increased risk levels found in today's projects, project participants are attempting to quantify project cost risk. Various detailed and conceptual estimating formats are reviewed and a number of probabilistic estimating methods are introduced. Particular attention is given to the process of matching a suitable probabilistic estimating technique to a specific estimating format. To select an appropriate probabilistic method, an estimator must consider data availability, existing correlations, output data requirements, the form of the, estimating model, and the number of cost elements contained in the model. Existing obstacles to more realistic probabilistic cost estimates are reviewed.     

Computer-Integrated System for Estimating the Costs of Building Projects

The cost estimate is considered one of the most important and critical phases of a construction project. Preparing reliable and accurate estimates to help decision makers is the most challenging assignment that cost engineers and estimators face. For decades, practitioners in the construction industry focused only on estimating the initial cost of a facility, neglecting costs associated with operating and maintaining it. Today, more interest lies in investigating the economics of facility management, which include the cost of owning and operating a building over its useful life. This paper presents a methodology that can be used for an integrated conceptual cost estimating and life-cycle cost analysis for construction projects during their initial phase. The methodology describes the development and implementation of a system that automates the preparation of parametric cost estimates and forecasts future running costs of building projects. The system integrates relational databases, a parametric cost estimate module, an AutoCAD module, a global module, a cost estimate forecasting and decision support system module, and a life cycle costing and sensitivity analysis module. The system will automatically generate a new parametric estimate upon any modification in building design. Once the capital costs are identified, the system forecasts the cost of running and maintaining the new building throughout its expected service life. After assigning the range of deviation, a sensitivity analysis is conducted, which identifies the most sensitive parameters for further consideration and analysis. Designing the system in a user-friendly environment allows owners and decision makers to envision the feasibility of new building projects within their anticipated life cycles. Moreover, it assists architects and cost engineers in generating parametric cost estimates in a dynamic environment. A numerical case example is presented to illustrate the usefulness and capabilities of the developed system.     

Time, Cost, and Quality in a Road Building Project

Time, cost and quality are the three factors that play a significant role in the planning and controlling of construction projects. The main barriers for their achievement are the changes in the project environment necessitating cost, time, and quality trade-offs. Literature has mainly focused on analyzing time and cost with little or no reported research focusing on models for optimizing construction time, cost, and quality jointly. Government agencies have recently started using new types of contracting methods which have placed an increasing pressure on decision makers in the construction industry to search for an optimal/near optimal resource utilization plan that minimizes construction cost and time while maximizing its quality. In this paper, a 0-1 Integer Programming model which enables meeting quality output standards and time and budget objectives respectively is developed. The clever use of binary variables allows us to solve many interesting and difficult problems and our ability to model complex problems increases tremendously when we use binary variables. A version of the model to minimize time meeting quality and cost objectives is applied to a road building project. Two alternatives versions of the model which enable to minimize cost meeting quality and time and to maximize quality meeting time and cost, are shown in an Appendix.     

Predicting Accuracy of Early Cost Estimates Using Factor Analysis and Multivariate Regression

The importance of accurate estimates during the early stages of capital projects has been widely recognized for many years. Early project estimates represent a key ingredient in business unit decisions and often become the basis for a project’s ultimate funding. However, a stark contrast arises when comparing the importance of early estimates with the amount of information typically available during the preparation of an early estimate. Such limited scope definition often leads to questionable estimate accuracy. Even so, very few quantitative methods are available that enable estimators and business managers to objectively evaluate the accuracy of early estimates. The primary objective of this study was to establish such a model. To accomplish this objective, quantitative data were collected from completed construction projects in the process industry. Each of the respondents was asked to assign a one-to-five rating for each of 45 potential drivers of estimate accuracy for a given estimate. The data were analyzed using factor analysis and multivariate regression analysis. The factor analysis was used to group the 45 elements into 11 orthogonal factors. Multivariate regression analysis was performed on the 11 factors to determine a suitable model for predicting estimate accuracy. The resulting model, known as the estimate score procedure, allows the project team to score an estimate and then predict its accuracy based on the estimate score. In addition, a computer software tool, the Estimate Score Program, was developed to automate the estimate score procedure. The multivariate regression analysis identified 5 of the 11 factors that were significant at the α = 10% level. The five factors, in order of significance, were basic process design, team experience and cost information, time allowed to prepare the estimate, site requirements, and bidding and labor climate.     

房地产开发中的工程成本控制

以“规划设计是龙头”为要点,说明工程建设项目的成本控制关键在规划设计。同时提出建立一套完善的房地产开发管理制度,对保证项目建设按流程进行,将工程成本控制在预期的范围内。     

Time-Cost Optimization of Construction Projects with Generalized Activity Constraints

Time-cost analysis is an important element of project scheduling, especially for lengthy and costly construction projects, as it evaluates alternative schedules and establishes an optimum one considering any project completion deadline. Existing methods for time-cost analysis have not adequately considered typical activity and project characteristics, such as generalized precedence relationships between activities, external time constraints, activity planning constraints, and bonuses/penalties for early/delayed project completion that would provide a more realistic representation of actual construction projects. The present work aims to incorporate such characteristics in the analysis and has developed two solution methods, an exact and an approximate one. The exact method utilizes a linear/integer programming model to provide the optimal project time-cost curve and the minimum cost schedule considering all activity time-cost alternatives together. The approximate method performs a progressive project length reduction providing a near-optimal project time-cost curve but it is faster than the exact method as it examines only certain activities at each stage. In addition, it can be easily incorporated in project scheduling software. Evaluation results indicate that both methods can effectively simulate the structure of construction projects, and their application is expected to provide time and cost savings.     

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.     

Contractual Incentives

Contract incentives are the means by which an owner intends to secure certain project goals through the contracting process. Incentive contracting is designed primarily to reduce cost in negotiated contracts through profit sharing ratios, which should improve on the efficiency of cost reimbursable contracts. In the process, financial risk and control are shared by the owner and contractor, according to a ratio which is established in the early stages of project design. Contractual incentives are used frequently in construction to reduce overall project time. However, there is a lack of published research on the theory and consequences of the use of incentives in construction. Studies in government research and development contracts using incentives shows that contractors may not always behave in the fashion intended by owners designing such contracts. The apparent reason is that the risk a contractor assumes under conditions of limited scope and design information biases the setting of targets, so that overruns∕underruns are more dependent on where targets are set, rather than on sharing ratios. In the construction industry this is apparently recognized, and targets are not fixed until design is approximately 40%–60% complete. Moreover, as the contractor and owner attain more knowledge of the project, both parties should attempt to reduce owner risk and control.     

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.     

Owners Respond: Preferences for Task Performance, Delivery Systems, and Quality Management

Twenty years ago, public owners did almost all of their own design work and project inspection, but this is no longer the case. Innovative construction ideas of 15 years ago are now considered mainstream, and newer ideas are taking their places. Fifteen years ago, the concept of fast-track construction was still being researched and many federal agencies refused to fund it; today, most large federal projects call for some sort of fast tracking. These systems allow construction input into the design process of construction projects. Whoever performs tasks such as scope development, prebid cost estimates, and quality assurance can significantly affect the quality of the project, as can which delivery system is used. The Construction Institute of ASCE sponsored a survey of construction owners to determine what delivery systems are used in their projects and whom they select to perform various tasks. The survey also sought to ascertain the owners’ satisfaction level with the performance of the individuals and systems they choose. This paper, written for practitioners and researchers, describes the results of that survey.     

Dynamic Site Layout Planning Using Approximate Dynamic Programming

Dynamic site layout planning requires identifying and updating the positions of all temporary construction facilities such as offices, storage areas, and workshops over the entire project duration. Existing models do not guarantee global optimal solutions because they focus on optimizing the planning and layout of successive construction stages in a chronological order, without considering the future implications of layout decisions made in early stages. This paper presents the development of an approximate dynamic programming model that is capable of searching for and identifying global optimal dynamic site layout plans. The model applies the concepts of approximate dynamic programming to estimate the future effects of layout decisions in early stages on future decisions in later stages. The model is developed in three main phases: (1) formulating the decision variables, geometric constraints, and objective function of the dynamic site layout planning problem; (2) modeling the problem using approximate dynamic programming; and (3) implementing and evaluating the performance of the model. An evaluation example is analyzed to illustrate the use of the model and demonstrate its capabilities in generating global optimal solution for dynamic site layout planning of construction projects.     

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.     

Potential of Total Cycle Time Compression in Construction: Focus on Program Development and Design

Time compression is a widely accepted approach to improving customer service levels while minimizing total costs. However, few applications within the construction sector exist, especially in relation to the early stages of program development and design. These stages are of paramount importance because of the resultant ramifications on the total cycle time of the entire construction project, in the form of changes, rework, waste, and risk. Most applications of time compression focus on quantifying and removing nonvalue adding time from business processes. This is difficult for program development and the design stages of construction because most information sources are qualitative. As a result, a methodology has been developed to identify the key areas of potential improvement during these early stages. Collection of opinion data via semistructured interviews, questionnaires, and a workshop with a cross section of supply chain members has made it possible to quantify the potential improvements. The action research findings have been summarized into a time-compression model for the program development stages in the format of two interlinked cause and effect diagrams. These further emphasize the need for effective program development to minimize the risks of project overrun.     

Integrated Schedule and Cost Model for Repetitive Construction Process

Several efforts have been made by many researchers to develop a model for schedule and cost integration in construction projects, but it is difficult to integrate and manage schedule and cost in an actual construction site using such a model. The integrated schedule and cost model developed in this study (1) enables the planning and control of repetitive construction processes and (2) can be used by a project manager in an actual construction site. Furthermore, an integrated schedule and cost model for the core wall construction, which is an important repetitive process in the recently booming high-rise building construction in terms of scheduling, was developed using the integration model developed in this study. It is expected that the integrated schedule and cost model developed can allow project managers to integrate the schedule and cost of repetitive construction processes more effectively and support the project managers’ decision-making.     

Drivers for Cost Estimating in Early Design: Case Study of Residential Construction

This paper proposes the use of a series of independent variables for an early estimation of the building construction cost of residential buildings. Based on 70 German residential properties, these variables serve as the cost drivers of a project, and the regression model, tested against the 70 properties, has a mean absolute percentage error of 9.6%. When applied to predict the cost of five more properties that were excluded from the 70 in the regression model, the percentage error ranges between–12 and 13%. The identified cost drivers are: compactness of the building, number of elevators, size of the project, expected duration of construction, proportion of openings in external walls, and region.