Journal of Construction Engineering and Management

Integrated project delivery (IPD) seeks to improve project outcomes through a collaborative approach of aligning the incentives and goals of the project team through shared risk and reward, early involvement of all parties, and a multiparty agreement. Although there has been a huge interest in IPD in principle, the current adoption status by the construction industry is unknown. Several professional organizations are supporting the advancement of IPD, and several projects have demonstrated its benefits; however, the amount of projects using IPD remains relatively small. This research is based on the results of a web-based survey that was designed to target a wide range of construction professionals in an effort to shed light on current status of IPD use and its future widespread adoption by the construction industry. The paper attempts to provide hard data concerning the knowledge and experience levels of construction professionals regarding IPD as well as their opinions concerning its benefits and problems as a project delivery method.

Proving and quantifying lost productivity due to cumulative impacts of multiple changes are difficult tasks. This paper presents the most acceptable methods from case law and demonstrates their applications for analyzing the loss of productivity. These methods include earned value analysis, measured mile analysis, and combinations of these two. They are either well established or drawn from recent court and board decisions. A case study is used to illustrate and compare the use of these methods. These methods result in considerably different loss of productivity values though the actual amount (i.e., inefficiency in labor hours) is unique for a particular case and though these methods are often thought to be similar or even the same. How a measured mile analysis and its variants are employed affects the amount of lost productivity estimated. The variants can avoid some drawbacks of measured mile and earned value studies. Nevertheless, which method is more accurate and reliable is difficult to provide for a particular claim. Practitioners should choose between them based on the availability of project records and the nature of changes and cumulative impacts. Practitioners may also employ two or more methods to perform a “sensitivity analysis” of the chosen methods and persuade the other party and/or the jury that their estimate of lost productivity is sufficiently certain.

The earned value method (EVM) is recognized as a viable method for evaluating and forecasting project cost performance. However, its application to schedule performance forecasting has been limited due to poor accuracy in predicting project durations. Recently, several EVM-based schedule forecasting methods were introduced. However, these are still deterministic and have large prediction errors early in the project due to small sample size. In this paper, a new forecasting method is developed based on Kalman filter and the earned schedule method. The Kalman filter forecasting method (KFFM) provides probabilistic predictions of project duration at completion and can be used from the beginning of a project without significant loss of accuracy. KFFM has been programmed in an add-in for Microsoft Excel and it can be implemented on all kinds of projects monitored by EVM or any other S-curve approach. Applications on two real projects are presented here to demonstrate the advantages of KFFM in extracting additional information from data about the status, trend, and future project schedule performance and associated risks.

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.

Implementing risk management in construction projects and organizations may bring a number of benefits and therefore it is necessary to have risk management as an integral part of a construction organization’s management practice. The aim of this paper is to develop a risk management maturity assessment model for construction organizations. The paper describes the development process of a Web-based RM3 (risk management maturity model), including its contents, its validation and testing, as well as its applications. The RM3 developed has five attributes namely, management, risk culture, ability to identify risk, ability to analyze risk, and application of standardized risk management process/system. These attributes are measured against four levels: initial, repeated, managed, and optimized. It is found that the proposed RM3 was suitable and useful. Using the RM3, it is found that the Australian construction industry’s overall risk management maturity level was relatively low (where 32% rated at Level 2 and 52% rated at Level 3). Furthermore, it is found that the weakest attribute was “analyzing risks” followed by “application of standardized risk management process.” It is therefore necessary to provide more training on qualitative and quantitative risk analysis to construction personnel and to develop and apply standardized enterprise risk management. It is concluded that the proposed RM3 is suitable for construction organizations to assess their risk management maturity levels and find ways for improvement.

A building maintenance scheme was introduced in Hong Kong Housing Authority in January 2006 which employed contractors to provide inspection inside the public housing tenants’ units and arranged necessary repair works. This study aims to assess the contractor service quality performance. A method of assessment is devised based on the SERVQUAL approach to measure the public housing owner’s frontline representatives’ expectations and perceptions of the contractors’ performance. The outcome indicates that the deficiencies of service quality variables are related to the willingness of the contractors to provide prompt responses to requests from the tenants or the owner’s frontline representatives, and the availability of sufficient resources to perform the promised service dependably and accurately in terms of time, cost, and quality. The outcome of this study helps to identify the weaknesses of the contractors and allows them to properly allocate resources to those urgently needed variables. The implications from this study are considered and recommendations for industrial practices and further research are made.

Sustainable high-performance buildings are being more widely adopted around the world to reduce energy costs and improve the well being of occupants. To achieve the set goals for these building projects within realistic financial and time constraints, superior planning, design, and construction processes are needed. The available literature lacks the descriptive project delivery metrics identifying scientific methods for providing insight or feedback about the performance of project delivery processes for sustainable high-performance buildings. This paper describes an exploratory study examining more than 100 variables in green project delivery to scientifically identify important metrics. Limited by a rather small sample due to the relatively young market of green buildings, the outcome of this paper, nevertheless, provides important direction for the continued development of meaningful metrics to assist in the establishment of a decision making support tool for project teams to facilitate optimum project delivery processes for sustainable high-performance buildings.

A common problem at state transportation agencies is the inability to complete projects within the original scope of work. Change orders, which are contractual documents issued to accommodate the additional work in a contract, are generally due to root causes such as design errors, unexpected site conditions, and weather conditions, and intermediate causes such as bidding characteristics. At the preaward phase of project management, an improved understanding of the factors that are associated with change orders will be of value and also can serve as a basis for taking steps to reduce concomitant contractual aberrations such as time delay and cost overruns. Recognizing that the occurrence of change orders is a count variable, this paper analyzes the frequency of change orders using a variety of count-modeling methods including the negative binomial, Poisson, zero-inflated negative binomial, and zero-inflated Poisson. Using 5 years of contract data from Indiana highway projects, appropriate models are estimated to assess the influence of project type, contract type, project duration, and project cost on the frequency of change orders.

Equitable allocation of risks between the government and the private sector in concession agreement is essential to the success of public-private partnership (PPP) projects. The decision-making process, based on the established risk allocation principles expressed in linguistic terms, requires qualitative judgment and experiential knowledge of construction experts. However, it is subjective, partial, and implicit in actual application. This paper aims to develop a fuzzy synthetic evaluation model for determining an equitable risk allocation between the government and the private sector. By doing so, it assists the PPP project practitioners to transform the risk allocation principles in linguistic terms into a more usable and systematic quantitative-based analysis using fuzzy set. Twenty-three principles and influencing factors for risk allocation were identified through a comprehensive literature review. Nine critical risk allocation criteria (RACs) that evaluate the risk carrying capability of project participants were further identified, validated, and compiled based on the experts’ knowledge via face-to-face interviews. On the other hand, the weighting for each critical risk allocation criterion was determined through a two-round Delphi questionnaire survey. A set of knowledge-based fuzzy inference rules was then established to set up the membership function for the nine RACs. Based on the research findings, a fuzzy synthetic evaluation model was finally established to determine an equitable risk allocation between the government and the private sector.

The use of design/build (DB) contracting by transportation agencies has been steadily increasing as a project delivery system for large complex highway projects. However, moving to DB from traditional design-bid-build procurement can be a challenge. One significant barrier is gaining acceptance of a best-value selection process in which technical aspects of a proposal are considered separately and then combined with price to determine the winning proposal. These technical aspects mostly consist of qualitative criteria, thus making room for human errors or biases. Any perceived presence of bias or influence in the selection process can lead to public mistrust and protests by bidders. It is important that a rigorous quantitative mathematical analysis of the evaluation process be conducted to determine whether bias exists and to eliminate it. The paper discusses two potential sources of bias—evaluators and weighting model—in the DB selection process and presents mathematical models to detect and remove biases should they exist. A score normalization model deals with biases from the evaluators; then a graphical weight-space volume model and a Monte Carlo statistical sampling model are developed to remove biases from the weighting model. The models are then tested and demonstrated using results from the DB bridge replacement project for the collapsed Mississippi River bridge of Interstate 35W in Minneapolis.

Taiwan has been promoting public-private-partnership (PPP) projects in the wastewater treatment sector with the aim of improving its sustainable environment and increasing its wastewater treatment rate. The Nanzih Wastewater Treatment Project is the first PPP application in Taiwan’s wastewater treatment sector. It provides important experiences and lessons for both the public and private sectors. This paper presents a detailed study of its tender process, concession agreement, financial structure, payment mechanism, and risk management. It explains the key features of the project and provides conclusive findings on the lessons learned. These observations should be useful for practitioners and academia who are interested in the development of future PPP wastewater treatment projects in Taiwan.

The selection of an appropriate project delivery system that suits all project and owner needs is one of the key decisions to a successful project. Therefore, this decision should be made based on thorough analysis. In this paper, a fuzzy multiattribute decision-making (FMADM) model is developed. The model accounts for uncertainties and imprecision in the decision space as well as fuzziness in the nature of the decision attributes. The model utilizes fuzzy decision-making approach in order to evaluate the membership function corresponding to the utility of each project delivery alternative. Project delivery system alternatives are ranked using fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method based on their utility membership functions and by evaluating the distance of each project delivery alternative from fuzzy ideal solutions. In the TOPSIS method, alternatives are ranked based on their closeness coefficient (CC). In addition, the risk attitude of the decision maker is considered in the model by using derived utility membership functions corresponding to the risk attitude of the decision maker. The model is applied to a petrochemical project as a case study. In the case study, the model outcome that ranked Turnkey system as the best system conforms to the lessons learned by the decision maker from several past projects. Moreover, sensitivity analysis is done in the case study. The results show the significant value of the FMADM model for selecting appropriate project delivery system for projects.