Journal of Construction Engineering and Management

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.

The competitive bidding system has been to blame for abnormally low bids, which are considered as one of the main causes of poor project quality. Previous studies have regarded the pricing of bidders as an optimum decision based on contractor’s cost and market competition level. However, the sell to produce characteristic of construction projects may induce contractors to offer a low bid and then make up the amount initially sacrificed from beyond-contractual reward (BCR) gained through cutting corners and claims. System dynamics was adopted in this study to develop a contractor’s pricing model with consideration of the dimensions of cost, market competition, and BCR. The model was then examined by statistical analysis of data collected from 44 highway projects in Taiwan. It was found that the equilibrium market price is significantly associated with BCR, which is assumed to be determined by the strictness of the owner’s construction management, including both soundness of contract and tightness in construction supervision. Research results suggest that contractors divide the market into different segments according to the owner’s strictness of construction management and the equilibrium price level of each market segment varies. The price level for projects with a strict owner is remarkably higher than for those with relatively less strict owners. Improvement in the construction management system of projects is crucial to lower the possibility that contractors gain BCR and do opportunistic bidding, and to further enhance project quality.

Linear repetitive construction projects require large amounts of resources which are used in a sequential manner and therefore effective resource management is very important both in terms of project cost and duration. Existing methodologies such as the critical path method and the repetitive scheduling method optimize the schedule with respect to a single factor, to achieve minimum duration or minimize resource work breaks, respectively. However real life scheduling decisions are more complicated and project managers must make decisions that address the various cost elements in a holistic way. To respond to this need, new methodologies that can be applied through the use of decision support systems should be developed. This paper introduces a multiobjective linear programming model for scheduling linear repetitive projects, which takes into consideration cost elements regarding the project’s duration, the idle time of resources, and the delivery time of the project’s units. The proposed model can be used to generate alternative schedules based on the relative magnitude and importance of the different cost elements. In this sense, it provides managers with the capability to consider alternative schedules besides those defined by minimum duration or maximizing work continuity of resources. The application of the model to a well known example in the literature demonstrates its use in providing explicatory analysis of the results.

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.

Currently there are no formal decision tools or guidelines to assist owners and project managers in choosing delivery systems and project strategies that would allow significant reductions in the project cycle time. The development of a decision aid that would allow a project manager to prioritize and apply project cycle reduction techniques would be a valuable tool for achieving project cycle time reduction in projects. This paper presents a new high performance project delivery system called “project manager’s game planner” (PMGP) designed to assist a decision maker in identifying and utilizing an optimal set of radical reduction techniques (RRTs) with the greatest potential for success in achieving cycle time reduction. Additionally, the PMGP can assist the user in identifying the top RRTs during any of the five project phases: preproject planning, design, material management, construction, and start-up. Most projects can utilize this PMGP to improve the project performance whether to achieve significant cycle time reduction or to simply achieve effective project execution.

This paper reports an empirical study that tested the usefulness of Web-based four-dimensional (4D) construction visualization in collaborative construction planning and scheduling. Several recent construction projects have employed 4D visualization to better understand the construction schedule and make proactive decisions to prevent logical errors in the construction sequence. Other groups have shown that construction information management with asynchronous Web-based communication can improve decision making among dispersed industry practitioners. It seems reasonable to anticipate that combining 4D visualization with Web-based information management would facilitate dispersed industry practitioners to make collaborative decisions for construction planning and scheduling. The empirical study presented here reports how experiment participants at separate locations collaboratively detected logical errors in a construction schedule when the 4D visualization model of the schedule was represented on the Web browser. Our results show that teams using 4D models detected logical errors more frequently, faster, with fewer mistakes, and with less team communication, than teams using 2D drawings and bar charts. These findings show industry practitioners empirical evidence that Web-based 4D construction visualization can improve team collaboration on construction planning and scheduling.

In the field of knowledge management research, socialization means to convert individual into group tacit knowledge. This process matters from the outset of an architecture, engineering, and construction (AEC) project to enhance collaborative work. Face-to-face meetings and phone calls undoubtedly facilitate socialization. However, meetings can be hard to timetable and expensive when AEC teams are geographically dispersed, whereas phone calls are cheap but offer limited capabilities for problem solving. Further, both media are not good at supporting asynchronous socialization. This study investigates the extent Internet-based media can promote cross-firm socialization and enhance collaborative work. The cross fertilization of findings from an exploratory case study with theory in computer-supported collaborative work (CSCW) informs the development of a conceptual framework on digital socialization. This framework underpins IDRAK—a proof-of-concept of a rich Internet application prototype to promote socialization in AEC projects. Our main contribution is the design of a novel methodology to evaluate the usability of digital systems to support socialization at the early design stage of an AEC project. The results from our lab experiments suggest that IDRAK can satisfactorily and efficiently enhance collaborative work. However, more research is needed, first, to evaluate the effectiveness of IDRAK to improve design quality and asynchronous socialization; and second, to investigate how other CSCW features can improve the performance of IDRAK-like systems.

Predicting performance of contractors is of interest to both academics and practitioners. The physical execution of a project is critical to the overall success of the development. Having a competent contractor that can deliver is most desirable. In this aspect, a significant number of performance prediction models have been developed. Multiple regression and neural networks are typically used as the analytical tools in these prediction models. This paper reports a study that employs a learning curve approach to perform the prediction task. It is suggested that this approach can accommodate the changes in performance as experience accumulates. Thus a performance pattern is projected in addition to the project final outcome. A two-step approach suggested by Everett and Farghal was adopted for this study. First, the learning curve model that best represents a contractors’ performance was explored using the least-square curve fitting analysis. Second, prediction analysis was performed by comparing the actual performance data with their respective prediction results obtained from extrapolation on the selected learning curve. The three-parameter hyperbolic model was found to provide the most reliable prediction on performance in this study.