Click on the titles below to expand the information about each abstract.
Viewing 1 results ...
Assaad, R, El-Adaway, I H and Abotaleb, I S (2020) Predicting Project Performance in the Construction Industry. Journal of Construction Engineering and Management, 146(05).
- Type: Journal Article
- ISBN/ISSN: 0733-9364
- URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001797
Regardless of the different project characteristics in the construction industry, cost and schedule overruns are always regarded as being of paramount importance in the project controls area. Numerous research efforts have been directed to forecast the aforementioned two important project variables using different modeling techniques. However, no prior work is believed to have offered an integrated approach to estimating the performance of construction projects. This critical knowledge gap is compounded even more by the evolving complexities and uncertainties in today’s construction industry. This paper creates a holistic framework to evaluate project progress and to predict its performance by incorporating a broad spectrum of inputs. The objectives are to (1) quantify the impacts of the risks related to the performance of projects in terms of cost and schedule; (2) formulate a holistic assessment model; and (3) correlate the developed system to predict cost and time at project completion. To this end, a multistep research methodology was utilized. First, for data collection, a survey was distributed and filled by 63 construction experts to study the effects of 25 performance risks that have shown to be the most important based on a meta-analysis of the literature in a previous study. Second, mathematical and statistical analysis techniques were used to develop a model that maps the investigated project risks to both cost and schedule performance. Steps included fitting parametric and nonparametric distributions, calculating cost overruns, verifying the model, and providing guidelines for using the proposed model. Third, for application, a hypothetical dataset was used to demonstrate the use of the model, its ability to deduce real-world behavior patterns, and associated limitations. The developed framework contributes to the body of knowledge by providing a novel model that improves project performance in terms of prediction, control, management, analysis, and decision making based on an individualized assessment of different risk indicators. This study is valuable for the construction industry because it allows all stakeholders to evaluate the performance of construction projects based on a list of variables, ultimately ensuring more effective and efficient delivery and execution of projects.