Advances in Engineering Advances in Engineering features breaking research judged by Advances in Engineering advisory team to be of key importance in the Engineering field. Papers are selected from over 10,000 published each week from most peer reviewed journals.
Significance Statement
For construction organizations, evaluating competencies is a crucial component in achieving improved performance. In order to attain this outcome, the relationships between project competencies and key performance indicators (KPIs) must be identified. KPIs measure project success against planned values related to cost, schedule, change, safety, quality, productivity, and satisfaction.
Using data collected through interview surveys administered on seven construction projects, Dr. Aminah Robinson Fayek and Dr. Moataz Nabil Omar from the University of Alberta in Canada quantified 41 project competencies with 248 criteria and 7 performance categories with 46 project KPIs. The project competencies were separated into two different categories: functional competencies and behavioural competencies. Functional competencies refer to organizational and project practices that contribute to the execution of tasks on a construction project, while behavioural competencies refer to the attributes, such as knowledge, skills, abilities, motives, beliefs, values, and interests, of individuals and teams working on a construction project.
Following the data collection process, prioritized fuzzy aggregation was used to measure the maturity and agreement of the different project competencies. Factor analysis was then performed on the aggregated data to group the different construction project competencies and generate factor coefficients, representing the importance of each project competency within its respective factor group. The factor analysis makes use of eigenvalues (i.e., linear transformations) to reduce the overall number of competency factors into a smaller number of factor groups. The authors created a set of fuzzy neural networks (FNNs), which integrate fuzzy set theory with artificial neural networks. The FNNs use the prioritized fuzzy aggregation results, factor analysis results, and data collected for the KPIs to quantify the relationships that exist between the project competencies and project KPIs.
The major contributions made by this study are in the development and testing of a new competency model, which uses prioritized fuzzy aggregation, factor analysis, and FNNs, for identifying and quantifying the impact of construction project competencies on project KPIs. The results of this study will help construction organizations to improve their project performance, which will in turn help them to improve their overall efficiency and competitiveness. The authors suggest that this model can also be used for other applications, including benchmarking construction processes at the organizational and industry levels.
Reference
Moataz Nabil Omar1 , Aminah Robinson Fayek2 , Modeling and evaluating construction project competencies and their relationship to project performance, Automation in Construction, 2016, Volume 69, Pages 115–130.
[expand title=”Show Affiliations”]1 Hole School of Construction Engineering, Dept. of Civil and Environmental Engineering, Univ. of Alberta, 7-381, Donadeo Innovation Centre for Engineering, Edmonton, AB T6G 1H9, Canada.
2 Hole School of Construction Engineering, Dept. of Civil and Environmental Engineering, Univ. of Alberta, 7-287, Donadeo Innovation Centre for Engineering, Edmonton, AB, T6G 1H9, Canada. [/expand]
Go To Automation in Construction
