2019 CSCE Annual Conference - Laval (Greater Montreal) Conference
Dr. Nima Gerami Seresht, University of Alberta
Dr. Aminah Robinson Fayek, University of Alberta
The productivity of construction projects is affected both directly and indirectly by numerous factors that range from the micro level (i.e., activity, crew, and project levels) to the macro level (organizational, provincial, national, and global levels). Extensive lists of the factors that influence construction productivity have been developed in previous research, and these influencing factors have also been used to analyze and predict productivity at the activity level or project level. The predictive models developed for construction productivity commonly map a set of inputs (i.e., influencing factors) to construction productivity, but they ignore the interrelationships between different influencing factors. In reality, however, construction systems (e.g., construction projects or construction activities) act as complex systems involving numerous interactions between the different factors that influence their productivity. In other words, the factors that influence construction productivity are rarely independent of each other, and changes in certain factors may cause changes in other factors. Despite the extensive research that has been conducted on identifying the factors influencing construction productivity, there is still a lack of research on the development of a structured approach for identifying the interactions between these influencing factors. Interpretive structural modeling (ISM) is an appropriate technique for identifying the interrelationships between the different factors of a multi-dimensional dataset; however, in this case, the application of the ISM technique poses specific challenges because of the uncertainties associated with factors influencing construction productivity. In this paper, therefore, fuzzy logic is hybridized with the ISM technique in order to develop a systematic approach for identifying the interrelationships between the different factors influencing construction productivity. In order to achieve the research objective, the factors that influence construction productivity are first identified through an extensive literature review. Next, the interrelationships between the identified factors are identified using the fuzzy ISM technique and presented using a self-interaction matrix. The results of this research help both researchers and practitioners by improving the accuracy of construction productivity modeling and analysis, leading to more effective project planning and control and an increase in productivity.