Constrained Fuzzy c-Mean Clustering Algorithm for Determining Bridge Let Projects

Abstract

Bridge engineers need to group bridges to determine adequate let projects based on their proximities, types of work, costs, and cost constraints for each clustered let project after bridges to be treated yearly are identified. This process is time-consuming and typically performed manually. First, this paper presents the formulation of a bridge clustering problem for determining let projects by considering the bridge proximity and type of work with preference membership functions to simulate the actual decision-making process. Second, a constrained fuzzy c-mean (FCM) clustering algorithm is presented to resolves this problem. A case study using the subset of bridges in the state of Georgia with the hypothetical treatments and costs was used to test the developed algorithm and to demonstrate its capability. The results show that the developed constrained FCM clustering algorithm can, in seconds, effectively determine adequate let projects by clustering bridges while meeting cost constraints. The presented formulation also allows incorporation of additional factors such as preference of clustering bridges with the same route number or same route type that are important to other state Departments of Transportation. Finally, conclusions about the benefits and characteristics of the developed algorithm are summarized, and recommendations for future research are discussed.