In the civil engineering field, there are usually unexpected troubles that can cause delays during execution. This situation involves numerous variables (resource number, execution time, costs, working area availability, etc.), mutually dependent, that complicate the definition of the problem analytical model and the related resolution. Consequently, the decision-maker may avoid rational methods to define the activities that could be conveniently modified, relying only on his personal experience or experts’ advices. In order to improve this kind of decision from an objective point of view, the authors analysed the operation correction using a data mining technique, called Fuzzy Clustering. This allows the analysts to represent complex real scenarios and classify the various activities according to their influence on the reduction of the total execution time. The proposed procedure provides positive results that are also in compliance with significant operational constraints, such as the control of costs and areas needed by the workers to perform the tasks. Finally, it is possible to increase the input variable number preserving the algorithm simplicity and avoiding lacks of accuracy in the final numerical outcomes.
|Titolo:||Time reduction for completion of a civil engineering construction using fuzzy clustering techniques|
|Data di pubblicazione:||2017|
|Citazione:||Bosurgi, G., Carbone, F., Pellegrino, O., & Sollazzo, G. (2017). Time reduction for completion of a civil engineering construction using fuzzy clustering techniques. PERIODICA POLYTECHNICA. TRANSPORTATION ENGINEERING, 45(1), 25-34.|
|Appare nelle tipologie:||1.01 Articolo in rivista|