Thesis Type: Postgraduate
Institution Of The Thesis: Orta Doğu Teknik Üniversitesi, Faculty of Engineering, Department of Civil Engineering, Turkey
Approval Date: 2008
Student: AZİZ HELVACI
Supervisor: RİFAT SÖNMEZAbstract:
Estimation of construction durations is a very crucial part of project planning, as several key decisions are based on the estimated durations. In general, construction durations are estimated by using planning and scheduling techniques such as Gannt or bar chart, the Critical Path Method (CPM), and the Program Evaluation and Review Technique (PERT). However, these techniques usually require detailed design information for estimation of activity durations and determination of the sequencing of the activities. In some cases, pre-design duration estimates may be performed by using these techniques, however, accuracy of these estimates mainly depends on the experience of the planning engineer. In this study, it is aimed to develop and compare alternative methods for conceptual duration estimation of building constructions with basic data information available at the early stages of projects. Five parametric duration estimation models are developed with the data of 17 building projects which were constructed by a contractor in United States. Regression analysis and artificial neural networks are used in the development of these five duration estimation models. A parametric cost estimation model is developed using regression analysis for cost estimations to be used in calculating the prediction performances of cost based duration estimation models. Finally, prediction performances of all parametric duration estimation models are determined and compared. The models provided reasonably accurate estimates for construction durations. The results also indicated that construction durations can be predicted accurately without making an estimate for the project cost.