Abstract

Construction Rework is a common and widespread issue in the construction industry that has a detrimental impact on the project performance, whether in terms of schedule delay or cost overrun. Developments in Building Information Modelling (BIM) have aided in the resolution of coordination issues, which are thought to be one of the leading sources of rework particularly during the construction stage. Nevertheless, the majority of research has focused on resolving coordination issues during the design stage with minimum regard to the coordination issues that occur during the construction stage. In a BIM environment, the goal of this research is to propose an interactive framework that forecasts possible regions in the project that may undergo rework during construction, as well as predict the severity of rework the project may experience. A two-stage methodology has been adopted to attain the goal of this research. First, a thorough examination of the literature in the areas of rework, BIM, coordination issues, and Artificial Neural Networks (ANN) was performed, followed by interviews and surveys to gather historical data for previous projects. The data collected was then processed to create a database to be later used in the ANN prediction model which includes 7 input neurons. Second, through the integration of BIM and ANN, a model has been constructed based on the data acquired in the first stage. To show the application of the established model and test its efficiency, it was applied to a pilot BIM project and a case study of a real project. The model application generated a user form that displayed the severity of rework. The severity is presented in terms of chance of occurrence, estimated project delay, and estimated cost overrun with an accuracy of 29.35%, 0.2% and 22.79% respectively. A 3D highlighted BIM model is also generated as per each project’s element contribution to rework. The suggested framework should assist decision makers in taking the required actions and focus the necessary resources to either mitigate or eliminate potential rework, hence decreasing potential delays and cost overruns.

School

School of Sciences and Engineering

Department

Construction Engineering Department

Degree Name

MS in Construction Engineering

Graduation Date

Winter 1-31-2022

Submission Date

1-26-2022

First Advisor

Khaled Nassar

Second Advisor

Elkhayam Dorra

Committee Member 1

Ibrahim Abotaleb

Committee Member 2

Mohamed Mahdy Marzouk

Extent

155 p.

Document Type

Master's Thesis

Institutional Review Board (IRB) Approval

Approval has been obtained for this item

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