Tonimoghadam, F (2021) A predictive model for reducing cost and time for the detection of clashes in residential and commercial constructions projects. DEng thesis, George Washington University, USA.
Abstract
A predictive model for reducing cost time for the detection of clashes in residential and commercial constructions projects This praxis introduces a predictive model that optimizes the clash detection process by applying a machine learning technique to reduce the cost and time for the detection of clashes in building projects. The success of projects and quality of decisions depends on efficient coordination among different professional teams during the design phase of a construction project and on discovering the clashes at an early stage. This clash detection process will be based on the Building Information Model (BIM) implemented in 3D design review software, such as Navisworks, to make the detection and disposition of clashes more efficient. Detection and analysis of many types of clashes in predictive models will help BIM managers find the relevant clashes automatically and more accurately without spending time detecting them manually. Detected clashes from previous and current projects are used as input data to the machine learning models to detect clashes in data from new projects.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Thesis advisor: | Fossaceca, J and Sarkani, S |
| Uncontrolled Keywords: | coordination; residential; construction project; building information model; learning; professional; machine learning |
| Index terms: | manager, machine learning, construction project, commercial construction, coordination, praxis, design phase |
| Subjects: | management, artificial intelligence, reflexivity, production management, professional practice, construction type, practitioner |
| Topics: | Project Management, Construction Technology, Roles and Professions, Research Practice, Organizational Design, Digital Applications, Design Practice |
| Descriptive scope: | 2 PC |
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