Date

2022-12-20

Author

Çalışkan, Ekrem Bahadır

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Requirement knowledge of a building project is the set of crucial statements governing all processes to achieve success by matching the objectives. Briefing is the process of capturing and identifying requirements with the involvement of project stakeholders. Various improvements on deficiencies and gaps, developments on technology, and definitions on frameworks for briefing have been explored and examined worldwide over the past three decades. Knowledge capturing is one of the major processes of knowledge management for creating valuable knowledge. The construction industry adopts and uses various techniques and technologies to increase the utilization of resources. The major aim of this study is to construct a framework for the elicitation of space requirements of building projects in the design briefing stage. The study considers the deficiencies and gaps in the creation and validation of the requirement knowledge. At the outset, a survey and interviews among industry practitioners in conjunction with a literature review were carried out to state the problem definition and research areas for improving requirement management in the design and pre-design briefing stages. Subsequently, the criteria and objectives were defined to propose a novel system by utilizing the evaluation and discussion of survey results and literature review. In the light of the findings, the proposed framework was utilized to develop a novel data-driven requirement elicitation system integrating database domain and machine learning activities. The proposed system was tested and validated with seven experiments in which experts executed requirement elicitation of spaces for the same session conditions. As to the judgment of experts, the system's overall performance was regarded as satisfactory. Knowledge capturing from data libraries of completed projects with machine learning activities has been pointed out in the first place as the potential contribution of the system. It also enables the requirement elicitation process without the involvement of experienced project stakeholders. The results were discussed with the recommendations for improvement of the proposed system.

Subject Keywords

Design Briefing, Knowledge Capturing, Machine Learning, Requirement Elicitation

URI

https://hdl.handle.net/11511/101924

Collections

Graduate School of Natural and Applied Sciences, Thesis