Preventing and correcting quality issues in construction with intelligent knowledge management

Saygılı, Murathan
The construction industry relies on the successful execution of what is planned. Project cost, schedule, and other performance criteria are set and written on a contract, by which all parties are obligated to abide. Similarly, corporate processes are defined by the procedures of companies. However, projects and corporate processes suffer from various obstacles in practice, some of which arise from certain events that might seem insignificant, causing significant deviations from the plan. These obstacles are quality issues that should be handled by preventing them and correcting the existing ones. The thesis addresses this need by providing a quality management (QM) strategy based on knowledge management (KM). Two research questions search for a systemic approach to prevent inadequate quality and correct nonconformities with KM. After showing the relationship between QM and KM, the research builds an information retrieval mechanism based on the cosine similarity metric and a natural language processing (NLP) model called FastText. The development process is divided into two. The first part enables the effective use of what was previously learned, and the second module deals with correction recommendations in the case of nonconformity. Overall, the study enables the recording, storing, and reusing of tacit information to build a continuous learning and continuous improvement cycle. Processing the information in internal audit findings and lessons learned documents with an NLP model, it is argued that nonconformities can be both fixed and prevented. Ultimately, the thesis aims at a state of zero mistakes in construction through continuous learning and continuous improvement.


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Citation Formats
M. Saygılı, “Preventing and correcting quality issues in construction with intelligent knowledge management,” M.S. - Master of Science, Middle East Technical University, 2022.