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Paper w78-2010-14:
Assessment of Impacts of Project Technical Complexity on Building Production Using Clustering and Knowledge-Based System

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Arthur W T Leung, C M Tam

Assessment of Impacts of Project Technical Complexity on Building Production Using Clustering and Knowledge-Based System

Abstract: Site production layout planning is highly correlated with the technical complexity of a building project. Building structures, building layouts, scales of project and external site conditions are the major components affecting allocation and positioning of site facilities and construction plant. The relationships between these attributes are well known by experienced project managers. In the planning and tendering process, project managers and planners would assess and decide the site production layout by applying their cognitive knowledge using intuitive rather than quantitative approaches. They recognize the benefit of using quantitative models in decision making, which however present much difficulty when modeling the intwined and complex relationships between large numbers of variables. This study proposes an assessment model to examine impacts of technical designs, building layout designs and site conditions on building production with respect to the site layout plan using a data-based platform, which can assist decision making in site planning.The system consists of two components, the Building Production Impact Assessment Model (BPIA) and the Building Production Impact Database (BPIDB). The BPIA adopts the natural clustering technique, the self-organizing Map (SOM), to classify building project samples in terms of technical complexity to compute the technical complexity index for the sample projects. The sample projects and their index are uploaded to the BPIDB forming the data records. In the assessment platform, planners can input the project information of a new project, and the system will return with a complexity index and three sample projects with the highest similarity. The objective of the proposed system is to generate both a quantitative complexity index derived by the clustering model and the cognitive knowledge through the selected projects to improve the quality of decisions. The conceptual framework of the system will be discussed and illustrated with examples.

Keywords: technical complexity, building production, clustering, database


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