Participatory Risk Management Model for Construction Projects Using Expert Systems
Abstract
AbstractConstruction involves processing of numerous interdependent risks. But risk management in the sector is still largely intuitive and mere system compliance. Meticulous efforts towards a system-based approach is lacking. These lapses emphasized the need for developing an easy to use participatory model for risk management at construction project sites. A model that would not only enhance the effectiveness of the risk management process but also generate essential knowledge that can be preserved for future use across the organization. The paper talks about a Risk Information Based Expert System (RIBES) having a user interface, a rule-based nodal inference engine and a knowledge base. The user interface is comprised of three modules namely, ‘Risk Review’, ‘Query’ and ‘Solution’ through which knowledge is banked, retrieved and created respectively. Primarily, the application predicts risks that might impact a project based on its work breakdown structure during bidding and in the construction phase. Further, the application also facilitates the incorporation of stakeholder perceptions into the risk management process of the organization by creating a module wherein all stakeholders are required to complete a questionnaire based on their contractual obligations. The nodal inference engine predicts mitigation measures for a given risk based on its attributes using a set of rules. The preliminary database comprises of a list of seventy-five unique risks and mitigation measures classified into suitable categories. The same was obtained by conducting several workshops & interviews across the hierarchy in a greenfield airport project. Keywords: Construction, risk management, knowledge management, expert systems, rule-based systemsCite this Article Priyadarshini Das, Gangadhar Mahesh, R. Ranjith Kumar. Participatory Risk Management Model for Construction Projects Using Expert Systems. Journal of Construction Engineering, Technology & Management. 2016; 6(2): 17–24p.
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