Yazdanian, Mehdi, Mokhlespour Esfahani, Mahdiyar, Sheikhkhoshkar, Moslem ORCID: https://orcid.org/0000-0001-9067-2705 and Khanzadi, Mostafa (2024) A new risk response strategy using association rule mining and building information modeling capabilities. International Journal of Engineering - TRANSACTIONS C: Aspects, 37 (12). pp. 2517-2528. ISSN 2423-7167 (Print), 1735-9244 (Online) (doi:10.5829/ije.2024.37.12c.10)

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Abstract

The dynamic and complex nature of the construction industry leads to increased project uncertainty, exposing construction projects to various risks and hazards. Poor risk management can hinder project objectives. Therefore, implementing effective risk management strategies can enhance project quality, safety, and ensure on-time, under-budget completion. This is achievable when the construction industry adopts cutting-edge methods and tools. Building information modeling (BIM) has been widely used to facilitate project risk management due to rapid technological advancements. Given the significance of risk management in construction projects, this study has proposed a novel BIM-based expert system for addressing project risk responses. Data were collected through a questionnaire, and hidden patterns were discovered using SPSS Modeler software (Clementine) through association rule mining. The Apriori algorithm extracted fifty-three top rules from the dataset based on rule evaluation indexes. Subsequently, an expert system was developed using the extracted rules to address project risks. Finally, the expert system was evaluated by five unbiased experts through a questionnaire. This study can serve as a foundation for addressing project risks using BIM and data mining. Subsequent research can apply this method to other construction projects and compare the results with the present study.

Item Type:	Article
Additional Information:	Pls. note that this journal has three editions with three different pairs of ISSNs. This ed. is TRANSACTIONS C: Aspects. See https://www.ije.ir/journal/about - MP
Uncontrolled Keywords:	building information modelling, risk management, risk responses, expert system, association rule mining, construction industry
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD61 Risk Management T Technology > T Technology (General) T Technology > TH Building construction
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Engineering (ENG)
Last Modified:	27 Mar 2025 15:17
URI:	http://gala.gre.ac.uk/id/eprint/50129

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