Purpose
– The next generation of Building Information Modelling (BIM) seeks to establish the concept of Building Knowledge Modelling (BKM). The current BIM applications in construction, including those for asset management, have been mainly used to ensure consistent information exchange among the stakeholders. However, BKM needs to utilise knowledge management (KM) techniques into building models to advance the use of these systems. The purpose of this paper is to develop an integrated system to capture, retrieve, and manage information/knowledge for one of the key operations of asset management: building maintenance (BM).
Design/methodology/approach
– The proposed system consists of two modules; BIM module to capture relevant information and case-based reasoning (CBR) module to capture the operational knowledge of maintenance activities. The structure of the CBR module was based on analysis of a number of interviews and case studies conducted with professionals working in public BM departments. This paper discusses the development of the CBR module and its integration with the BIM module. The case retaining function of the developed system identifies the information/knowledge relevant to maintenance cases and pursues the related affected building elements by these cases.
Findings
– The paper concludes that CBR as a tool for KM can improve the performance of BIM models.
Originality/value
– As the research in BKM is still relatively immature, this research takes an advanced step by incorporating the intelligent functions of knowledge systems into BIM-based systems which helps the transformation from the conventional BIM to BKM.
The success of Public Private Partnership (PPP) infrastructure projects is highly dependent on the demand for the services provided by these projects. The demand forecasting process is complex because of the influence of various economic, social and technical factors and the interrelationships among them. In addition, this process is dynamic in nature as many of these factors are time dependent. Current models used for demand forecasting have failed to account for many of these aspects. Among various modeling techniques, System Dynamics (SD) is a promising method for modeling systems with complexity and dynamicity features. The modeling process using SD can be broadly divided into Qualitative System Dynamics and Quantitative System Dynamics. This paper describes the development stages of a conceptual Qualitative SD model for demand forecasting which include: factors identification, creating Causal Loop Diagrams (CLDs), and the CLDs validation. As expert knowledge and perceptions are key requirements to develop a realistic SD model, the paper will emphasis on the knowledge elicitation involved in the development stages. The paper articulates different approaches used to collect and analyze perceptions solicited from experts in toll road projects and the demand forecasting discipline in order to build this qualitative model. In addition, it depicts how the information has been integrated into the different stages of the modeling process. The developed qualitative model will form the basis for the development of the quantitative SD model aiming at improving the practices of demand forecasting in PPP toll road projects.
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