also provide a basis for evaluating existing BIM software products and devising a building waste performance analysis (BWA) process. Further, some technological solutions including big data analytics, NoSQL systems, and semantic technologies have also been proposed to complement BIM, which are deemed useful for developing construction waste minimisation plugin. More specifically, our research objectives are: Identification of the critical features of BIM and ICT based technology solutions for construction waste prediction and minimisation. Evaluation of BIM software based on the identified critical features to assess their capabilities for plugin development. The main stream of knowledge behind this involves a thorough review of extant literature on BIM software products and Focused Group Interviews (FGIs) to identify critical BIM features. Transcripts of FGIs were used to confirm and validate these criteria using thematic analysis. This study contributes to effective waste management by identifying critical BIM features along with identification of big data solutions that could be tailored to implement robust waste minimisation plugin. Our research contributions include (i) an evaluation of leading BIM software products on the basis of their support of critical BIM features, (ii) identification of 12 imperative BIM factors that should be harnessed to tackle construction waste, and (iii) devising a BIM-enabled construction waste performance analysis (BWA) process, and (iv) the study of the implication of using big data technologies for plugin development. This study contains general insights for stakeholders involved in construction waste management. In particular, we offer insights and guidelines for software engineers interested in developing similar kinds of tools for construction waste simulation by leveraging BIM and big data technologies. Section 2 briefly introduces BIM software products. In Section 3, the research methodology underpinning this study is explained. Section 4 deliberates our layered approach to explain critical BIM features. Section 5 deliberates BIM-enabled building waste performance analysis (BWA) process. Section 6 highlights big data technologies
Language (GDL) scripting language Open Standards Architectural IFC, RVT, DWG, DGN, PLN, NWD IFC, DNG, DWG IFC, DWG, DGN IFC IFC, DWG Structural Model IFC, CIS/2 IFC, CIS/2 IFC IFC IFC, CIS/2 CAD Data DXF, DWG DWG, DXF DWG, DXF DWG, DXF DWG, DXF GIS Data SHP, KMZ, WFS, GML SHP, KMZ, WFS, GML SHP, KMZ, WFS SHP, KMZ, WFS - Civil Engineering DWG, DGN DWG LandXML, DWG DWG DWG Cost Estimating XLSX, ODBC ODBC ODBC ODBC ODBC Visualisation Model FBX, SKP, NWD SKP, Rhino MOV, SKP, WMF SKP - COBie Data IFC, XLSX IFC IFC IFC - Scheduling Data P3, MPP P3, MPP P3, MPP MPP P3 Energy Analysis IFC, gbXML IFC, gbXML IFC, gbXML IFC, gbXML IFC, gbXML Site Imagery JPG, PNG PNG PNG, JPG, BMP, TIFF BMP, JPG, PNG GIF, PNG, TIFF BIM-enabled Building Waste Performance Analysis (BWA) The term Building waste performance analysis (BWA) is coined here to capture the whole process of employing the BIM for predicting and designing out construction waste. The BWA is mainly comprised of four key steps namely, (i) building model analysis, (ii) waste prediction, (iii) waste visualization, and (iv) waste minimisation. Transcripts of the FGIs are used to develop the phases of the BWA, which are given at the beginning of these phases. The BWA process is illustrated in the Figure 3 as shown below. ,