The Creation of Construction Schedules in 4D BIM: A Comparison of Conventional and Automated Approaches

• Published in: Buildings (Basel) Vol. 12; no. 8; p. 1145
• Main Authors: Doukari, Omar, Seck, Boubacar, Greenwood, David
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-08-2022; MDPI
• Subjects: 4D BIM; Artificial intelligence; Automation; Building information modeling; Building management systems; building renovation; Case studies; Construction; Construction industry; construction scheduling; Decision making; Electronic devices; Engineering Sciences; Ontology; Project management; project planning; Research projects; Schedules; Scheduling; Simulation; Software; Task scheduling; Three dimensional models; Typology; automation; building renovation; construction scheduling; lean methodology; project planning; ontology; artificial intelligence; 4D BIM
• ISSN: 2075-5309; 2075-5309
• DOI: 10.3390/buildings12081145

Building Information Modelling (BIM) is now a globally recognised phenomenon, though its adoption remains inconsistent and variable between and within the construction sectors of different countries. BIM technology has enabled a wide range of functional applications, one of which, ‘4D BIM’, involves linking the tasks in a project’s construction schedule to its object-orientated 3D model to improve the logistical decision making and delivery of the project. Ideally, this can be automatically generated but in reality, this is not currently possible, and the process requires considerable manual effort. The level of maturity and expertise in the use of BIM amongst the project participants still varies considerably; adding further obstacles to the ability to derive full benefits from BIM. Reflecting these challenges, two case studies are presented in this paper. The first describes a predominantly manual approach that was used to ameliorate the implementation of 4D BIM on a project in Paris. In fact, there is scope for automating the process: a combination of BIM and Artificial Intelligence (AI) could exploit newly-available data that are increasingly obtainable from smart devices or IoT sensors. A prerequisite for doing so is the development of dedicated ontologies that enable the formalisation of the domain knowledge that is relevant to a particular project typology. Perhaps the most challenging example of this is the case of renovation projects. In the second case study, part of a large European research project, the authors propose such an ontology and demonstrate its application by developing a digital tool for application within the context of deep renovation projects.