- Title
- Offsite construction skills prediction: A conceptual model
- Creator
- Ginigaddara, Buddhini; Perera, Srinath; Feng, Yingbin; Rahnamayiezekavat, Payam
- Publisher Link
- http://dx.doi.org/10th World Construction Symposium (WCS 2022). Proceedings of the 10th World Construction Symposium (WCS 2022) (Colombo, Sri Lanka 24-26 June, 2022) p. 648-656
- Relation
- https://ciobwcs.com/papers/
- Publisher
- Ceylan Institute of Builders (CIOB)
- Resource Type
- conference paper
- Date
- 2022
- Description
- Industry 4.0 driven technological advancements have accelerated the uptake of Offsite Construction (OSC), causing the need for re-skilling, up-skilling, and multi-skilling traditional onsite construction skills and competencies. The purpose of this paper is to develop a conceptual model that predicts OSC skills as a response to the OSC demand. The paper is a theoretical resentation of a skill profile prediction model which introduces the key concepts, OSC typology, OSC skill classification and their relationships. Components, panels, pods, modules, and complete buildings represent the OSC typology. Managers, professionals, technicians, and trade workers, clerical and administration workers, machine operators and drivers, and labourers constitute the OSC skill classification. The conceptual model takes the OSC project parameters: gross floor area, OSC value percentage and skill quantities as input and provides predicted skill variations as the output. The skills are quantified in “manhours/m2” under six skill categories, for five distinct OSC types. As such, the research presents a comprehensive conceptual model for the development of an OSC skills predictor to capture the skill variations and demand in a construction market moving towards rapid industrialisation. The research contributes to the existing body of knowledge by identifying the key concepts, parameters, and mutual relationships of those parameters that are needed to develop a realistic prediction of future trends of OSC skills.
- Subject
- conceptual model; offsite construction; prediction; skills
- Identifier
- http://hdl.handle.net/1959.13/1462914
- Identifier
- uon:46591
- Language
- eng
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