An approach to reduce concrete rework using Building Information Models
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Bibliographic Details
| Author(s): |
Okuhle Vonco
(Stellenbosch University, South Africa)
Jan Wium (Stellenbosch University, South Africa) |
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| Medium: | conference paper | ||||
| Language(s): | English | ||||
| Conference: | IABSE Congress: Resilient technologies for sustainable infrastructure, Christchurch, New Zealand, 3-5 February 2021 | ||||
| Published in: | IABSE Congress Christchurch 2020 | ||||
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| Page(s): | 920-926 | ||||
| Total no. of pages: | 7 | ||||
| DOI: | 10.2749/christchurch.2021.0920 | ||||
| Abstract: |
The paper describes a risk-based approach to enable construction teams to predict potential areas of rework. This is achieved by capturing historic construction data of concrete elements using Building Information Models (BIM), augmented by manual capturing by project parameters. The approach consists of two parts. In the first part data is captured of relevant project parameters that may impact on rework. This data is stored in a database and relationships are determined between these factors and the occurrence of rework using a machine learning approach. In a second part concrete elements in a BIM is verified against the database to determine the rework risk of the element. The approach will enable construction teams to pro-actively manage the construction process to reduce the probability of rework with resulting savings in time and cost. |
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| Keywords: |
concrete construction rework construction quality Building Information Models machine learning algorithms
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