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Journal Article
Integrated formwork removal decision framework for concrete slabs using FBG sensors and machine learning
- Authors
- Hyoung-Jun Park, Yubin Choe, Dae-Gil Kim, Inkyu Rhee
- Issue Date
- 2026-03
- Citation
- Frontiers in Materials, v.13, pp.180-180
- ISSN
- 2296-8016
- Publisher
- Frontiers Media S.A.
- Language
- English
- Type
- Journal Article
- Abstract
- Accurate prediction of early-age concrete strength is critical for ensuring construction safety and optimizing formwork removal schedules. This study presents an integrated decision-making framework for curing quality management, employing Fiber Bragg Grating (FBG) sensors to monitor internal temperature and drying shrinkage for 28 days. To validate hydration kinetics, a thermo-mechanical coupled analysis was conducted, and a machine learning framework using Gradient Boosting was explored to predict compressive strength. The results indicated that a literature-based dataset limited to 55 points led to an R2 approaching 1, revealing inherent overfitting due to reliance on ambient rather than internal core temperatures. These observed deviations highlight the necessity of in-situ monitoring. The study concludes that enhancing predictive robustness requires larger datasets synchronized with internal hydration heat records to mitigate overfitting in field applications.
- Keyword
- concrete maturity, concrete slab, coupled thermo-mechanical analysis, form removal, machine learning, fiber bragg grating sensor
- KSP Keywords
- Accurate prediction, Compressive Strength, Concrete slab, Concrete strength, Construction safety, Coupled analysis, Coupled thermo-mechanical analysis, Decision framework, Decision-making, Drying Shrinkage, Early-age concrete
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
