تقرير
Probabilistic selection and design of concrete using machine learning
| العنوان: | Probabilistic selection and design of concrete using machine learning |
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| المؤلفون: | Forsdyke, Jessica C., Zviazhynski, Bahdan, Lees, Janet M., Conduit, Gareth J. |
| المصدر: | Data-Centric Engineering, 4, e9. (2023) |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science Condensed Matter |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Condensed Matter - Materials Science, Computer Science - Computational Engineering, Finance, and Science |
| الوصف: | Development of robust concrete mixes with a lower environmental impact is challenging due to natural variability in constituent materials and a multitude of possible combinations of mix proportions. Making reliable property predictions with machine learning can facilitate performance-based specification of concrete, reducing material inefficiencies and improving the sustainability of concrete construction. In this work, we develop a machine learning algorithm that can utilize intermediate target variables and their associated noise to predict the final target variable. We apply the methodology to specify a concrete mix that has high resistance to carbonation, and another concrete mix that has low environmental impact. Both mixes also fulfill targets on the strength, density, and cost. The specified mixes are experimentally validated against their predictions. Our generic methodology enables the exploitation of noise in machine learning, which has a broad range of applications in structural engineering and beyond. Comment: 21 pages (18 pages paper + 3 pages supplementary material) |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1017/dce.2023.5 |
| URL الوصول: | http://arxiv.org/abs/2304.11226 |
| رقم الأكسشن: | edsarx.2304.11226 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1017/dce.2023.5 |
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