基于机器学习的混凝土概率选择与设计

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jessica C. Forsdyke, Bahdan Zviazhynski, J. Lees, G. Conduit
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引用次数: 1

摘要

由于组成材料的自然变异性和多种可能的混合比例组合,开发具有较低环境影响的坚固混凝土混合料具有挑战性。通过机器学习进行可靠的性能预测可以促进基于性能的混凝土规范,减少材料效率低下,提高混凝土施工的可持续性。在这项工作中,我们开发了一种机器学习算法,该算法可以利用中间目标变量及其相关噪声来预测最终目标变量。我们应用该方法指定具有高抗碳化性能的混凝土混合料,以及具有低环境影响的另一种混凝土混合料。这两种混合物在强度、密度和成本上都达到了目标。根据他们的预测,实验验证了指定的混合物。我们的通用方法能够在机器学习中利用噪声,这在结构工程及其他领域具有广泛的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Probabilistic selection and design of concrete using machine learning
Abstract 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.
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
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