使用机器学习方法优化超高性能混凝土的碳排放.
Min Wang1, Mingfeng Du2, Yue Jia2
1China Merchants Chongqing Communications Technology Research and Design Institute Co., Ltd., Chongqing 400067, China.
Materials (Basel, Switzerland)
|April 13, 2024
概括
这项研究使用机器学习来优化超高性能混凝土 (UHPC) 混合比例,显著减少其碳足迹. 开发的模型为UHPC生产提供了一种可持续的方法.
科学领域:
- 材料科学 材料科学 材料科学
- 土木工程 土木工程是指土木工程.
- 可持续建筑 可持续建筑
背景情况:
- 超高性能混凝土 (UHPC) 是一个关键的研究领域,由于其优越的性能.
- 然而,UHPC生产具有高碳排放,与可持续发展目标相冲突.
- 优化UHPC混合设计对于减少环境影响至关重要.
研究的目的:
- 开发一种基于机器学习的战略,以优化UHPC混合比例.
- 为了减少与UHPC生产相关的显著碳排放.
- 为UHPC制造提供可持续的替代品.
主要方法:
- 人工神经网络 (ANN) 用于创建UHPC压力强度和沉流量的预测模型.
- 使用遗传算法 (GA) 在特定约束条件下最大限度地减少UHPC的碳排放.
- 进行了实验验证,以支持模型的预测和优化结果.
主要成果:
- 该ANN模型展示了高预测准确性UHPC属性,误差低于10%.
- 通过GA优化,UHPC的碳排放量成功降低到688kg/m3.
- 这项研究证实,通过实施的机器学习方法,可以获得巨大的环境效益.
结论:
- 机器学习模型,特别是ANN和GA,对于优化UHPC混合比例是有效的.
- 这种方法显著降低了HHPC的碳足迹,与可持续发展趋势保持一致.
- 开发的ML模型为未来的UHPC优化工作提供了一个强大的理论框架.
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