使用基于回归的机器学习方法,软计算技术用于预测原料大米混凝土的性能
Nakkeeran Ganasen1, L Krishnaraj1, Kennedy C Onyelowe2
1Department of Civil Engineering, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamil Nadu, 603203, India.
Scientific reports
|September 4, 2023
概括
这项研究使用回收材料开发了优化的生米混凝土,通过人工神经网络等机器学习模型在预测压力强度,水吸收和密度方面取得了高准确性.
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:
- 传统的混凝土生产对环境产生了重大影响.
- 回收材料,如原始大米皮,飞灰和水合石灰,为可持续建筑提供了潜力.
- 开发环保的混凝土替代品对于减少碳足迹至关重要.
研究的目的:
- 评估使用原始大米皮,飞灰和水合石灰作为混凝土中的细砂和水泥的替代品.
- 为了优化这些新的混凝土的压力强度,吸水能力和干燥密度.
- 使用机器学习技术验证优化模型的预测准确性.
主要方法:
- 使用响应表面方法 (RSM) 来优化混凝土的性能.
- 人工神经网络 (ANN) 和多重线性回归 (MLR) 用于模型验证.
- 进行了敏感性分析,以评估输入参数对结果的影响.
主要成果:
- 与MLR相比,ANN和RSM模型在预测混凝土的性能方面表现出更高的准确性.
- 高R平方值 (ANN的R2>0.9997,MLR的R2>0.9155) 证实了模型的可靠性.
- 优化的混凝土显示出有前途的机械性能,准确预测了28天的压力强度,吸水率和密度.
结论:
- 机器学习,特别是ANN与RSM相结合,提供了一种有效和准确的方法来预测混凝土的机械性质.
- 这种方法节省了土木工程研究和开发中的时间,劳动力和资源.
- 基于生米的混凝土是一种可行的可持续建筑材料.
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