混凝土压力强度和沉降特征的先进预测建模:通过PSO优化的BPNN,SVM和RF模型的比较评估
Xuefei Chen1,2, Xiucheng Zhang1,2, Wei-Zhi Chen3
1School of Civil Engineering, Putian University, Putian 351100, China.
Materials (Basel, Switzerland)
|October 16, 2024
概括
这项研究开发了机器学习模型,以预测混凝土的压力强度和值. 逆向传播神经网络 (BPNN) 在预测强度方面表现出色,而随机森林 (RF) 在低迷预测方面表现最好.
科学领域:
- 土木工程 土木工程是指土木工程.
- 材料科学 材料科学 材料科学
- 计算科学 计算科学
背景情况:
- 准确地预测混凝土性能对于建筑而言至关重要.
- 传统的方法在预测复杂的物质行为时往往缺乏准确性.
- 优化混凝土混合设计需要可靠的性能预测.
研究的目的:
- 开发和比较机器学习模型,用于预测混凝土的压力强度和缩.
- 为每个绩效指标确定最准确的预测模型.
- 将优化模型集成到一个用于增强混凝土混合设计的框架中.
主要方法:
- 使用逆向传播神经网络 (BPNN),支持矢量机 (SVM) 和随机森林 (RF) 进行预测建模.
- 使用的粒子群集优化 (PSO) 具有超参数调整的交叉验证.
- 使用相关系数 (R),根平均平方误差 (RMSE) 和平均绝对误差 (MAE) 评估模型性能.
主要成果:
- 对于压力强度 (R=0.9531,RMSE=4.2568,MAE=2.6627) 的预测准确度,BPNN显示出优异的预测准确度.
- 随机森林 (RF) 在预测暴跌值方面表现最好 (R=0.8986,RMSE=9.4906,MAE=5.5034).
- 这两种模型都使用PSO和交叉验证进行了优化,以获得可靠的预测.
结论:
- 机器学习,特别是BPNN和RF,提供了一种强大的方法来预测具体的性能.
- 优化模型可以显著提高混凝土混合设计的准确性和效率.
- 这些预测工具有助于工程师开发针对特定建筑需求的定制混凝土配方.
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