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时间序列模型和算法的开发:对低碳混凝土材料的爬行预测

Zhengpeng Zhou¹, Houmin Li^1,2, Keyang Wu³

¹School of Civil Engineering, Architecture and the Environment, Hubei University of Technology, Wuhan 430068, China.

Materials (Basel, Switzerland)

|July 12, 2025

概括

这项研究引入了一种新的方法,用于使用时间序列数据和先进算法预测低碳混凝土的爬行. 增强的自适应皇冠猪优化算法 (ACCPO) 显著提高了预测准确性,ACCPO-LSTM 是最佳模型.

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:

低碳混凝土对于可持续发展至关重要,但它的爬行行为需要准确的预测.
传统的爬行特征表征方法很慢,不能完全捕捉多因素相互作用.
准确的爬行预测对于可持续混凝土材料的可靠应用至关重要.

研究的目的:

开发和验证一种新的时间建模框架,用于预测低碳混凝土的爬行.
通过使用先进的算法优化人工神经网络 (ANN),随机森林 (RF) 和长短期记忆 (LSTM) 模型来提高预测准确性.
确定在低碳混凝土材料中预测爬行最有效的模型.

主要方法:

为低碳混凝土的行为特征建立一个时间序列数据库.
对ANN,RF和LSTM模型进行重新训练,以进行爬行预测.
实施和验证一个增强的自适应冠状猪优化算法 (ACCPO) 用于模型优化.

主要成果:

ACCPO显著提高了ANN,RF和LSTM模型的性能.
在优化后,单一指标准确率达到95.9% (ANN),93.9% (RF) 和97.8% (LSTM).
实现了22.6% (ANN),7.9% (RF) 和8% (LSTM) 的错误减少,证实了模型的有效性.

结论:

拟议的时间建模框架是合理的,有效的爬行预测.
ACCPO算法在优化预测模型方面取得了显著的改进.
ACCPO-LSTM时间序列模型被认为是低碳混凝土爬行预测的优越选择.

关键词:

爬行效应爬行效应是一种爬行效应.低碳混凝土是一种低碳混凝土.机器学习优化优化优化算法优化算法时间依赖的爬行符合合规性.时间序列模型的模型.

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