在自愈混凝土中使用机器学习预测裂纹修复百分比
Hossein Khosravi1, Peyman Kiani2, Mohammad Bahram2
1Department of Civil Engineering, Hakim Sabzevari University, Sabzevar, Iran. h.khosravi@hsu.ac.ir.
Scientific reports
|December 9, 2025
概括
研究人员开发了人工智能模型,以预测混凝土的自我愈合. 这些模型提供了一种更快,更可靠的方法来评估混凝土的耐用性,有助于创建可持续的建筑材料.
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.
- 人工智能的人工智能
背景情况:
- 混凝土对微裂纹的易感性损害了耐用性和结构完整性.
- 自愈混凝土提供了一个可持续的解决方案,但其评估受到昂贵和耗时的实验方法的阻碍.
- 缺乏标准化的方法限制了自愈混凝土的实际实施.
研究的目的:
- 开发和评估混合人工智能模型,用于预测自愈混凝土裂愈合.
- 解决传统实验方法的局限性,以评估自我愈合的有效性.
- 为增强基于水泥的材料的耐用性和可持续性提供强大的框架.
主要方法:
- 开发了三种混合预测模型:通过遗传算法 (GA) 优化的人工神经网络 (ANN),粒子群优化 (PSO) 和莱文伯格-马奎特 (LM) 算法.
- 使用多个统计指数评估模型性能.
- 混合模型与参考研究进行比较 (Zhuang等. ) 的情况.
主要成果:
- 与参考研究相比,所有三个开发的混合模型都表现出优异的性能.
- 用莱文伯格-马奎特算法 (ANN-LM) 优化的人工神经网络实现了最高的预测准确性.
- 这项研究证实了将优化算法与ANN集成为预测混凝土自我愈合的有效性.
结论:
- 混合人工智能模型为预测自愈混凝土裂愈合百分比提供了可靠和高效的框架.
- 这些模型可以大大帮助设计和改进耐用和可持续的建筑材料.
- 这些发现为更实践地实施自愈混凝土技术铺平了道路.
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