使用优化深度学习和大型语言模型预测混凝土的压力强度
Safaa Zaman1, Marwa M Eid2, Ebrahim A Mattar3
1Information Sciences Department, College of Life Sciences, Kuwait University, Kuwait City, Kuwait.
Scientific reports
|February 26, 2026
概括
本研究引入了一种新的AI框架,将iHow优化算法 (iHowOA) 和空间时间图卷积网络 (STGCN) 结合起来,以准确预测混凝土的压力强度,改进可持续的建筑材料设计.
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:
- 预测混凝土的压力强度对于可持续建筑至关重要.
- 混合物成分,添加剂和固化条件之间的复杂相互作用带来了挑战.
- 现有的方法经常与这些相互作用的非线性性质作斗争.
研究的目的:
- 开发一个先进的混合人工智能框架,用于增强混凝土压力强度预测.
- 提高建筑材料预测模型的准确性和稳定性.
- 为材料科学应用利用新的优化和深度学习技术.
主要方法:
- 将iHow优化算法 (iHowOA) 与时空图形卷积网络 (STGCN) 的集成.
- 使用大型语言模型 (LLM) 进行数据预处理,包括语义验证和特征协调.
- 使用iHowOA的自适应性决策和知识获取能力优化STGCN架构.
- 基于图形的建模,以捕捉空间依赖性和时间力量演变.
主要成果:
- 拟议的iHowOA-STGCN框架与其他十个元启发式优化器相比,显示出优异的预测性能.
- 在公开数据集上实现了较低的预测错误和更高的相关系数.
- 确定了水泥特性,与年龄相关的强度增强和物理化学相互作用之间的关键关系.
结论:
- iHowOA-STGCN框架提供了一个有前途的数据驱动决策支持工具,用于具体的强度预测.
- 基于LLM的预处理提高了数据质量和模型输入的稳定性.
- 建议对各种数据集进行进一步的验证,以确认在现实场景中可概括性和实际适用性.
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