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优化压力强度预测使用对抗性学习和混合正规化
Tamoor Aziz1, Haroon Aziz2, Srijidtra Mahapakulchai3
1Sirindhorn International Institute of Technology, Thammasat University, Pathum-Thani, Thailand. tamoor.azi@dome.tu.ac.th.
Scientific reports
|August 7, 2024
概括
本研究引入了一种使用生成对抗网络的新方法,以预测有限数据的混凝土压力强度. 该方法通过准确预测回收聚合物的材料性能来增强可持续建筑.
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
- 人工智能的人工智能
背景情况:
- 基础设施开发大大消耗自然资源,产生建筑垃圾,对环境产生影响.
- 在使用回收聚合物时,混凝土的内在性质的恶化使废弃材料的可持续性受到挑战.
- 使用回收聚合物准确预测混凝土的压力强度至关重要,但数据采集是昂贵和耗时的.
研究的目的:
- 用有限的数据开发一种新的方法来预测混凝土的压力强度.
- 利用生成对抗网络 (GAN) 进行合成数据生成,以克服数据稀缺.
- 提高可持续建筑材料压力强度预测的准确性和可靠性.
主要方法:
- 用于合成数据生成的生成对抗网络 (GAN).
- 混合训练策略利用常规或启发式损失函数来防止模型过拟合.
- 从多变量正常分布中嵌入随机噪声到训练样本中以捕获数据变异.
- 灵敏度分析以确定影响压力强度预测的关键特征.
主要成果:
- 回收粗聚合物的尺寸和含水量被确定为最重要的预测特征.
- 超级可塑化剂,回收粗聚合物密度和吸水率显示出尽管相关性较低,但有显著的预测贡献.
- 提出的基于GAN的方法表现优于随机森林,支持向量回归,人工神经网络和自适应增强.
- 实现了7.97的平均平方误差,根的平均平方误差为2.82,平均绝对误差为2.13,确定系数为0.96.
结论:
- 拟议的混合训练GAN方法有效地预测了有限的数据的混凝土压力强度.
- 这种技术支持可持续建筑,使回收聚合物能够准确地评估其性能.
- 这些发现为优化在基础设施项目中使用回收材料提供了有价值的工具.
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