首页|改进Informer对GGBS混凝土长龄期抗压强度的评估

改进Informer对GGBS混凝土长龄期抗压强度的评估

扫码查看
为了致力于开发精确预测高炉矿渣(GGBS)混凝土长龄期抗压强度的方法.鉴于其动态趋势变化特性,选取Informer模型为基础,并从数据分解、编码器设计和损失函数优化三个角度进行创新改进.通过与原Informer、LSTM和Transformer模型的对比实验,证实了改进后的模型在预测精度和稳定性上的卓越表现.实验结果在R2,RMSE和MAE等指标上均表现出色.此外,还深入剖析了水泥、减水剂、高炉矿渣等关键成分对混凝土抗压强度的影响.这项研究不仅提升了预测精度,还为深度学习在建筑材料性能评估中的应用提供了重要参考,有助于推动该领域的发展.
Improved Informer Evaluation of Long-term Compressive Strength of GGBS Concrete
In order to develop a method for accurately predicting the compressive strength of blast furnace slag (GGBS)concrete over a long period of time.In view of its dynamic characteristics,we choose the Informer model as the basis,and make innovative improvements from three perspectives:da-ta decomposition,encoder design and loss function optimization.Compared with the original Informer, LSTM and Transformer models,it is proved that the improved model has excellent performance in pre-diction accuracy and stability.The results showed excellent performance in R2,RMSE and MAE.In addition,the influence of key components such as cement,water reducing agent and blast furnace slag on the compressive strength of concrete is deeply analyzed.This study not only improves the prediction accuracy,but also provides an important reference for the application of deep learning in the perform-ance evaluation of building materials,helping to promote the development of the field.

blast furnace slag concretecompressive strength at long agesimproved Informer modelperformance evaluation of building materials

袁志祥、沐先军

展开 >

安徽工业大学 计算机科学与技术学院,安徽 马鞍山 243000

安徽工业大学 工程研究院,安徽 马鞍山 243000

高炉矿渣混凝土 长龄期抗压强度 改进的Informer模型 建筑材料性能评价

国家自然科学基金安徽省高校科学研究重点项目

61806005KJ2021A0373

2024

佳木斯大学学报(自然科学版)
佳木斯大学

佳木斯大学学报(自然科学版)

影响因子:0.159
ISSN:1008-1402
年,卷(期):2024.42(7)
  • 4