Evaluation of transfer learning models for predicting the lateral strength of reinforced concrete columns
Evaluation of transfer learning models for predicting the lateral strength of reinforced concrete columns
复制标题
DOI：
10.1016/j.engstruct.2022.114579
复制
发表时间：
2022-09
影响因子：
5.5
通讯作者：
H. Pak;S. Paal
中科院分区：
文献类型：
--
作者：
H. Pak;S. Paal
迁移学习旨在从一个或多个源任务中提取知识，并将知识应用于不同的任务，以实现更准确的预测。本研究的主要目的是探讨不同的知识转移技术，应用它们仅用少量的训练数据就能准确预测钢筋混凝土柱的抗侧力，并比较每种方法的可转移性。根据不同的源和目标域，设计了三个不同的试验，直接比较性能的横截面类型，抗剪钢筋面积，混凝土抗压强度。在本研究的所有情况下，知识转移技术显示出比没有任何知识转移技术训练的模型更好的预测性能。因此，我们可以得出结论，从源域转移预先训练的知识使模型能够更好地解释目标域中的响应变量。当目标域的可用数据很小时，性能的提高特别强调。因此，迁移学习可以成为解决结构工程中数据稀缺问题的一种方法。此外，传递预先训练的知识与源域和目标域之间的底层物理关系更相关，而与源域和目标域分布之间的差异更不相关。
Transfer learning aims to extract knowledge from one or more source tasks and apply the knowledge to a different task for more accurate predictions. The main purposes of this study are to investigate different knowledge transfer techniques, apply them to accurately predict the lateral strength of reinforced concrete columns with only a small amount of training data, and compare the transferability of each method. According to the various source and target domains, three different experiments are designed to directly compare the performance across section type, shear reinforcement area, and concrete compressive strength. In all cases in this study, knowledge transfer techniques show better prediction performance than the models trained without any knowledge transfer techniques. Therefore, we can conclude that transferring pre-trained knowledge from the source domain enables a model to better explain the response variable in the target domain. The performance improvement is particularly emphasized when the available data for the target domain is small. Thus, transfer learning can be one way to address the data scarcity problem in structural engineering. Furthermore, transferring the pre-trained knowledge is more associated with the underlying physical relationship between the source and the target domains and less associated with the discrepancy between the source and the target domain distributions.