Multifidelity approach for data-driven prediction models of structural behaviors with limited data
Multifidelity approach for data-driven prediction models of structural behaviors with limited data
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DOI：
10.1111/mice.12817
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发表时间：
2022-01-18
影响因子：
9.6
通讯作者：
Feng, De-Cheng
中科院分区：
文献类型：
--
作者：
Chen, Shi-Zhi;Feng, De-Cheng
The data-driven approach based on plenty of high-fidelity data such as experimental data becomes prevalent in the prediction of structural behavior. However, sometimes the high-fidelity data are hard to obtain and are only in small amount. Meanwhile, the low-fidelity data like simulation result are in large amount but their accuracy is relatively poor and are not suitable for establishing models. Thus, based on machine learning (ML) algorithms a multifidelity approach is present, which can enhance the prediction models performance under multifidelity data. First the basic theory and application procedure of this approach are introduced. Then a case study for predicting the shear capacity of reinforced concrete deep beams was carried out to validate this method's feasibility. The influence of different ML algorithms, low-fidelity data resources, and high-fidelity data ratios were thoroughly investigated. The results showed that this approach would effectively promote a models accuracy under multifidelity data and has the potential to be an alternative to facilitate solving some prediction issues in structural engineering.
