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.

中文翻译：

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有限数据结构行为数据驱动预测模型的多保真方法

基于大量高保真数据（如实验数据）的数据驱动方法在结构行为预测中变得普遍。然而，有时高保真数据很难获得，而且数量很少。同时，仿真结果等低保真数据量大，但精度较差，不适合建立模型。因此，基于机器学习 (ML) 算法，存在一种多保真方法，它可以提高多保真数据下的预测模型性能。首先介绍了该方法的基本理论和应用过程。然后以钢筋混凝土深梁抗剪承载力预测为例，验证了该方法的可行性。不同机器学习算法、低保真数据资源的影响，和高保真数据比率进行了彻底调查。结果表明，该方法将有效提高多保真数据下的模型准确性，并有可能成为促进解决结构工程中某些预测问题的替代方法。