Multifidelity approach for data-driven prediction models of structural behaviors with limited data

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.