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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.
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.
Multifidelity approach for data‐driven prediction models of structural behaviors with limited data
Chen, Shi‐Zhi (author) / Feng, De‐Cheng (author)
Computer‐Aided Civil and Infrastructure Engineering ; 37 ; 1566-1581
2022-10-01
16 pages
Article (Journal)
Electronic Resource
English
Stability Assessment of Concrete Gravity Dams via Multifidelity Surrogate Models
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