Sequential optimization method based on the adaptive Kriging model for the possibility-based design optimization

Wei, Ning; Lü, Zhenzhou

doi:10.1016/j.ast.2022.107939

Cited by 9 publications

(1 citation statement)

References 40 publications

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“…KS models are semiparametric surrogate models based on statistical theory 18,[56][57][58] . They accurately predict data samples within a domain, thereby providing additional predicted samples within the domain that have the same physical significance as real samples.…”

Section: Kriging-based Surrogate (Ks) Modelmentioning

confidence: 99%

Kriging-based surrogate data-enriching artificial neural network prediction of strength and permeability of permeable cement-stabilized base

Wang,

Xiao,

et al. 2024

Nat Commun

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Limited test data hinder the accurate prediction of mechanical strength and permeability of permeable cement-stabilized base materials (PCBM). Here we show a kriging-based surrogate model assisted artificial neural network (KS-ANN) framework that integrates laboratory testing, mathematical modeling, and machine learning. A statistical distribution model was established from limited test data to enrich the dataset through the combination of markov chain monte carlo simulation and kriging-based surrogate modeling. Subsequently, an artificial neural network (ANN) model was trained using the enriched dataset. The results demonstrate that the well-trained KS-ANN model effectively captures the actual data distribution characteristics. The accurate prediction of the mechanical strength and permeability of PCBM under the constraint of limited data validates the effectiveness of the proposed framework. As compared to traditional ANN models, the KS-ANN model improves the prediction accuracy of PCBM’s mechanical strength by 21%. Based on the accurate prediction of PCBM’s mechanical strength and permeability by the KS-ANN model, an optimization function was developed to determine the optimal cement content and compaction force range of PCBM, enabling it to concurrently satisfy the requirements of mechanical strength and permeability. This study provides a cost-effective and rapid solution for evaluating the performance and optimizing the design of PCBM and similar materials.

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Section: Kriging-based Surrogate (Ks) Modelmentioning

confidence: 99%