Keywords: evolutionary algorithm, cubic regression spline, computational material model, machine learning for heterogeneity, machine learning for varying boundary conditions, nonlinear analysis of reinforced concrete structures.

Recently, computational material models accelerated innovations by harnessing machine learning (ML) methods, but they face challenges. It is difficult to incorporate internal heterogeneity and diverse boundary conditions (BC’s) into existing ML methods, and weak interpretability of ML poses challenges. This paper generalizes a recently developed self-evolving computational material models framework built upon physics-ingrained ML-friendly new features via information convolution and the Bayesian evolutionary algorithm. This paper proposes a new material-specific information index (II), which is capable of autonomously quantifying the internal heterogeneity and diverse BC’s. Also, this paper introduces highly flexible cubic regression spline (CRS)-based link functions which can offer mathematical expressions of salient material coefficients of the existing computational material models in terms of convolved II. Thereby, this paper suggests a novel means by which ML can directly leverage internal heterogeneity and diverse BC’s to autonomously evolve computational material models while keeping interpretability. Validations using a wide spectrum of large-scale reinforced composite structures confirm the favorable performance of the generalization. Example expansions of nonlinear shear of quasi-brittle materials and progressive compressive buckling of reinforcing steel underpin efficiency and accuracy of the generalization. This paper adds a meaningful avenue for accelerating the fusion of computational material models and ML.

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