An adaptive data-driven algorithm with machine learning for modeling high-order nonlinear beam-column finite elements
Year: 2024
Type: article
Abstract: Structural analyses of buildings using beam-column finite elements are typically performed using procedures based on rotation polynomials truncated to the second order. This truncation will lead to inaccurate results that must be reduced. For this purpose, this study develops straightforward high-order Hermitian interpolation function matrices for Timoshenko elements. Moreover, a novel distributed plasticity hinge is devised. However, the structure complexity and the round-off error will increase with the interpolation function order. Nonetheless, a p -adaptive procedure can alleviate this problem. In contrast to other research, a technique for removing outliers for the maximum permissible error in the p -adaptive method is used to obtain results in a discrete space instead of using integrals in the used norm, which is more consistent with the theory. Moreover, as with other studies, it has been noticed that naïve p -adaptive processes diverge because of oscillations in the iterations. The proposed algorithm groups elements to be refined in different stages, employing the clustering function of the K-means method. Additionally, it is demonstrated that using fourth-order beam-column elements is also inefficient in elements that must be refined. After showing the theoretical base, this investigation's importance is proved with examples of buildings of different complexities.
Source: Journal of Building Engineering
Authors: Edgar David Mora Martinez, Naser Khaji
Institution: Hiroshima University
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,320
Award: G2299367249
OpenAlex ID: W4404516626