Semantic Scholar Open Access 2020 211 sitasi

A machine learning approach to fracture mechanics problems

Xing Liu Christos E. Athanasiou N. Padture B. Sheldon Huajian Gao

Lihat Sumber DOI

Abstrak

Abstract Analytical and empirical solutions to engineering problems are usually preferred because of their convenience in applications. However, they are not always accessible in complex problems. A new class of solutions, based on machine learning (ML) models such as regression trees and neural networks (NNs), are proposed and their feasibility and value are demonstrated through the analysis of fracture toughness measurements. It is found that both solutions based on regression trees and NNs can provide accurate results for the specific problem, but NN-based solutions outperform regression-tree-based solutions in terms of their simplicity. This example demonstrates that ML solutions are a major improvement over analytical and empirical solutions in terms of both reliable functionality and rapid deployment. When analytical solutions are not available, the use of ML solutions can overcome the limitations of empirical solutions and substantially change the way that engineering problems are solved.

Topik & Kata Kunci

Materials Science

Penulis (5)

Xing Liu

Christos E. Athanasiou

N. Padture

B. Sheldon

Huajian Gao

Format Sitasi

APA MLA BibTeX

Liu, X., Athanasiou, C.E., Padture, N., Sheldon, B., Gao, H. (2020). A machine learning approach to fracture mechanics problems. https://doi.org/10.1016/j.actamat.2020.03.016

Akses Cepat

Lihat di Sumber doi.org/10.1016/j.actamat.2020.03.016

Informasi Jurnal

Tahun Terbit: 2020
Bahasa: en
Total Sitasi: 211×
Sumber Database: Semantic Scholar
DOI: 10.1016/j.actamat.2020.03.016
Akses: Open Access ✓