Transfer (machine) learning approaches coupled with target data augmentation to predict the mechanical properties of concrete

Machine Learning with Applications

Aditya Kumar¹  Narayanan Neithalath²  Gaurav Sant³  Kailasnath Maneparambil⁴  Emily Ford⁵ 

[1] Computer Science and Engineering, Arizona State University, Tempe, AZ 85287, United States of America;Department of Civil and Environmental Engineering, University of California Los Angeles, Los Angeles, CA 90095, United States of America;Department of Materials Science and Engineering, Missouri University of Science and Technology, Rolla, MO 65409, United States of America;Intel Corporation, Chandler, AZ 85224, United States of America;School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85287, United States of America;

关键词: Machine learning;    Transfer learning;    Data augmentation;    Elastic modulus;    Compressive strength;    High performance concrete;   

DOI  :  

来源: DOAJ

【 摘 要 】

Transfer learning, a machine learning technique which employs prior knowledge from solving a source problem to solve a related target problem, is utilized in this work to predict the compressive strength and modulus of elasticity of different concrete mixtures. The use of data augmentation through empirical models to estimate missing data outputs allows for the use of inductive parameter transfer-learning artificial neural network (ANN) models for fast convergence. The paper considers two distinct cases: one where the domain of the target lies somewhat outside that of the source — termed domain expansion, and another where the target output (e.g., elastic modulus) is different, but related to the source output (e.g., compressive strength) — termed domain adaptation. Transfer learning is found to be most accurate when the source dataset is more complex than the target dataset, since more features could be learned. Data augmentation and the coupling of traditional machine learning with transfer learning are demonstrated to greatly enhance the predictive capability for important concrete properties, from mixture proportions. Limited experimental data can be used to transfer-learn the properties (output) of a new dataset from a reliable source model for a related system.

【 授权许可】

Unknown