Practical machine learning-based prediction model for axial capacity of square CFST columns

TT Le - Mechanics of Advanced Materials and Structures, 2022 - Taylor & Francis
In this paper, a surrogate Machine-Learning (ML) model based on Gaussian Process
Regression (GPR) was developed to predict the axial load of square concrete-filled steel …

Prediction of tensile strength of polymer carbon nanotube composites using practical machine learning method

TT Le - Journal of Composite Materials, 2021 - journals.sagepub.com
This paper is devoted to the development and construction of a practical Machine Learning
(ML)-based model for the prediction of tensile strength of polymer carbon nanotube (CNTs) …

A novel hybrid model based on a feedforward neural network and one step secant algorithm for prediction of load-bearing capacity of rectangular concrete-filled steel …

QH Nguyen, HB Ly, VQ Tran, TA Nguyen, VH Phan… - Molecules, 2020 - mdpi.com
In this study, a novel hybrid surrogate machine learning model based on a feedforward
neural network (FNN) and one step secant algorithm (OSS) was developed to predict the …

Investigation of force transmission, critical breakage force and relationship between micro-macroscopic behaviors of agricultural granular material in a uniaxial …

TT Le - Particulate Science and Technology, 2022 - Taylor & Francis
In this paper, numerical DEM simulations are presented that were used to study the
response of agricultural granular materials, such as dry soybeans, in a uniaxial compaction …

Role of packing defects in force networks of hexagonally packed structures using discrete element method

A Vijayan, A Banerjee, RK Desu - Granular Matter, 2022 - Springer
Packed structures are an essential part of nuclear reactors, food, chemical, transport, and
process industries. Since the safety and quality of products in the packed structures is of …