Machine learning for structural engineering: A state-of-the-art review
HT Thai - Structures, 2022 - Elsevier
Abstract Machine learning (ML) has become the most successful branch of artificial
intelligence (AI). It provides a unique opportunity to make structural engineering more …
intelligence (AI). It provides a unique opportunity to make structural engineering more …
Prediction of axial load capacity of rectangular concrete-filled steel tube columns using machine learning techniques
This work aims to develop a novel and practical equation for predicting the axial load of
rectangular concrete-filled steel tubular (CFST) columns based on soft computing …
rectangular concrete-filled steel tubular (CFST) columns based on soft computing …
Machine learning-based prediction of CFST columns using gradient tree boosting algorithm
Among recent artificial intelligence techniques, machine learning (ML) has gained
significant attention during the past decade as an emerging topic in civil and structural …
significant attention during the past decade as an emerging topic in civil and structural …
Evaluation of multivariate linear regression and artificial neural networks in prediction of water quality parameters
H Zare Abyaneh - Journal of Environmental Health Science and …, 2014 - Springer
This paper examined the efficiency of multivariate linear regression (MLR) and artificial
neural network (ANN) models in prediction of two major water quality parameters in a …
neural network (ANN) models in prediction of two major water quality parameters in a …
Optimization of artificial intelligence system by evolutionary algorithm for prediction of axial capacity of rectangular concrete filled steel tubes under compression
Concrete filled steel tubes (CFSTs) show advantageous applications in the field of
construction, especially for a high axial load capacity. The challenge in using such structure …
construction, especially for a high axial load capacity. The challenge in using such structure …
Review on the use of artificial intelligence to predict fire performance of construction materials and their flame retardancy
The evaluation and interpretation of the behavior of construction materials under fire
conditions have been complicated. Over the last few years, artificial intelligence (AI) has …
conditions have been complicated. Over the last few years, artificial intelligence (AI) has …
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 …
Regression (GPR) was developed to predict the axial load of square concrete-filled steel …
Predicting the ultimate axial capacity of uniaxially loaded cfst columns using multiphysics artificial intelligence
The object of this research is concrete-filled steel tubes (CFST). The article aimed to develop
a prediction Multiphysics model for the circular CFST column by using the Artificial Neural …
a prediction Multiphysics model for the circular CFST column by using the Artificial Neural …
Application of ANN to the design of CFST columns
In this paper, artificial neural network (ANN) is used to predict the ultimate strength of
rectangular and circular concrete-filled steel tubular (CFST) columns subjected to concentric …
rectangular and circular concrete-filled steel tubular (CFST) columns subjected to concentric …
Novel fuzzy-based optimization approaches for the prediction of ultimate axial load of circular concrete-filled steel tubes
An accurate estimation of the axial compression capacity of the concrete-filled steel tubular
(CFST) column is crucial for ensuring the safety of structures containing them and preventing …
(CFST) column is crucial for ensuring the safety of structures containing them and preventing …