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 …
Application of bio and nature-inspired algorithms in agricultural engineering
The article reviewed the four major Bioinspired intelligent algorithms for agricultural
applications, namely ecological, swarm-intelligence-based, ecology-based, and multi …
applications, namely ecological, swarm-intelligence-based, ecology-based, and multi …
Influence of data splitting on performance of machine learning models in prediction of shear strength of soil
The main objective of this study is to evaluate and compare the performance of different
machine learning (ML) algorithms, namely, Artificial Neural Network (ANN), Extreme …
machine learning (ML) algorithms, namely, Artificial Neural Network (ANN), Extreme …
Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models
This study aims to implement a hybrid ensemble surrogate machine learning technique in
predicting the compressive strength (CS) of concrete, an important parameter used for …
predicting the compressive strength (CS) of concrete, an important parameter used for …
A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength
DJ Armaghani, PG Asteris - Neural Computing and Applications, 2021 - Springer
Despite the extensive use of mortars materials in constructions over the last decades, there
is not yet a reliable and robust method, available in the literature, which can estimate its …
is not yet a reliable and robust method, available in the literature, which can estimate its …
A novel integrated approach of augmented grey wolf optimizer and ANN for estimating axial load carrying-capacity of concrete-filled steel tube columns
The purpose of this study is to offer a high-performance machine learning model for
determining the ultimate load-carrying capability of concrete-filled steel tube (CFST) …
determining the ultimate load-carrying capability of concrete-filled steel tube (CFST) …
Prediction of compressive strength of geopolymer concrete using a hybrid ensemble of grey wolf optimized machine learning estimators
SK Parhi, SK Patro - Journal of Building Engineering, 2023 - Elsevier
Geopolymer concrete (GPC) has the potential to replace conventional concrete. But, the
mixed proportion of GPC poses several difficulties due to various contributing factors. The …
mixed proportion of GPC poses several difficulties due to various contributing factors. The …
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 …
Metaheuristic optimization of Levenberg–Marquardt-based artificial neural network using particle swarm optimization for prediction of foamed concrete compressive …
Foamed concrete (FC) shows advantageous applications in civil engineering, such as
reduction in dead loads, contribution to energy conservation, or decrease the construction …
reduction in dead loads, contribution to energy conservation, or decrease the construction …
Evaluation of the ultimate eccentric load of rectangular CFSTs using advanced neural network modeling
In this paper an Artificial Neural Network (ANN) model is developed for the prediction of the
ultimate compressive load of rectangular Concrete Filled Steel Tube (CFST) columns, taking …
ultimate compressive load of rectangular Concrete Filled Steel Tube (CFST) columns, taking …