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 …
A review of prediction methods for global buckling critical loads of pultruded FRP struts
H Zhang, F Li - Composite Structures, 2024 - Elsevier
Pultruded fiber-reinforced composites (FRP) are widely used in structural engineering due to
their excellent properties along the fiber direction. Its global buckling performance has …
their excellent properties along the fiber direction. Its global buckling performance has …
[HTML][HTML] Prediction of shear capacity of RC beams strengthened with FRCM composite using hybrid ANN-PSO model
The aim of this study is to develop a hybrid Artificial Neural Network-Particle Swarm
Optimization (ANN-PSO) model for improving shear strength prediction of reinforced …
Optimization (ANN-PSO) model for improving shear strength prediction of reinforced …
[HTML][HTML] Machine learning models for the elastic-critical buckling moment of sinusoidal corrugated web beam
The torsional stiffness of I-beams with sinusoidal corrugated web is higher than that of flat
web beams and the accuracy of the available hand-calculation methods to determine the …
web beams and the accuracy of the available hand-calculation methods to determine the …
A Machine Learning‐Based Model for Predicting Atmospheric Corrosion Rate of Carbon Steel
The purpose of this study is to develop a practical artificial neural network (ANN) model for
predicting the atmospheric corrosion rate of carbon steel. A set of 240 data samples, which …
predicting the atmospheric corrosion rate of carbon steel. A set of 240 data samples, which …
Prediction of axial compression capacity of cold-formed steel oval hollow section columns using ANN and ANFIS models
The steel oval hollow section (OHS) provides an aesthetic architecture and a greater local
buckling strength. However, the existing design codes do not specify the effective width in …
buckling strength. However, the existing design codes do not specify the effective width in …
Prediction of speed limit of cars moving on corroded steel girder bridges using artificial neural networks
This paper develops an artificial neural network (ANN) model for predicting the speed limit of
cars moving on corroded steel girder bridges. A total of 311 datasets, which are created from …
cars moving on corroded steel girder bridges. A total of 311 datasets, which are created from …
Improving axial load-carrying capacity prediction of concrete columns reinforced with longitudinal FRP bars using hybrid GA-ANN model
This study aims to develop a hybrid machine learning model, so-called Genetic algorithm–
Artificial neural network (GA-ANN), for efficiently predicting the axial load-carrying capacity …
Artificial neural network (GA-ANN), for efficiently predicting the axial load-carrying capacity …
[HTML][HTML] Neural network models for the critical bending moment of uniform and tapered beams
C Couto - Structures, 2022 - Elsevier
Most design standards require the calculation of the elastic critical bending moment for the
design and verification of steel beams. Formulae exists for uniform beams with double or …
design and verification of steel beams. Formulae exists for uniform beams with double or …
Shear strength prediction of concrete beams reinforced with FRP bars using novel hybrid BR-ANN model
Shear strength is a very important parameter in designing of reinforced concrete beams or
concrete beams reinforced with fiber-reinforced polymer (FRP) bars. So far, numerous …
concrete beams reinforced with fiber-reinforced polymer (FRP) bars. So far, numerous …