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 …
Deep learning-based procedure for structural design of cold-formed steel channel sections with edge-stiffened and un-stiffened holes under axial compression
This paper proposes a framework of deep belief network (DBN) for studying the structural
performance of cold-formed steel (CFS) channel sections with edge-stiffened/un-stiffened …
performance of cold-formed steel (CFS) channel sections with edge-stiffened/un-stiffened …
Mechanical features and durability of concrete incorporating recycled coarse aggregate and nano-silica: Experimental study, prediction, and optimization
F Rezaei, A Memarzadeh, MR Davoodi… - Journal of Building …, 2023 - Elsevier
The mechanical features of concrete made with multiple quantities of recycled coarse
aggregate replacing natural gravel were investigated in this work. In addition, behavior …
aggregate replacing natural gravel were investigated in this work. In addition, behavior …
Effect of web perforations on end-two-flange web crippling behaviour of roll-formed aluminium alloy unlipped channels through experimental test, numerical …
Aluminium alloy has recently become popular in New Zealand's construction sector as a
sustainable building material. However, in roll-formed aluminium alloy (RFA) channel …
sustainable building material. However, in roll-formed aluminium alloy (RFA) channel …
Real-time prediction of structural fire responses: A finite element-based machine-learning approach
Finite Element (FE) methods have been widely adopted in structural design to analyze the
mechanical performance quantitatively, while most comprehensive FE models are …
mechanical performance quantitatively, while most comprehensive FE models are …
Predicting the splitting tensile strength of concrete using an equilibrium optimization model
Splitting tensile strength (STS) is an important mechanical parameter of concrete. This study
offers novel methodologies for the early prediction of this parameter. Artificial neural network …
offers novel methodologies for the early prediction of this parameter. Artificial neural network …
Evaluation of machine learning models for load-carrying capacity assessment of semi-rigid steel structures
The paper investigates the potential application of machine learning methods to estimate the
load-carrying capacity of semi-rigid connected steel structures. The database is developed …
load-carrying capacity of semi-rigid connected steel structures. The database is developed …
Predicting real-time deformation of structure in fire using machine learning with CFD and FEM
Real-time prediction of structural safety conditions is critical to firefighting teams during
building fire rescue operations. This paper presents a numerical data-based machine …
building fire rescue operations. This paper presents a numerical data-based machine …
Predicting tensile strength of spliced and non-spliced steel bars using machine learning-and regression-based methods
H Dabiri, A Kheyroddin, A Faramarzi - Construction and Building Materials, 2022 - Elsevier
Mechanical properties of steel reinforcement bars, which have a critical effect in the overall
performance of reinforced concrete (RC) structures, should be reported and assessed …
performance of reinforced concrete (RC) structures, should be reported and assessed …
Prediction of shear capacity of steel channel sections using machine learning algorithms
This study presents the application of popular machine learning algorithms in prediction of
the shear resistance of steel channel sections using experimental and numerical data …
the shear resistance of steel channel sections using experimental and numerical data …