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 …
Recent trends in prediction of concrete elements behavior using soft computing (2010–2020)
M Mirrashid, H Naderpour - Archives of Computational Methods in …, 2021 - Springer
Soft computing (SC), due to its high abilities to solve the complex problems with uncertainty
and multiple parameters, has been widely investigated and used, especially in structural …
and multiple parameters, has been widely investigated and used, especially in structural …
Estimation of the compressive strength of concretes containing ground granulated blast furnace slag using hybridized multi-objective ANN and salp swarm algorithm
The use of supplementary cementitious materials such as ground granulated blast furnace
slag (GGBFS) in concrete mixtures provides many technical and economic benefits. The use …
slag (GGBFS) in concrete mixtures provides many technical and economic benefits. The use …
Predicting the compressive strength of self‐compacting concrete containing Class F fly ash using metaheuristic radial basis function neural network
The use of Class F fly ash (CFFA) as a partial replacement of cement in the concrete mixture
can provide a wide variety benefits such as improving the mechanical properties, reducing …
can provide a wide variety benefits such as improving the mechanical properties, reducing …
BP-ANN based bond strength prediction for FRP reinforced concrete at high temperature
L Huang, J Chen, X Tan - Engineering Structures, 2022 - Elsevier
In view of the difficulty in establishing a mathematical model to characterize the interfacial
performance of FRP reinforced concrete at high temperature, the back-propagation neural …
performance of FRP reinforced concrete at high temperature, the back-propagation neural …
State-of-the-art AI-based computational analysis in civil engineering
C Wang, L Song, Z Yuan, J Fan - Journal of Industrial Information …, 2023 - Elsevier
With the informatization of the building and infrastructure industry, conventional analysis
methods are gradually proving inadequate in meeting the demands of the new era, such as …
methods are gradually proving inadequate in meeting the demands of the new era, such as …
Explicit neural network model for predicting FRP-concrete interfacial bond strength based on a large database
This study builds a large database from an extensive survey of existing single-lap shear
tests on fiber-reinforced polymer (FRP)-concrete interfacial bonds, comprising 969 test …
tests on fiber-reinforced polymer (FRP)-concrete interfacial bonds, comprising 969 test …
Interpretable machine learning algorithms to predict the axial capacity of FRP-reinforced concrete columns
Fiber-reinforced polymer (FRP) rebars are increasingly being used as an alternative to steel
rebars in reinforced concrete (RC) members due to their excellent corrosion resistance …
rebars in reinforced concrete (RC) members due to their excellent corrosion resistance …
Evaluation and prediction of bond strength of GFRP-bar reinforced concrete using artificial neural network optimized with genetic algorithm
Assessment of bond behavior of glass fiber-reinforced polymer (GFRP) bars to concrete
plays an important role in design and implementation of the polymer-matrix composites …
plays an important role in design and implementation of the polymer-matrix composites …
Estimation of the FRP-concrete bond strength with code formulations and machine learning algorithms
The present study pertains to the bond strength and development length of FRP bars
embedded in concrete. The experimental results in the literature were compared to the …
embedded in concrete. The experimental results in the literature were compared to the …