Forecast flexural strength of pervious concrete by SVR

Ahmed, Farabi Bin; Mitu, Sadia Mannan; Biswas, Rajib Kumar; Ahsan, Khan Abid; Mim, Sabrina Mostarin; Ahmed, Shadab

doi:10.1016/j.matpr.2021.01.832

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…ANNs and SVR have been successfully applied in the field of architecture and achieved ideal results [ 46 , 47 , 48 , 49 , 50 ]. Whether they can accurately predict the axial compressive mechanical behaviour of FRP-confined circular concrete columns needs to be evaluated.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Mechanical Behaviours of FRP-Confined Circular Concrete Columns Using Artificial Neural Network and Support Vector Regression: Modelling and Performance Evaluation

et al. 2022

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The prediction and control of the mechanical behaviours of fibre-reinforced polymer (FRP)-confined circular concrete columns subjected to axial loading are directly related to the safety of the structures. One challenge in building a mechanical model is understanding the complex relationship between the main parameters affecting the phenomenon. Artificial intelligence (AI) algorithms can overcome this challenge. In this study, 298 test data points were considered for FRP-confined circular concrete columns. Six parameters, such as the diameter-to-fibre thickness ratio (D/t) and the tensile strength of the FRP (ffrp) were set as the input sets. The existing models were compared with the test data. In addition, artificial neural networks (ANNs) and support vector regression (SVR) were used to predict the mechanical behaviour of FRP-confined circular concrete columns. The study showed that the predictive accuracy of the compressive strength in the existing models was higher than the peak compressive strain for the high dispersion of material deformation. The predictive accuracy of the ANN and SVR was higher than that of the existing models. The ANN and SVR can predict the compressive strength and peak compressive strain of FRP-confined circular concrete columns and can be used to predict the mechanical behaviour of FRP-confined circular concrete columns.

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Section: Introductionmentioning

confidence: 99%

Prediction of Mechanical Behaviours of FRP-Confined Circular Concrete Columns Using Artificial Neural Network and Support Vector Regression: Modelling and Performance Evaluation

et al. 2022

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A Data-Driven Influential Factor Analysis Method for Fly Ash–Based Geopolymer Using Optimized Machine-Learning Algorithms

Cui

Huang

et al. 2022

J. Mater. Civ. Eng.

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. Study of properties of pervious concrete

Kravcov,

Makaeva,

Odenbakh

2024

Smart Composite in Construction

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Pervious concrete has the ability to permeate water through its structure, which makes it effective in creating drainage systems and culverts. The authors focus on analyzing the dependence of physical and mechanical properties of pervious concrete on formulation factors such as cement consumption, water-cement ratio and polypropylene fibre addition. The authors performed a complete two-factor experiment and regression analysis. It allows to determine the effect of each of the mentioned factors on the density, porosity and strength characteristics of the material. The results of the study show that the density and porosity of concrete samples, flexural and compressive strengths vary mainly with cement consumption. Changing the consumption of polypropylene fibre in the concrete mix has a negligible effect on strength but is important for improving other performance properties such as frost resistance. The study revealed that optimising the composition of pervious concrete, taking into account cement consumption and the addition of polypropylene fibre, allows the production of a construction material with the required strength and porosity values.

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Forecast flexural strength of pervious concrete by SVR

Cited by 3 publications

References 16 publications

Prediction of Mechanical Behaviours of FRP-Confined Circular Concrete Columns Using Artificial Neural Network and Support Vector Regression: Modelling and Performance Evaluation

Prediction of Mechanical Behaviours of FRP-Confined Circular Concrete Columns Using Artificial Neural Network and Support Vector Regression: Modelling and Performance Evaluation

A Data-Driven Influential Factor Analysis Method for Fly Ash–Based Geopolymer Using Optimized Machine-Learning Algorithms

. Study of properties of pervious concrete

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