The prediction analysis of compressive strength and electrical resistivity of environmentally friendly concrete incorporating natural zeolite using artificial neural network

Shahmansouri, Amir Ali; Yazdani, Maziar; Hosseini, Mehdi; Bengar, Habib Akbarzadeh; Ghatte, Hamid Farrokh

doi:10.1016/j.conbuildmat.2021.125876

Cited by 49 publications

(18 citation statements)

References 77 publications

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“…However, the load-bearing capacity of GFRP-strengthened concrete deep beams decreased by increasing the a/h ratio from 1 to 1.7, as reported by Zinkaah et al [50] (Table 3). This can be owing to the fact that the concrete is a brittle material with low tensile strength [64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79][80][81][82][83]. When the a/h ratio and length of concrete deep beams increased, the concrete component was unable to carry the higher amount of the flexural load, leading to a decrease in the load-carrying resistance of the GFRP-strengthened concrete deep beams.…”

Section: Effect Of A/h Ratio On the Stress Distribution In The Deep B...mentioning

confidence: 99%

Effects of the Concrete Strength and FRP Reinforcement Type on the Non-Linear Behavior of Concrete Deep Beams

et al. 2022

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To provide sustainable reinforced concrete deep beams, the replacement of steel rebars by FRP rebars with high-chemical resistance is proposed by researchers. However, the effects of the concrete strength, top and web longitudinal reinforcements, and types of FRP flexural rebars on the non-linear performance of concrete deep beams have rarely been evaluated. This study numerically assessed the effects of the top and web longitudinal reinforcements and concrete strength on the non-linear behaviour of GFRP- and CFRP-strengthened concrete deep beams with various shear span-to-overall depth (a/h) ratios. As per the results, the highest tensile stress was obtained for the steel reinforcement, and the tensile stress in the CFRP reinforcement was more than that of the GFRP reinforcement under the failure load. Meanwhile, the results of high- and normal-strength concrete deep beams with the web reinforcement (16.4%) were lower than those without the web reinforcement (22.3%). Therefore, the web reinforcement moderately compensated for the low strength of normal concrete and the absence of the top longitudinal rebar to reinforce concrete deep beams in carrying the ultimate load. Furthermore, the participation of the GFRP reinforcement with the high-strength concrete was more than that with the normal-strength concrete in carrying a higher amount of loading.

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Section: Effect Of A/h Ratio On the Stress Distribution In The Deep B...mentioning

confidence: 99%

Effects of the Concrete Strength and FRP Reinforcement Type on the Non-Linear Behavior of Concrete Deep Beams

et al. 2022

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“…Natural mineral and energy resources are heavily utilized to construct building envelope components (such as roofing systems) and provide adequate indoor thermal comfort [22,[44][45][46][47][48][49][50][51][52][53]. For instance, consuming an estimated 40% of primary energy for construction sectors has produced a series of insoluble environmental concerns [54,55].…”

Section: Introductionmentioning

confidence: 99%

Water permeability, water retention capacity, and thermal resistance of green roof layers made with recycled and artificial aggregates

Kazemi

Courard

Attia

2023

Building and Environment

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“…A backpropagation neuron network model (BPNN) is one type of an artificial neuron networks (ANN), which assesses the differences between the predictions and labels, then propagates the errors to each neuron to adjust the weightings and biases to achieve the expected training goal. BPNN models or neural network-based models have been deeply studied and widely applied in predicting or estimating fresh or hardened properties of concrete or cementitious composites [ 30 , 31 , 32 , 33 ]. A simple BPNN model consists of three layers: an input layer, numerous hidden layers and an output layer, as shown in Figure 7 .…”

Section: Prediction Modelsmentioning

confidence: 99%

Prediction Models of Shielding Effectiveness of Carbon Fibre Reinforced Cement-Based Composites against Electromagnetic Interference

Narayanan

Zhang

Aslani

2023

Sensors

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With the rapid development of communication technology as well as a rapid rise in the usage of electronic devices, a growth of concerns over unintentional electromagnetic interference emitted by these devices has been witnessed. Pioneer researchers have deeply studied the relationship between the shielding effectiveness and a few mixed design parameters for cementitious composites incoporating carbon fibres by conducting physical experiments. This paper, therefore, aims to develop and propose a series of prediction models for the shielding effectiveness of cementitious composites involving carbon fibres using frequency and mixed design parameters, such as the water-to-cement ratio, fibre content, sand-to-cement ratio and aspect ratio of the fibres. A multi-variable non-linear regression model and a backpropagation neural network (BPNN) model were developed to meet the different accuracy requirements as well as the complexity requirements. The results showed that the regression model reached an R2 of 0.88 with a root mean squared error (RMSE) of 2.3 dB for the testing set while the BPNN model had an R2 of 0.96 with an RMSE of 2.64 dB. Both models exhibited a sufficient prediction accuracy, and the results also supported that both the regression and the BPNN model are reasonable for such estimation.

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The prediction analysis of compressive strength and electrical resistivity of environmentally friendly concrete incorporating natural zeolite using artificial neural network

Cited by 49 publications

References 77 publications

Effects of the Concrete Strength and FRP Reinforcement Type on the Non-Linear Behavior of Concrete Deep Beams

Effects of the Concrete Strength and FRP Reinforcement Type on the Non-Linear Behavior of Concrete Deep Beams

Water permeability, water retention capacity, and thermal resistance of green roof layers made with recycled and artificial aggregates

Prediction Models of Shielding Effectiveness of Carbon Fibre Reinforced Cement-Based Composites against Electromagnetic Interference

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