Prediction of Axial Compressive Strength for FRP-Confined Concrete Compression Members

Raza, Ali; Khan, Qaiser uz Zaman; Ahmad, Afaq

doi:10.1007/s12205-020-1682-x

Cited by 42 publications

(19 citation statements)

References 23 publications

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“…The use of fiber and plastic makes the FRP high corrosion resistance and suitable for use in undersea structures [8]. The applications of FPRs are not limited to RC building members but are also extended to RC bridge piers, girders, and slabs to enhance their strength at ULS [9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Properties of FRP-Confined Concrete Cylinders Based on Artificial Neural Networks

2020

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Recently, the use of fiber-reinforced polymers (FRP)-confinement has increased due to its various favorable effects on concrete structures, such as an increase in strength and ductility. Therefore, researchers have been attracted to exploring the behavior and efficiency of FRP-confinement for concrete structural elements further. The current study investigates improved strength and strain models for FRP confined concrete cylindrical elements. Two new physical methods are proposed for use on a large preliminary evaluated database of 708 specimens for strength and 572 specimens for strain from previous experiments. The first approach is employing artificial neural networks (ANNs), and the second is using the general regression analysis technique for both axial strength and strain of FRP-confined concrete. The accuracy of the newly proposed strain models is quite satisfactory in comparison with previous experimental results. Moreover, the predictions of the proposed ANN models are better than the predictions of previously proposed models based on various statistical indices, such as the correlation coefficient (R) and mean square error (MSE), and can be used to assess the members at the ultimate limit state.

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Section: Introductionmentioning

confidence: 99%

Prediction of Properties of FRP-Confined Concrete Cylinders Based on Artificial Neural Networks

2020

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“…e present work aims to compare the results obtained by using Compressive Force Path method (CFP), Current Design Codes (CDCs), and Artificial Neural Network (ANN) for estimating the response of the reinforced concrete (RC) Members at ULR. e comparative study exhibited that these codes predicted the underestimated values [11,[40][41][42][43][44][45][46][47][48]. erefore, in this study, ANN models have been developed for predicting real load-carrying capacity of reinforced concrete flat slab.…”

Section: Introductionmentioning

confidence: 96%

“…In this work, response of both complex and simple reinforced concrete members at ULR has been assessed by using CDC, CFP, and ANN techniques. In previous studies, ANNs have been used to estimate material behavior [36][37][38][39] as well as response of RC members [11,[40][41][42][43][44][45][46][47][48]. ANNs have achieved the advanced attention of the researcher for solving the problems, especially for estimating the ULR of composite concrete members (CCM) [49][50][51].…”

Section: Introductionmentioning

confidence: 99%

Knowledge-Based Prediction of Load-Carrying Capacity of RC Flat Slab through Neural Network and FEM

Ahmad

Arshid

Mahmood

et al. 2021

Mathematical Problems in Engineering

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The present research work aims to compare the results for predicting the ultimate response of Reinforced Concrete (RC) members using Current Design Codes (CDCs), an alternative method based on the Compressive Force Path (CFP) method, and Artificial Neural Network (ANN). For this purpose, the database of 145 samples of RC Flat Slab with the simple supported condition under concentrated load is developed from the latest published work. All the cases studied were Square Concrete Slabs (SCS). The critical parameters used as input for the study were column dimension, cs, depth of the slab, ds, shear span ratio, a v s / d , longitudinal percentage steel ratio, ρls, yield strength of longitudinal steel, fyls, the compressive strength of concrete, fcs, and ultimate load-carrying capacity, Vus. Seven ANN models were trained using different combinations of input parameters and different points of hidden neurons with different activation functions. The results exhibited that SCS-4 was the most optimized ANN model, having the maximum value of R (89%) with the least values of MSE (0.62%) and MAE (6.2%). It did not only reduce the error but also predicted accurate results with the least quantity of input parameters. The predictions obtained from the studied models (i.e., CDCs, CFP, and ANN) exhibited that results obtained using the ANNs model correlated well with the experimental data. Furthermore, the FEM results for the selected cases show the closer result to the ANN predictions.

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“…The CFP based on the member response included the critical region of the member between points of the counter flexural, unlike the sectional approach of the TA, as shown in Figure 1. The supports points are denoted by A, B, and D and vertical upward load is applied on C. With the computer science advancements in the last two decades, many researchers have proposed the use of soft computing (SC) methods to solve complex problems [15][16][17][18][19][20][21][22][23][24][25][26]. Artificial neural networks (ANNs), response surface methodology (RSM), fuzzy logic (FL), particle swarm optimization (PSO) and genetic algorithms (GAs) are among the most popular SC methods [27].…”

Section: Introductionmentioning

confidence: 99%

Neural Network-Based Prediction: The Case of Reinforced Concrete Members under Simple and Complex Loading

2021

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The objective of this study is to compare conventional models used for estimating the load carrying capacity of reinforced concrete (RC) members, i.e., Current Design Codes (CDCs), with the method based on different assumptions, i.e., the Compressive Force Path (CFP) method and a non-conventional problem solver, i.e., an Artificial Neural Network (ANN). For this purpose, four different databases with the details of the critical parameters of (i) RC beams in simply supported conditions without transverse steel or stirrups (BWOS) and RC beams in simply supported conditions with transverse steel or stirrups (BWS), (ii) RC columns with cantilever-supported conditions (CWA), (iii) RC T-beams in simply supported conditions without transverse steel or stirrups (TBWOS) and RC T-beams in simply supported conditions with transverse steel or stirrups (TBWS) and (iv) RC flat slabs in simply supported conditions under a punching load (SCS) are developed based on the data from available experimental studies. These databases obtained from the published experimental studies helped us to estimate the member response at the ultimate limit-state (ULS). The results show that the predictions of the CFP and the ANNs often correlate closer to the experimental data as compared to the CDCs.

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Prediction of Axial Compressive Strength for FRP-Confined Concrete Compression Members

Cited by 42 publications

References 23 publications

Prediction of Properties of FRP-Confined Concrete Cylinders Based on Artificial Neural Networks

Prediction of Properties of FRP-Confined Concrete Cylinders Based on Artificial Neural Networks

Knowledge-Based Prediction of Load-Carrying Capacity of RC Flat Slab through Neural Network and FEM

Neural Network-Based Prediction: The Case of Reinforced Concrete Members under Simple and Complex Loading

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