Modeling Uniaxial Compressive Strength of Some Rocks from Turkey Using Soft Computing Techniques

AbstractThis study presents the application of soft computing techniques, namely, as multiple regressions (MRs), neural networks (NNs), genetic programming (GP), and adaptive neuro-fuzzy inference system (ANFIS) for modeling of compressive strength of carbon fiber-reinforced polymer (CFRP) confined concrete cylinders. The proposed soft computing models are based on experimental results collected from literature. They represent the ultimate strength of concrete cylinders after confinement with CFRP composites, which is in terms of diameter and height of the cylindrical specimen, ultimate circumferential strain in the CFRP jacket, elastic modulus of CFRP, unconfined concrete strength, and total thickness of CFRP layer used. The accuracy of the proposed soft computing models is very satisfactory compared to experimental results. Moreover, the results of proposed soft computing models are compared with five models existing in the literature proposed by various researchers so far and are found to be, by far, more accurate.

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Soft computing techniques: Systematic multiscale models to predict the compressive strength of HVFA concrete based on mix proportions and curing times

Journal of Building Engineering ◽

10.1016/j.jobe.2020.101851 ◽

2021 ◽

Vol 33 ◽

pp. 101851 ◽

Cited By ~ 2

Author(s):

Ahmed Mohammed ◽

Serwan Rafiq ◽

Parveen Sihag ◽

Rawaz Kurda ◽

Wael Mahmood

Keyword(s):

Compressive Strength ◽

Soft Computing ◽

Multiscale Models ◽

Soft Computing Techniques

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A Proposed Soft Computing Model for Ultimate Strength Estimation of FRP-Confined Concrete Cylinders

Applied Sciences ◽

10.3390/app10051769 ◽

2020 ◽

Vol 10 (5) ◽

pp. 1769 ◽

Cited By ~ 4

Author(s):

Reza Kamgar ◽

Hosein Naderpour ◽

Houman Ebrahimpour Komeleh ◽

Anna Jakubczyk-Gałczyńska ◽

Robert Jankowski

Keyword(s):

Compressive Strength ◽

Soft Computing ◽

Concrete Strength ◽

Fiber Reinforced Polymer ◽

Confined Concrete ◽

Confinement Pressure ◽

Reinforced Polymer ◽

Soft Computing Techniques ◽

Concrete Cylinders ◽

New Formulation

In this paper, the feed-forward backpropagation neural network (FFBPNN) is used to propose a new formulation for predicting the compressive strength of fiber-reinforced polymer (FRP)-confined concrete cylinders. A set of experimental data has been considered in the analysis. The data include information about the dimensions of the concrete cylinders (diameter, length) and the total thickness of FRP layers, unconfined ultimate concrete strength, ultimate confinement pressure, ultimate tensile strength of the FRP laminates and the ultimate concrete strength of the concrete cylinders. The confined ultimate concrete strength is considered as the output data, while other parameters are considered as the input data. These parameters are mostly used in existing FRP-confined concrete models. Soft computing techniques are used to estimate the compressive strength of FRP-confined concrete cylinders. Finally, a new formulation is proposed. The results of the proposed formula are compared to the existing methods. To verify the proposed method, results are compared with other methods. The results show that the described method can forecast the compressive strength of FRP-confined concrete cylinders with high precision in comparison with the existing formulas. Moreover, the mean percentage of error for the proposed method is very low (3.49%). Furthermore, the proposed formula can estimate the ultimate compressive capacity of FRP-confined concrete cylinders with a different type of FRP and arbitrary thickness in the initial design of practical projects.

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