Lightweight Ferrocement Matrix Compressive Behavior: Experiments Versus Finite Element Analysis

Madadi, Amirhossein; Eskandari‐Naddaf, Hamid; Gharouni-Nik, Morteza

doi:10.1007/s13369-017-2557-4

Cited by 16 publications

(5 citation statements)

References 44 publications

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“…As the prediction of foundation behavior under various conditions can improve its structural performance, the present work aimed to use the FEM to accurately estimate the behavior of the concrete foundation under dynamic loads with different materials. To this end, the Abaqus Unified FEA which is a finite element-based software and a powerful tool to investigate the behavior of concrete structures [20] was applied in this study. It is noteworthy that due to the possible imbalance of the fan, the load applied to the foundation is generally dynamic unless a damper or isolator is used.…”

Section: Abstract: Concrete Foundation Compressive Strength Loadingmentioning

confidence: 99%

Lifetime Analysis on Centrifugal ID Fan Foundation in Cement Plants

Eskandari‐Naddaf

Gharouni-Nik

Pakzad

2018

IJIE

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Section: Abstract: Concrete Foundation Compressive Strength Loadingmentioning

confidence: 99%

Lifetime Analysis on Centrifugal ID Fan Foundation in Cement Plants

Eskandari‐Naddaf

Gharouni-Nik

Pakzad

2018

IJIE

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“…Concrete with a similar shape to pozzolan is mixed with a different amount of cement, changing how practical concrete behaves [17]- [20]. Because of the silica fumes' small particle size, it can serve as both a filler and a pozzolan in the concrete mixture [21].…”

Section: Introductionmentioning

confidence: 99%

Modeling the Compressive Strength of Ultra-High Performance Concrete (Uhpc) by a Machine Learning Technique Optimized by Novel Algorithm

Zuo,

Liu,

Gao

et al. 2023

CEJ

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The Ultra-High Performance Concrete (UHPC) as an efficient material in constructional projects needs to be investigated in terms of ingredients and their magnitudes to compute the compressive strength of concrete. Empirical determination of relationships between constituents can demand more energy and cost. At the same time, the intelligent systems have enabled us to appraise the compressive strength based on ingredients' composition. Also, choosing eco-friendly materials in concrete as one of the widely-used items worldwide should be encouraged. This study has attempted to model the compressive strength of UHPC. Support Vector Regression (SVR), as a machine learning technique aligned with the Particle Swarm Optimization (PSO) and Henry's Gas Solubility Optimization (HGSO), have been used to simulate the compressive strength of concrete calculated based on different materials used in the present article. Eight constituents were tested to generate the compressive strength values. Various metrics were used to evaluate the modeling process. In this regard, the R2 of test phase modeling for SVR-HGSO was obtained 0.960 while for SVR-PSO, 0.925. In the training stage, the correlation rate was computed 0.902 for SVR-HGSO, which is 1.7 percent higher than SVR-PSO with R2 0.887.

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“…Repetition of this process leads to formation of microcracks, breakdown of wall structure, and damage to concrete or mortar . Damage to concrete due to freezing and thawing also deteriorates its mechanical properties and fracture behavior . The results of previous researches indicate that concrete elasticity modulus decreases when the number of cycles increases .…”

Section: Introductionmentioning

confidence: 99%

“…[15][16][17] Damage to concrete due to freezing and thawing also deteriorates its mechanical properties and fracture behavior. [18][19][20][21] The results of previous researches indicate that concrete elasticity modulus decreases when the number of cycles increases. 22 It is also indicated that the fracture energy of concrete after number of cycles is lower than original value before cycling.…”

Section: Introductionmentioning

confidence: 99%

Genetic prediction of cement mortar mechanical properties with different cement strength class after freezing and thawing cycles

Ghaemi‐Fard

Eskandari‐Naddaf

Ebrahimi

2018

Structural Concrete

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This study predicts the mechanical properties of cement mortars. It focuses specifically on cement strength classes of 32.5, 42.5, and 52.5 MPa, respectively. To this end, a total of 270 specimens using 54 mix designs were constructed and subjected to freezing and thawing cycles (FTCs) at −18 and 18 °C. Then, mechanical properties such as compressive and flexural strength and porosity of the specimens after 0, 50, 100, 150, and 200 cycles were obtained. Afterward, genetic expression programming (GEP) was used to predict the obtained results and to represent nonlinear relationships between the mechanical properties, freezing and thawing cycle, and cement strength class. The experimental data was used for training and testing of two GEP approach models. The results show that the strength of mortar reduces with increase in FTC. On other hand, a close correlation was observed between the predicted and experimental values when cement strength class and number of cycle were considered as independent input parameters. This fact indicates the significance of the aforementioned parameters along with the other parameters such as water to cement ratio, sand to cement ratio, and weight of cement on the accuracy of the prediction.

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Lightweight Ferrocement Matrix Compressive Behavior: Experiments Versus Finite Element Analysis

Cited by 16 publications

References 44 publications

Lifetime Analysis on Centrifugal ID Fan Foundation in Cement Plants

Lifetime Analysis on Centrifugal ID Fan Foundation in Cement Plants

Modeling the Compressive Strength of Ultra-High Performance Concrete (Uhpc) by a Machine Learning Technique Optimized by Novel Algorithm

Genetic prediction of cement mortar mechanical properties with different cement strength class after freezing and thawing cycles

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