Mechanical Properties and Microstructure Evaluation of Cement Mortar with Different Cement Strength Classes by Image Analysis

Korouzhdeh, Tahereh; Eskandari‐Naddaf, Hamid

doi:10.1007/s13369-021-06257-4

Cited by 9 publications

(3 citation statements)

References 58 publications

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“…The appropriate technical properties of the cement class (CEM I-V) employed in the production of concrete should be carefully examined [8,9]. In addition, the physical and chemical properties of water [10,11], the type of aggregates [12,13], and admixtures such as setting retarders, plasticizers, air entrainers, water impermeability agents, and consistency agents [14][15][16] should also be thoroughly analyzed and selected according to the intended purpose.…”

Section: Introductionmentioning

confidence: 99%

Investigation of the use of cobalt and nickel based nanoalloys as cement mortar additives

Karaduman,

Pişkin

2024

Mater. Res. Express

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The usage potential of chemical and green synthesized cobalt (Co) and nickel (Ni) nanoalloys (CoNiNAs) as mortar additives at different ratios was evaluated. The CoCl2 and NiCl2 metallic salt solutions were mixed in volume ratios of 1-1, 1-2, and 2-1 and reduced with NaBH4 and St. John's Wort aqueous extract, respectively. The obtained Co-Ni based complex nanoalloys were analyzed by Ultraviolet-Visible Spectroscopy (UV-Vis), X-Ray Diffraction Analysis (XRD), Field Emission Scanning Electron Microscopy (FE-SEM), Fourier Transform Infrared Spectroscopy (FT-IR), and Dynamic Light Scattering Particle Size Analyzer (DLS). The effect of CoNiNAs was investigated based on the amount used in mortar, flexural and compressive strengths of mortar, setting time retarder properties, and carbonation depth measurements of mortars and nanoalloy form based on whether they were solid (chemical synthesise) or liquid (green synthesise). The results revealed that the chemical synthesized CoNiNAs were amorphous metal-metal-oxide complexes with small spherical particles and a low dispersity index, whereas the green synthesized complexes had a more crystalline structure and smaller sizes. The mortar properties were affected by Co and Ni synthesis ratios and addition amounts. The incorporation of CoNiNAs led to an increase in the setting times of mortar. Furthermore, the ‘CN’ 2% sample exhibited the highest compression (49.10±1.19 MPa) and flexural (8.19±0.20 MPa) strengths. In addition, the ‘CN2’ 1% sample exhibited the lowest carbonation depth (2.95±0.35 mm) compared to other samples. Overall, mortars with CoNiNAs additives may be used in high temperature environments, and long shipment times require remote locations due to setting time retarder effect without losing necessary physical properties.

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Section: Introductionmentioning

confidence: 99%

Investigation of the use of cobalt and nickel based nanoalloys as cement mortar additives

Karaduman,

Pişkin

2024

Mater. Res. Express

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Section: Introductionmentioning

confidence: 99%

“…It is worth noting that the strength properties of cementitious materials depend on many parameters, including mix design, material quality, the age of the specimen, and pores structure in the form of P. [8][9][10][11][12][13][14][15] Recently, several prediction methods have been developed to achieve an optimum mix design with expected strength properties. 16,17 The artificial neural network (ANN) method is one of the powerful artificial intelligence techniques, and it has widely been applied for solving complex problems with nonlinear variables.…”

Section: Introductionmentioning

confidence: 99%

New insight into the prediction of strength properties of cementitious mortar containing nano‐ and micro‐silica based on porosity using hybrid artificial intelligence techniques

Kazemi

Eskandari‐Naddaf

Korouzhdeh

2023

Structural Concrete

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Nowadays, the accurate prediction of strength properties of cementitious materials containing nano‐ and micro‐silica (NS–MS) remains an open question because of the highly nonlinear function of its constituents on the porosity. In the present study, a combined framework is developed by integrating ant colony optimization (ACO), particle swarm optimization (PSO), and biogeography‐based optimization (BBO) with the artificial neural network (ANN) to predict compressive and flexural strengths of cement mortar in two different forms of ignoring (ANNII) and considering (ANNIII) the porosity as an input parameter. This procedure is accomplished considering the porosity effect on the strengths and implementing an experimental program containing 32 mixes (960 specimens) with different NS–MS contents at various ages. Macro‐ and micro‐structural analyses showed that NS–MS caused more decreased pore structure, and thus this situation increases strength properties compared to their separate use. Also, MBBO‐MOANNIII results indicated an improvement in convergence speed and model accuracy compared to other models. This improvement is because of considering the porosity.

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Strength Properties of Cement Mortar with Dark Glass Waste Powder as a Partial Sand Replacement

Ng,

Lim,

Lee

et al. 2024

Lecture Notes in Civil Engineering

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Mechanical Properties and Microstructure Evaluation of Cement Mortar with Different Cement Strength Classes by Image Analysis

Cited by 9 publications

References 58 publications

Investigation of the use of cobalt and nickel based nanoalloys as cement mortar additives

Investigation of the use of cobalt and nickel based nanoalloys as cement mortar additives

New insight into the prediction of strength properties of cementitious mortar containing nano‐ and micro‐silica based on porosity using hybrid artificial intelligence techniques

Strength Properties of Cement Mortar with Dark Glass Waste Powder as a Partial Sand Replacement

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