Prediction of Plastic Shrinkage Cracking of Self‐Compacting Concrete

Ghoddousi, Parviz; Abbasi, Armin Monir; Shahrokhinasab, Esmail; Abedin, Mohammad

doi:10.1155/2019/1296248

Cited by 11 publications

(4 citation statements)

References 16 publications

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“…The model was built based on a large amount of experimental data. Ghoddousu et al [20] developed an equation to estimate plastic shrinkage values based on the coarse aggregate ratio and W/P ratio under hot-dry climate conditions. The results showed that plastic shrinkage depends on time and has a linear relationship with evaporable water content.…”

Section: Introductionmentioning

confidence: 99%

The Application of Neural Networks to Predict the Water Evaporation Percentage and the Plastic Shrinkage Size of Self-Compacting Concrete Structure

Nguyen,

Tran

2024

Civ Eng J

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This article presents a solution using an artificial neural network and a neuro-fuzzy network to predict the rate of water evaporation and the size of the shrinkage of a self-compacting concrete mixture based on the concrete mixture parameters and the environment parameters. The concrete samples were mixed and measured at four different environmental conditions (i.e., humid, dry, hot with high humidity, and hot with low humidity), and two curing styles for the self-compacting concrete were measured. Data were collected for each sample at the time of mixing and pouring and every 60 minutes for the next ten hours to help create prediction models for the required parameters. A total of 528 samples were collected to create the training and testing data sets. The study proposed to use the classic Multi-Layer Perceptron and the modified Takaga-Sugeno-Kang neuro-fuzzy network to estimate the water evaporation rate and the shrinkage size of the concrete sample when using four inputs: the concrete water-to-binder ratio, environment temperature, relative humidity, and the time after pouring the concrete into the mold. Real-field experiments and numerical computations have shown that both of the models are good as parameter predictors, where low errors can be achieved. Both proposed networks achieved for testing results R2 bigger than 0.98, the mean of squared errors for water evaporation percentage was less than 1.43%, and the mean of squared errors for shrinkage sizes was less than 0.105 mm/m. The computation requirements of the two models in testing mode are also low, which can allow their easy use in practical applications. Doi: 10.28991/CEJ-2024-010-01-07 Full Text: PDF

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Section: Introductionmentioning

confidence: 99%

The Application of Neural Networks to Predict the Water Evaporation Percentage and the Plastic Shrinkage Size of Self-Compacting Concrete Structure

Nguyen,

Tran

2024

Civ Eng J

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“…Generally, researchers used a wind tunnel for this simulation. Ghoddousi Parviz et al [27] specified the preparation of 13 self-compacting concrete samples, which were made according to different mixing designs. These samples were placed inside a wind tunnel, which simulated warm and dry environmental conditions.…”

Section: Introductionmentioning

confidence: 99%

Test Method for Studying the Shrinkage Effect under Controlled Environmental Conditions for Concrete Reinforced with Coconut Fibres

et al. 2023

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This study proposes a novel test method and corresponding procedure to evaluate how coconut fibres affect crack propagation rates resulting from plastic shrinkage during the accelerated drying of concrete slabs. The experiment employed concrete plate specimens, which were used to simulate slab structural elements with a surface dimension notably greater than their thickness. These slabs were reinforced with coconut fibre with 0.5%, 0.75%, and 1% fibre content. A wind tunnel was designed to simulate two significant climate parameters (wind speed and air temperature), which could impact the cracking behaviour of surface elements. The proposed wind tunnel allowed air temperature to be controlled alongside wind speed while monitoring moisture loss and the cracking propagation process. During testing, a photographic recording method was used to evaluate cracking behaviour, with the total crack length serving as a parameter to assess the impact of fibre content on the crack propagation of slab surfaces. Additionally, crack depth was measured using ultrasound equipment. The results indicate that the proposed test method was appropriate for future research, allowing for the evaluation of the effect of natural fibres on the plastic shrinkage behaviour of surface elements under controlled environmental conditions. Based on initial studies and the results obtained through the proposed test method, concrete containing 0.75% fibre content exhibited significantly reduced crack propagation on slab surfaces, as well as a reduction in the crack depth caused by plastic shrinkage during the early age of the concrete.

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“…UHPC with a different mix design model is a combination of portland cement, sand, quartz powder, silica fume, superplasticizer, water, and steel fibres [4,[8][9][10][11]. In the UHPC mixture, a higher portion of cement is used compared to the HPC and normal strength concrete (NSC) [12,13] and the key factor of UHPC production is the enhancement of material density, mechanical homogeneity, and particle packing by improving macro and micro properties [12][13][14][15][16][17][18][19][20], which help the durability of UHPC against chloride diffusion. The water-to-cement ratio (W/C) of UHPC is typically less than 0.25 due to the replacement of the portion of un-hydrated cement with blast furnace slag, fly ash, or crushed quartz [21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Numerical Modelling of Concrete-to-UHPC Bond Strength

et al. 2020

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Ultra-High Performance Concrete (UHPC) has been a material of interest for retrofitting reinforced concrete elements because of its pioneer mechanical and material properties. Numerous experimental studies for retrofitting concrete structures have shown an improvement in durability performance and structural behaviour. However, conservative and sometimes erroneous estimates for bond strength are used for numerically calculating the strength of the composite members. In addition, different roughening methods have been used to improve the bond mechanism; however, there is a lack of numerical simulation for the force transfer mechanism between the concrete substrate and UHPC as a repair material. This paper presents an experimental and numerical programme designed to characterize the interfacial properties of concrete substrate and its effect on the bond strength between the two materials. The experimental programme evaluates the bond strength between the concrete substrates and UHPC with two different surface preparations while using bi-surface test and additional material tests, including cylinder and cube tests for compression property, direct tension test, and flexural test to complement UHPC tensile properties. Non-linear finite element analysis was conducted, which uses a numerical zero thickness volume model to define the interface bond instead of a traditional fixed contact model. The numerical results from the zero thickness volume model show good agreement with the experimental results with a reduction in error by 181% and 24% for smooth and rough interface surfaces if compared to the results from the model with a fixed contact.

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Prediction of Plastic Shrinkage Cracking of Self‐Compacting Concrete

Cited by 11 publications

References 16 publications

The Application of Neural Networks to Predict the Water Evaporation Percentage and the Plastic Shrinkage Size of Self-Compacting Concrete Structure

The Application of Neural Networks to Predict the Water Evaporation Percentage and the Plastic Shrinkage Size of Self-Compacting Concrete Structure

Test Method for Studying the Shrinkage Effect under Controlled Environmental Conditions for Concrete Reinforced with Coconut Fibres

Numerical Modelling of Concrete-to-UHPC Bond Strength

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