Mathematical Model for Early-Aged UHPFRC Compressive Strength Changes

Peng, Xi; Yang, Qiuwei; Cao, Hui; Wang, Haozhen

doi:10.3390/coatings13030525

Cited by 5 publications

(5 citation statements)

References 30 publications

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“…(2) Only a small amount of experimental data are needed for curve fitting, which is very simple in calculation. For concrete strength evaluation, the common mathematical models are hyperbolic models [19,20], exponential models [21,22], polyno-mial models, and mixed models [23,24]. For carbonation depth prediction, the basic idea of mathematical prediction model is to directly establish the relationship between carbonation depth and age of concrete by using some function curve [25][26][27][28][29][30].…”

Section: Mathematical Curve Models For Concrete Carbonation Depthmentioning

confidence: 99%

A Review of Concrete Carbonation Depth Evaluation Models

Wang,

Yang,

Peng

et al. 2024

Coatings

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Carbonation is one of the critical issues affecting the durability of reinforced concrete. Evaluating the depth of concrete carbonation is of great significance for ensuring the quality and safety of construction projects. In recent years, various prediction algorithms have been developed for evaluating concrete carbonation depth. This article provides a detailed overview of the existing prediction models for concrete carbonation depth. According to the data processing methods used in the model, the existing prediction models can be divided into mathematical curve models and machine learning models. The machine learning models can be further divided into the following categories: artificial neural network model, decision tree model, support vector machine model, and combined models. The basic idea of the mathematical curve model is to directly establish the relationship between the carbonation depth and age of concrete by using certain function curves. The advantage of the mathematical curve model is that only a small amount of experimental data is needed for curve fitting, which is very convenient for engineering applications. The limitation of the curve model is that it can only consider the influence of some factors on the carbonation depth of concrete, and the prediction accuracy cannot be guaranteed. The advantage of using the machine learning model to predict the carbonation depth of concrete is that many factors can be considered at the same time. When there are sufficient experimental data, the trained machine learning model can give more accurate prediction results than the mathematical curve model. The main defect of the machine learning model is that it needs a lot of experimental data as training samples, so it is not as convenient as the mathematical curve model in engineering applications. A future research direction may be to combine a machine learning model with a mathematical curve model to evaluate the carbonation depth of concrete more accurately.

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Section: Mathematical Curve Models For Concrete Carbonation Depthmentioning

confidence: 99%

A Review of Concrete Carbonation Depth Evaluation Models

Wang,

Yang,

Peng

et al. 2024

Coatings

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“…The newly proposed evaluation model was validated using experimental data from references [28,30,34,36,39,[41][42][43][44][45] (referred to as cases 1 to 11). The results were compared with the fitting results of existing logarithmic, hyperbolic and polynomial models to demonstrate the superiority of the model.…”

Section: Model Verification and Comparison Via Concrete Experiments Datamentioning

confidence: 99%

“…Case 4. In reference [42], 36 sets of 100 mm × 100 mm × 100 mm high-strength concrete specimens were manufactured and cured under standard curing conditions. The components of concrete materials are listed in Table 12.…”

Section: Evaluation Of Compressive Strengthmentioning

confidence: 99%

General Curve Model for Evaluating Mechanical Properties of Concrete at Different Ages

Yang,

Wang,

Peng

et al. 2023

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During the process of pouring and solidification of concrete, the compressive strength and elastic modulus of concrete exhibit dynamic growth patterns. The mechanical properties of concrete usually remain stable in the later stage (28 days after pouring). Studying appropriate curve models to accurately evaluate the changes in early mechanical properties of concrete has always been an important topic in the field of concrete materials. This work proposes a new dual parameter curve model for accurately evaluating the growth pattern of early compressive strength and elastic modulus of concrete. A comparative study was conducted between the proposed new curve model and existing curve models using 18 sets of experimental data from 10 literature sources. The research results indicate that the fitting average error and standard deviation of this new curve model are significantly smaller than the existing curve models. For some examples, the fitting error and standard deviation of the new model are only about 20%–30% of those of the existing models. The main advantages of this new curve model lie in two aspects. The first advantage is that this new curve model only contains two unknown parameters, so only a small amount of experimental data is required for data fitting and does not require complex mathematical operations. The second advantage is that this new curve model has a wide range of applications, which include compressive strength evaluation and elastic modulus evaluation and can also be extended to the evaluation of the mechanical properties of other materials similar to concrete.

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“…The earliest research on corrosion resistance of coastal concrete was by Bouygues Company [7] and proposed reactive powder concrete (RPC), also known as ultra-highperformance concrete [8,9]. This concrete is produced according to the theory of maximum compactness and is formed by adding cement, quartz sand and active mineral composition.…”

Section: Introductionmentioning

confidence: 99%

Influence of NaCl Solution External Erosion on Corrosion Resistance of RPC Reinforced with Straw Fiber

et al. 2023

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Straw fiber, as a kind of waste if not properly treated, will pollute the environment. It can be used in cement-based materials as a plant fiber material. Agricultural solid-waste straw fiber has good tensile properties and is expected to be used as a fiber-reinforced material for reactive powder concrete (RPC) and to improve the corrosion resistance of RPC. In this paper, the ultrasonic velocity through specimens, the electrical resistance, the AC impedance spectroscopy and tafel curve were analyzed. The corrosion resistance of the steel bar under the chloride salt freeze–thaw cycles and dry–wet alternations was systematically studied. The result shows that adding a certain content of straw fiber can improve its corrosion resistance. Under the action of two chloride salt environments, the lowest mass loss rate was 0.82% for the sample with 3% straw fiber content and the mass growth rate of the specimens with 4% straw fiber is the highest aqt 0.9%. In terms of ultrasonic velocity, the lowest loss rate was 5.68% for specimens with fiber content of 2%. The specimens were subjected to 0 dry–wet alternations and freeze–thaw cycles; the highest electrical resistance is 19.96 kΩ when the fiber content is 1% and the lowest electrical resistance is 11.105 kΩ when the fiber content is 2%. Under the dry–wet alternations, the content of straw fiber and its corrosion resistance are: 1% > 4% > 0% > 3% > 2%. Under freeze–thaw cycles, the content of straw fiber and its corrosion resistance were as follows: 1% > 0% > 4% > 3% > 2%.

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Mathematical Model for Early-Aged UHPFRC Compressive Strength Changes

Cited by 5 publications

References 30 publications

A Review of Concrete Carbonation Depth Evaluation Models

A Review of Concrete Carbonation Depth Evaluation Models

General Curve Model for Evaluating Mechanical Properties of Concrete at Different Ages

Influence of NaCl Solution External Erosion on Corrosion Resistance of RPC Reinforced with Straw Fiber

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