Effect of stray current and sulfate attack on cementitious materials in soil

Fang, Zheng; Li, Zijian; Zhou, Ying; Xie, Qiang; Peng, Haiyou; Zhou, Shuai; Wang, Chong

doi:10.1016/j.conbuildmat.2023.133723

Cited by 7 publications

(1 citation statement)

References 38 publications

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“…Regarding the application of nano additions, several contributions have noted that employing various cement substitutes could increase the mechanical qualities and durability [26]. Authors also have proved that cementitious materials' endurance can be further increased by adding SCMs through experiments [27]. Hence, if the carbon emissions can be reduced, the UHPC will have a wider usage.…”

Section: Introductionmentioning

confidence: 99%

Carbon Emission Optimization of Ultra-High-Performance Concrete Using Machine Learning Methods

Wang,

Du,

Jia

et al. 2024

Materials

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Due to its exceptional qualities, ultra-high-performance concrete (UHPC) has recently become one of the hottest research areas, although the material’s significant carbon emissions go against the current development trend. In order to lower the carbon emissions of UHPC, this study suggests a machine learning-based strategy for optimizing the mix proportion of UHPC. To accomplish this, an artificial neural network (ANN) is initially applied to develop a prediction model for the compressive strength and slump flow of UHPC. Then, a genetic algorithm (GA) is employed to reduce the carbon emissions of UHPC while taking into account the strength, slump flow, component content, component proportion, and absolute volume of UHPC as constraint conditions. The outcome is then supported by the results of the experiments. In comparison to the experimental results, the research findings show that the ANN model has excellent prediction accuracy with an error of less than 10%. The carbon emissions of UHPC are decreased to 688 kg/m3 after GA optimization, and the effect of optimization is substantial. The machine learning (ML) model can provide theoretical support for the optimization of various aspects of UHPC.

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Section: Introductionmentioning

confidence: 99%

Carbon Emission Optimization of Ultra-High-Performance Concrete Using Machine Learning Methods

Wang,

Du,

Jia

et al. 2024

Materials

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Accelerated external sulfate attack: Improved laboratory methods, considering physical and chemical processes

Biook Aghazadeh,

Mirvalad

2024

Structural Concrete

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The most common and well‐known method for evaluating the sulfate resistance of cement blends is ASTM C1012 procedure. It takes at least 12–18 months to evaluate the results, considering this standard. The present research proposes two supplementary methods to accelerate the sulfate ions penetration into the mortar bars used in ASTM C1012. Both of these two supplementary methods were evaluated by studying mortar samples' expansion, mass change, visual condition, compressive strength, ultrasonic pulse velocity, and XRD. In the first method, a developed device provided an electrical conduction environment for mortar bars made of three cement blends. The results showed that this method produced between 8 and 16 times more expansion considering different studied cement blends. In the second method, mortar bars of seven cement blends were exposed to a devised prior condition under vacuum. Considering the results, this method produced between 2.5 and 9 times more expansion in relation to each cement blend and approach. Additionally, an excellent polynomial correlation was observed between the expansion of new accelerated methods and ASTM C1012 results. It can be concluded that using these accelerated methods as complementary to ASTM C1012 can help to evaluate the sulfate resistance of cement blends in a shorter time period.

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Thaumasite sulfate attack characteristic of seawater and sea sand concrete with limestone powder in different sulfate environment at low temperature

Wang,

Zhang,

Chen

et al. 2024

Construction and Building Materials

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Effect of stray current and sulfate attack on cementitious materials in soil

Cited by 7 publications

References 38 publications

Carbon Emission Optimization of Ultra-High-Performance Concrete Using Machine Learning Methods

Carbon Emission Optimization of Ultra-High-Performance Concrete Using Machine Learning Methods

Accelerated external sulfate attack: Improved laboratory methods, considering physical and chemical processes

Thaumasite sulfate attack characteristic of seawater and sea sand concrete with limestone powder in different sulfate environment at low temperature

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