Structural Concrete and Composites: Processes, Corrosion and Modeling

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. Full article

The expansion induced by sulfate attack on cement-treated aggregates (SACA) is a well-known problem that can be solved. It causes obvious heaves in road bases and railway subgrades. In this paper, the effects of the sodium sulfate content, cement content, degree of compaction, sulfate types, attack types, aluminum ion supply, concentration of curing sulfate solution, and temperature on the expansion behavior induced by SACA were investigated over 60 days in the laboratory. Based on the Sobol sensitivity analysis method, the influence of the sensitivity of each factor on the expansion was quantitatively analyzed, and the dominant factor of expansion was proposed. Results show that sulfate content is the domain factor of expansion that is induced by SACA, and it presents a logarithmic function relationship with strain. The 0.5% sodium sulfate content is the minimum sulfate content which causes the expansion that is induced by SACA. When the sulfate content is less than 1%, the expansion induced by SACA is minor. When the sulfate content is between 1% and 3%, the expansion behavior is expressed in four stages as follows: rapid strain increase, followed by a short stagnation period, then a significant strain increase and, finally, constant strain. When the sulfate content is greater than 5%, there are two stages comprising the expansion behavior as follows: the rapid strain increases and constant strain occurs. Greater sulfate content, greater degree of compaction, and lower temperature have positive effects on the expansion induced by SACA. The cement content does not have a consistent effect on expansion behavior. Compared with a sodium sulfate attack, both the reaction rate and expansion of cement-treated aggregates that are attacked by gypsum are smaller, and the attack period is also longer. When the sulfate content is greater than 1%, the addition of kaolin promotes the progression of the expansion induced by SACA. A small amount of water is sufficient for the demand for the sulfate attack. When the sulfate content is at a certain level, the expansion induced by SACA that is under external attack is much smaller than the expansion that is under internal attack. This study is expected to serve as a reference for future research on the mechanics of SACA, and it attempts to provide theoretical support for amending expansions that are induced by SACA. Full article