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As one of the concrete admixtures, water reducing admixtures and superplasticizers are usually used to reduce the mixing water volume and improve the performance of the harden concrete while maintaining better workability of the fresh concrete. However, the concrete strength and durability properties are affected differently by different types and dosages of the water reducing admixtures and superplasticizers. Based on the published literatures, this paper comprehensively reviews and analyzes this problem. Different types of the concretes, including ordinary Portland cement concrete, ordinary Portland cement concrete containing pozzolan, fly ash and ground granulated blast furnace slag, calcium sulfoaluminate cement concrete, ferrite aluminate cement concrete, recycled aggregates concrete, lightweight aggregate concrete, self-compacting concrete and ultra-high performance concrete, are considered to discuss the influence of types and dosages of the water reducing admixtures and superplasticizers on their strengths. Water absorption, frost resistance and permeability resistance of the concrete are mainly reviewed to discuss this influence on the durability properties of the concrete. Then, some suggestions on the application of the water reducing admixtures and superplasticizers in reinforced concrete structures and projects are proposed.

Under high-temperature conditions, the fluidity retention properties of polycarboxylate superplasticizers remain a challenge. This study improved the molecular structure of traditional slow-release polycarboxylate superplasticizers (also known as slump-retention agents) by introducing hydroxypropyl acrylate, successfully synthesizing a novel slump-retention agent that maintains concrete slump for an extended period at high temperatures. Comparing its performance with two other conventionally synthesized slump-retention agents, the superior performance of the new agent was verified. We then delved into the behavior of this novel slump-retention agent under different temperature conditions.

A series of polycarboxylate ether (PCE) based superplasticizers with early-strength functional monomers were prepared to study the influence of early-strength PCE on the hydration heat of cement, early strength properties, and setting time of concrete. It was found that PCE containing DAC and UP-152 had the most obvious improvement in the early strength performance of concrete, greatly improved the dispersibility of cement particles, accelerated the formation of C-S-H gel, enhanced the early hydration performance, and early advanced the setting time of concrete. It can also reduce the pores and gaps in the cement concrete and improve its compactness. Finally, it had the most prominent effect on the early strength of concrete.

Polycarboxylate superplasticizer, as the third-generation concrete superplasticizer, has become an indispensable component in modern concrete production processes. This paper aims to investigate the effect of polycarboxylate superplasticizers on the workability and mechanical properties of concrete at different test temperatures. The study explores the variations in initial slump, initial expansion, 1-hour slump, 1-hour expansion, and release rate, as well as the compressive and flexural strengths at 3 days, 7 days, and 28 days of concrete formulated with polycarboxylate superplasticizer at test temperatures ranging from 30 °C to -20 °C. The conclusions drawn are as follows: As the test temperature gradually decreases from 30 °C to -20 °C, the initial fluidity of cement paste decreases by 16%, and the initial slump and initial expansion of concrete decrease by 9% and 6%, respectively. The release rate slows down by 114%, and both the compressive and flexural strengths at 3 days and 7 days decrease to some extent.

Geopolymer composites can be used as a proper substitute for ordinary Portland cement, which can reduce carbon dioxide (CO2) emissions and make rational use of industrial waste. In this study, an investigation of the workability and compressive strength of geopolymer composites was carried out through a series of experiments, such as slump flow test, consistency meter test and compressive strength test, to clarify the interaction mechanism among superplasticizer (SP), polyvinyl alcohol (PVA) fiber, Nano-SiO2 (NS) and geopolymer composites, thereby improving the properties of engineered composites. The results showed that with the increase in PVA fiber content, the flowability of geopolymer composites decreased, while the thixotropy increased. With the increase in the NS content, the flowability of geopolymer composites first increased and then decreased, reaching its best at 1.0%, while the thixotropy was the opposite. With the increase in the SP content, the flowability of geopolymer composites increased, while the thixotropy decreased. A significant correlation between thixotropy and flowability of geopolymer composites was found (R2 > 0.85). In addition, the incorporation of single PVA fiber or NS significantly improved the compressive strength of geopolymer composites. Specifically, the compressive strength of geopolymer composites with 0.8% content PVA fiber (60.3 MPa) was 33.4% higher than that without PVA fiber (45.2 MPa), and the compressive strength of geopolymer composites with 1.5% content NS (52.6 MPa) was 16.4% higher than that without NS (45.2 MPa). Considering the synergistic effect, it is found that the compressive strength of geopolymer composites (58.5–63.3 MPa) was significantly higher than that without PVA fiber (45.2–52.6 MPa). However, the flowability and compressive strength of geopolymer composites were only slightly improved compared to that without NS. With the increase in the SP content, the compressive strength of geopolymer composites showed a trend of a slight decrease on the whole. Consequently, the results of this study may be useful for further research in the field of repair and prevention of the delamination of composite structures.

This paper numerically investigates the required superplasticizer (SP) demand for self-consolidating concrete (SCC) as a valuable information source to obtain a durable SCC. In this regard, an adaptive neuro-fuzzy inference system (ANFIS) is integrated with three metaheuristic algorithms to evaluate a dataset from non-destructive tests. Hence, five different non-destructive testing methods, including J-ring test, V-funnel test, U-box test, 3 min slump value and 50 min slump (T50) value were performed. Then, three metaheuristic algorithms, namely particle swarm optimization (PSO), ant colony optimization (ACO) and differential evolution optimization (DEO), were considered to predict the SP demand of SCC mixtures. To compare the optimization algorithms, ANFIS parameters were kept constant (clusters = 10, train samples = 70% and test samples = 30%). The metaheuristic parameters were adjusted, and each algorithm was tuned to attain the best performance. In general, it was found that the ANFIS method is a good base to be combined with other optimization algorithms. The results indicated that hybrid algorithms (ANFIS-PSO, ANFIS-DEO and ANFIS-ACO) can be used as reliable prediction methods and considered as an alternative for experimental techniques. In order to perform a reliable analogy of the developed algorithms, three evaluation criteria were employed, including root mean square error (RMSE), Pearson correlation coefficient (r) and determination regression coefficient (R2). As a result, the ANFIS-PSO algorithm represented the most accurate prediction of SP demand with RMSE = 0.0633, r = 0.9387 and R2 = 0.9871 in the testing phase.

Self-consolidating concrete (SCC) is a well-known type of concrete, which has been employed in different structural applications due to providing desirable properties. Different studies have been performed to obtain a sustainable mix design and enhance the fresh properties of SCC. In this study, an adaptive neuro-fuzzy inference system (ANFIS) algorithm is developed to predict the superplasticizer (SP) demand and select the most significant parameter of the fresh properties of optimum mix design. For this purpose, a comprehensive database consisting of verified test results of SCC incorporating cement replacement powders including pumice, slag, and fly ash (FA) has been employed. In this regard, at first, fresh properties tests including the J-ring, V-funnel, U-box, and different time interval slump values were considered to collect the datasets. At the second stage, five models of ANFIS were adjusted and the most precise method for predicting the SP demand was identified. The correlation coefficient (R2), Pearson’s correlation coefficient (r), Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), mean absolute error (MAE), and Wilmot’s index of agreement (WI) were used as the measures of precision. Later, the most effective parameters on the prediction of SP demand were evaluated by the developed ANFIS. Based on the analytical results, the employed algorithm was successfully able to predict the SP demand of SCC with high accuracy. Finally, it was deduced that the V-funnel test is the most reliable method for estimating the SP demand value and a significant parameter for SCC mix design as it led to the lowest training root mean square error (RMSE) compared to other non-destructive testing methods.

This study aims to investigate the comparative performance of ester- and ether-based polycarboxylate superplasticizers in maintaining the fluidity and controlling the setting time of alkali-activated slag (AAS) paste. The experiments employed rheological tests, mini-slump tests, ultrasonic pulse velocity (UPV) measurements, and gel permeation chromatography (GPC) analysis. The results indicate that ether-based superplasticizers maintain fluidity approximately 25% longer than their ester-based counterparts and extend the setting time by about 30%. The enhanced performance of ether-based superplasticizers is attributed to their superior molecular stability in highly alkaline environments, which mitigates early polymer degradation. Additionally, the Na2O/SiO2 ratio was maintained at 1:1 throughout the experiments to ensure consistency in the activation process. The relationship between fluidity loss and the onset of setting occurs more rapidly in AAS paste than in conventional cement-based systems. These findings provide valuable insights for the development of environmentally friendly construction materials by optimizing the use of superplasticizers in alkali-activated systems.

The effects of initial dropping temperature, amount of ferrous sulfate and dropping time on the performance of Polycarboxylate Superplasticizer were studied, and the measures to maintain the performance stability of Polycarboxylate Superplasticizer from 15 °C to 40 °C were discussed. The results show that the stability of the process is better when the initial dropping temperature is 20°C-30°C. When the temperature is higher than 30 °C, the stability of the process can be achieved by prolonging the dropping time or reducing the amount of FeSO 4 ; When the temperature is lower than 15 C, the performance can be improved by increasing the amount of FeSO 4 , so as to achieve the stability of the process.

Machine learning techniques are widely used algorithms for predicting the mechanical properties of concrete. This study is based on the comparison of algorithms between individuals and ensemble approaches, such as bagging. Optimization for bagging is done by making 20 sub-models to depict the accurate one. Variables like cement content, fine and coarse aggregate, water, binder-to-water ratio, fly-ash, and superplasticizer are used for modeling. Model performance is evaluated by various statistical indicators like mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Individual algorithms show a moderate bias result. However, the ensemble model gives a better result with R2 = 0.911 compared to the decision tree (DT) and gene expression programming (GEP). K-fold cross-validation confirms the model’s accuracy and is done by R2, MAE, MSE, and RMSE. Statistical checks reveal that the decision tree with ensemble provides 25%, 121%, and 49% enhancement for errors like MAE, MSE, and RMSE between the target and outcome response.