A comprehensive review of the statistical experimental optimization problem concerning the mixture design of various cement-based materials is presented herein. This review summarizes and discusses over 80 applications of optimum design regarding the basic test information under response surface method (RSM), including influence factor and corresponding response, statistical method, and coefficient of determination. The statistical experimental design reported in previous studies has shown that RSM is a sequential procedure to provide a suitable approximation for the mixture optimization. Then, linear, quadratic and interactive relationships of the statistical model can be evaluated available. Especially, the multi-objective optimization issues with multiple or competing performance requirements for various cement-based materials have also been reported, by considering fluidity, strength development, environmental impact, cost and durability. Overall, the results from existing publications have demonstrated that statistical inference and analysis of variance (ANOVA) are suitable for mix proportion design and process optimization of cement-based materials. The W/B ratio and mixture components are the prevalent factors in experimental design optimization, and then the fluidity and strength as the most popularly used response. Thus, theoretical optimum mixture proportioning can be used to predict valuable fresh and hardened properties. Finally, a critical discussion of the selection of design strategy, independent factors and their responses, and the experimental region involved in statistical experimental design, is provided. Based on this review, we conclude that the multi-objective optimization approaches need a further systematic study, and further studies of sustainable concrete optimization are needed by comparing the different chemical composition and particle characteristics.

Optimization of mixture proportions by statistical experimental design using response surface method - A review

Durability performance of pervious concrete containing rice husk ash and calcium carbide: A response surface methodology approach

Efforts are being devoted to reducing the harmful effect of the construction industry around the globe, including the use of rice husk ash as a partial replacement of cement. However, no method is available to date to predict the compressive strength (CS) of rice husk ash blended concrete (RHAC). In this study, advanced machine learning techniques (artificial neural network, artificial neuro-fuzzy inference system) were used to predict the CS of RHAC. Based on the published literature, six inputs, i.e., age of specimen, percentage of rice husk ash, percentage of superplasticizer, aggregates, water, and amount of cement, were selected. Results obtained from machine learning methods were compared with traditional methods such as linear and non-linear regressions. It was observed that the performance of machine learning methods was superior to traditional methods for determining the CS of RHAC. This study will prove beneficial in minimizing the cost and time of executing laboratory experiments for designing the optimum content portions of RHAC.

https://www.mdpi.com/2073-4352/11/7/779/pdf

Comparison of Machine Learning Approaches with Traditional Methods for Predicting the Compressive Strength of Rice Husk Ash Concrete

Plastic waste (PW) generation continuously increases every year due to the growing population and demand for plastic materials. This situation poses a challenge to many countries, including developed ones, on how to dispose of PW. Accordingly, PW was utilized in this study to replace coarse aggregates partially in high-volume fly-ash (HVFA) concrete. However, PW decreased the strength and durability of concrete. To address this issue, graphene nanoplatelets (GNPs) were added to mitigate the negative consequences of PW and HVFA on concrete’s properties. The objective of this study is to investigate the influences of PW and GNP contents on the durability and deformation of HVFA concrete. Response-surface methodology (RSM) was used to design and optimize a series of cement mixes to achieve the most desirable properties. Independent variables included PW content as partial replacement for coarse aggregates (0%, 15%, 30%, 45%, and 60% by volume), fly ash as partial substitute for cement (0%, 20%, 40%, 60%, and 80% by volume), and GNPs as additives (0%, 0.075%, 0.15%, 0.225%, and 0.3%). The considered responses were concrete unit weight, modulus of elasticity (MoE), and Cantabro abrasion loss at 300 revolutions. Results showed that PW and HVFA decreased concrete unit weight and MoE but increased Cantabro abrasion loss. By contrast, GNPs increased concrete unit weight and MoE but decreased Cantabro abrasion loss. PW and HVFA also increased the compressive toughness and porosity of concrete, while GNPs increased its stiffness but decreased its compressive toughness and porosity. The mathematical models developed to predict the unit weight, MoE, and abrasion resistance of concrete were significant, with errors of less than 6%. An optimized mix was achieved by partially replacing 12.44% of coarse aggregates with PW and 24.57% of cement with fly ash and adding 0.279% GNPs with a desirability of 100%. 

Compressive Behavior and Durability Performance of High-Volume Fly-Ash Concrete with Plastic Waste and Graphene Nanoplatelets by Using Response-Surface Methodology

Residual compressive stress–strain relationship of lightweight aggregate concrete after exposure to elevated temperatures

Mix design for recycled aggregate pervious concrete based on response surface methodology

A new multi-principal Co-Cr-Cu-Fe-Ni-Mo-Al high entropy alloy with good mechanical properties and corrosion resistance was prepared. For the phenomenon of Cu segregation, we report a strategy to tailor the existing form of Cu in HEA from undesired large-scale segregation to uniform distribution in the solid solution phase. The effects of the Al content on the microstructure, mechanical properties and corrosion behavior of CoCrCuFeNiMoAlx (x = 0, 0.32, 0.67, and 1.0) alloys were studied. The results show that when Al content is low, Cu segregation phenomenon is serious, and the microstructure of the alloy is FCC structure and a little BCC structure. With the increase of Al content, the microstructure is gradually transformed into BCC structure, and the Cu segregation phenomenon is improved. In addition, with the increase of Al content, the mechanical properties of the alloy are improved to some extent. According to the experimental results, the hardness increased by 30.4 %and the compressive strength increases by 22.9 %. Moreover, an increasing Al content had a positive influence on the corrosion resistance of the alloy, increased corrosion potential of the alloy and reduced corrosion current. 

Effect of Al content on the microstructure and properties of CoCrCuFeNiMoAlx high entropy alloy

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Mix design for recycled aggregate pervious concrete based on response surface methodology

Effect of Al content on the microstructure and properties of CoCrCuFeNiMoAlx high entropy alloy