# Model-Based Methods to Produce Greener Metakaolin Composite Concrete
## 1 Introduction  
Metakaolin mainly consists of SiO\({}_{2}\) and Al\({}_{2}\)O\({}_{3}\) and shows high reactivity. The addition of metakaolin can provide various benefits to concrete, such as increased late age strength, enhanced chloride ingress and acid resistance, and lower shrinkage and greenhouse gas emissions [1, 2]. In recent years, metakaolin has increasingly been used for concrete manufacturing. Strength is one of the fundamental engineering properties of structural concrete. The evaluation of strength development is meaningful for structural element design and construction management. Many numerical models have been presented for evaluating the strength of metakaolin composite concrete. [PERSON] and [PERSON] [3] proposed a strength model that analyzes the efficiency of metakaolin using metakaolin replacement percentage, water/binder ratio, and age. Using the concept of efficiency factor, [PERSON] and [PERSON] [4] and [PERSON] et al. [5] proposed a software package that evaluates the strength class and service life of blended concrete. In addition to the efficiency factor method, some strength models have been proposed that use machine learning methods. [PERSON] [6],[PERSON] et al. [7], [PERSON] and [PERSON] [8] analyzed the strength of metakaolin concrete by using artificial neural networks. [PERSON] et al. [9] analyzed the strength using multivariate adaptive regression splines and an M5P model tree. [PERSON] et al. [10] analyzed strength development based on the method of gene expression programming. [PERSON] [11] evaluated the strength of self-compacting metakaolin composite concrete using response surface analysis. Sustainability is becoming more and more important for the modern concrete industry. Many studies have evaluated the sustainability of concrete. [PERSON] et al. [12] performed lifecycle CO2 evaluations of different concretes and found that high strength concrete can lower lifecycle energy consumption. [PERSON] et al. [13] proposed a blended system that can reduce CO2 emissions by 40% for structural light-weight aggregate concrete. [PERSON] et al. [14] proposed models to calculate the CO2 emissions and CO2 uptake of composite concrete. [PERSON] et al. [15] calculated the CO2 emissions, energy consumption, and resource consumption of self-compacting concrete with various mineral admixtures. [PERSON] et al. [16] reported that material substitution and CO2 capture technology are useful for making low-CO2-emission cement. [PERSON] et al. [17] reported when 20% of cement was replaced by pozzolanic materials, the number of cracks and width of cracks can be reduced. [PERSON] et al. [18] made a cost analysis between ferrocement panels and conventional reinforced concrete, and showed ferrocement panels can reduce the initial cost by about 27%. [PERSON] et al. [19] proposed an effective methodology of sustainable assessment of concrete mixtures considering environmental impact, performance, service life, and cost. Although various models have been used to evaluate strength and sustainability, said models present some weakness. Regarding the strength evaluation models, machine learning-based ones are black box techniques that do not consider the physical/chemical processes of binder hydration and strength development. Moreover, the extrapolation ability of machine learning-based models is very weak. When the ranges of input parameters, such as water/binder ratio, metakaolin/binder ratio, and age fall outside of the ranges of the training data, a machine learning-based model cannot be used. In addition, although the physical meaning of the efficiency factor method is much clearer than the workings of a machine learning-based model, it is difficult to obtain an accurate equation for the efficiency factor that accounts for various mixtures and curing ages. Lastly, previous models mainly focused on single aims, such as a strength evaluation or evaluating CO2 emissions. A dual-aim model which can evaluate both strength and CO2 emissions is necessary.