## 1 Introduction  
Previous studies [1, 2] highlighted that the strength of samples could have alterations because they underwent temperature fluctuations and specific curing processes. Researchers and practitioners (e.g., [2, 3]) suggested that concrete strengths were affected by curing techniques. Hence, curing method selection should be an influence factor of such a prediction model when using various test samples. A multivariate regression model for concrete strength prediction, in this case, intends to establish a relationship between multiple independent variables or predictors (such as temperature, test sample shape and size, and curing method) and multiple dependent variables or responses (such as compressive strength and flexural strength). Accurate concrete strength prediction models can help avoid potential risks of structure failures caused by unmatured concrete and make good decisions on when to open roads [4]. The models lay the groundwork for predictive analytics, enabling the projection of future outcomes using historical data combined with analytical methods like machine learning [5]. A multivariate model can be developed using a database comprising hundreds of distinct concrete mixtures, including the mixture designs of cement, slump indicators,temperatures, densities, and compressive and flexural strengths at certain days (e.g., 7 and 28 days [4]), which then creates the foundation for the predictions of concrete strengths and their variance analysis. Various analysis methods like statistical regression and machine-learning-based prediction models can be developed using datasets as input to obtain concrete strengths and their variances as output. The output results are evaluated using evaluation functions like R-squared to compare the differences between the predicted outcomes and the observed data in the databases. The calculated accuracy indicators are further analyzed to make improvements (e.g., using machine-learning algorithms [4]). Once the predicted outcomes are considered accurate, the prediction models can be used to generate reliable estimates of concrete strengths for Day 1, Day 2, Day 3, etc. Because concrete strength testing is usually performed on Days 7 and 28, accurate and reliable strength estimations on the other days, especially the early days, can help project management save time, reduce costs, improve material quality, and avoid risks. The instructions (e.g., [6]) on how to make and cure concrete test specimens in the field are deficient in explanations of how the strength of field-cured specimens compares to that of in-place concrete. For example, transportation agencies mostly use field-cured cylinders or the maturity method to decide when to open pavement or remove formwork or falseword [7]. The current practice of concrete construction is still unclear about the appropriateness of using linear or nonlinear correlations of the cylinders' strengths when they are field-cured versus the strengths calculated using the maturity method [8]. Therefore, clarification of these correlations can facilitate dependable and pragmatic comparisons of concrete strengths across diverse samples. Table 1 lists some common curing methods [9]. In concrete construction, people normally perform specimen tests after 1, 3, 7, 14, and 28 days of curing. The tests on Day 1, Day 3, and Day 7 are generally for early strength. Two types of cylinders: 100 mm \(\times\) 200 mm (4 in. diameter \(\times\) 8 in. tall; a.k.a., 4 in. for simplicity) cylinders and 150 mm \(\times\) 300 mm (6 in. diameter \(\times\) 12 in. tall; a.k.a., 6 in. for simplicity) cylinders, were used to test compressive strength. Mathematically, compressive strength is calculated using a load force divided by a load-bearing area, so a 4 in. cylinder and a 6 in. cylinder should have the same result. Yet, a concrete item, like a slab or a structural column, is substantially larger than a specimen and can generate more heat during cement hydration. Similarly, a 6 in. cylinder can generate more heat than a 4 in.