## 2 Proposed Solution  
Figure 1 shows the theoretical framework of this research. To solve the research questions, we conducted a literature review that explored and identified the concrete curing \begin{table}
\begin{tabular}{c c c} \hline \hline
**Technique** & **Ex.** & **Features** \\ \hline ANOVA & [12] & A two-way mixed ANOVA was used to study the roles of spatial information in virtual reality. \\ \hline PCA & [10; 11] & PCA was used to calculate research results like social vulnerability scores and dominant social factors [10]. PCA was used to visually review a range of clustering scenarios for the determination of the optimal number of clusters [11]. Three to six different clusters of snowstorms existed. The use of PCA facilitated visualization and improved the understanding of high-dimensional data. \\ \hline LDA & [13] & Compared with other supervised learning methods like random forest (RF) and gradient-boosting machine (GBM), linear discriminant analysis (LDA) behaved the worst in classification accuracy and reliability measured using Cohen’s Kappa. \\ \hline MANOVA & [14] & Multivariate ANOVA (MANOVA) was used for the evaluation of how workers’ visual attention was affected by their hazard-identification skills. The dependent variables included eye movement metrics, and the independent variables were hazard-identification skills. Separate ANOVAs and a discriminant function analysis were performed on the dependent variables. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Techniques for reducing dataset dimensionality. methods #C1, #C2, #C3, and CIP (see Table 1). PCA, LDA, and MANOVA are used to analyze the multivariate regressions and variances in the influence factors of the estimation of in-place concrete strength. Common performance indicators (like a confidence interval, correlation value, r, R-squared value, root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE)) are calculated to evaluate the accuracy of the predictions of the models. The definitions and details of the indicators are explained in the Research Methodology section. The PCA algorithm in this research takes datasets of points in a multiple-dimensional space, for example, values of multiple independent variables like curing days, specimen type, season, method, and temperature, as the input. The output is a set of principal components, which have special directions that capture the most important patterns and variances in the datasets in the dimensional space. The algorithm is based on a covariance matrix of eigenvalues, which indicate how much variance is captured by each eigenvector, while eigenvectors represent the directions of the variables in the dimensional space. One limitation of PCA is the assumption of correlations among features [10; 11]. For example, if the curing days, specimen type, season, method, and temperature components are unrelated, PCA cannot determine the principal component. In this research, the components were identified based on the literature review and empirical data. LDA takes a dataset of points in n-dimensional space as the input, with the output as a set of principal components, which are the directions in that n-dimensional space capturing the most important patterns and variances in the dataset. Different from PCA, which is an unsupervised technique that disregards class labels within the data, LDA is a supervised approach, operating under the assumptions of normally (Gaussian) distributed data and consistent covariance matrices across each class. For example, after calculating the eigenvalues and eigenvectors of the covariance matrix of the components, LDA sorts the eigenvalues in descending order and selects the top k principal components. One limitation of LDA is that regressions cannot be logistic [13]. This research uses hypothesis testing to verify the covariance regression types for LDA. Additionally, PCA was performed before LDA to avoid over-fitting and regularize the problem.