## 3 Case Study
### Modeling Preparation  
Based on the input feature variables identified in Section 2.2.1, the dataset needs to cover various scenarios, such as multiple strength grades of concrete, different pouring seasons, and various temperature control strategies, to ensure the model's generalization ability [41; 42; 43; 44]. Dam section 7 is in the abutment area, while sections 11 and 17 are in the central region. A dataset comprising 36 blocks from these sections was selected to develop the concrete temperature prediction model. These blocks included different materials (C40, C35, and C30 cement), seasonal variations in pouring time throughout the year (hot season from September to October and cold season from December to January), dynamic temperature control strategies (cooling water temperatures of 10-12 \({}^{\circ}\)C and 14-16 \({}^{\circ}\)C in the hot and cold seasons, respectively), and seasonal maintenance measures (thicker insulation in the cold season, spray moisturization, and water flow conservation in the hot season). The temperature data were preprocessed to construct a time-series database with a sampling interval of 0.5 days, covering the entire cooling period (0-120 days). Of the 8640 data points processed, missing values and outliers accounted for about 0.05% of the total data. Figure 8: Fiber embedding scheme: (**a**) fiber embedding method in 11-008; (**b**) cross-sectional view on I-I. Figure 7: Distribution of fiber burial locations. Numbers in circles dam section numbers. Lines in different colors denote optical fibers connected to different DTS hosts. Data from 28 pouring blocks were randomly selected as the model's training set, while the remaining 8 were designated as the test set (ratio of about 8:2). Table 1 shows the statistical properties of the different feature variables of the dataset, which ensures the consistency of the statistical properties among them. During the modeling process, sample data were normalized between 0 and 1, and the output results were denormalized. Data preparation was carried out using a database, and model construction and prediction were performed in Python 3.11.0. The computational core used for this study was an R5-5600H CPU at 3.3 GHz with 16 GB of RAM. Previous studies have demonstrated that the radial basis function (RBF) kernel is one of the most commonly used and widely validated kernel functions for modeling nonlinear data with small sample sizes [46; 47]. During the model training process, the RBF kernel provided a favorable balance between computational efficiency and prediction accuracy. Therefore, the RBF kernel function was chosen. The ISVR model's hyperparameter ranges were determined as \(c\in\)[1; 20] and \(\gamma\in\) [0.01, 10] based on similar works and multiple tests in this study [48]. The hyperparameters \(c\) and \(\gamma\) of the ISVR model were optimized using SSA. The SSA fitness function is the mean absolute error (MAE) between the predicted and measured values. The control parameters include the population size (pop) and the maximum number of iterations (mit), which are set to pop = 100 and mit = 20, respectively. The SSA is designed to be robust and efficient, and the maximum number of iterations is set to \(c\) and \(\gamma\). The final optimized values of \(c\) and \(\gamma\) in the initial model were set to 4.722 and 0.062, respectively. In the online update of the model, the KKT condition was configured with a sample size limit of 1000 and a prediction error threshold of 0.5 \({}^{\circ}\)C for new samples to balance computational efficiency and accuracy. Table 2 compares the impact of different values of the similarity weighting coefficient \(\beta\) on the model's prediction accuracy.