## 1 Introduction  
Temperature control and crack prevention in concrete during the construction of arch dams have long been challenging issues in the engineering field [1, 2]. Cracks directly impact the load-bearing capacity and durability of the structure, and temperature stress is one of the primary causes of concrete cracking during the construction period [3]. In the actual construction process of arch dams, the pouring block serves as the smallest temperature control unit. Typically, temperature control strategies for each block are formulated based on the monitored temperature within that block. However, concrete temperature is influenced by various time-varying factors, including material properties, age, heat dissipation conditions, and temperature control measures [4, 5, 6]. Engineers often find it difficult to quantitatively determine temperature control parameters and their effectiveness based on real-time monitoring data, which frequently results in temperature control quality indicators exceeding standards, posing potential risks for concrete cracking. Therefore,constructing a rapid and accurate temperature prediction model and investigating the influence patterns of concrete temperature factors are crucial for crack prevention in dam concrete during construction. Currently, the methods for predicting the temperature of dam concrete can be broadly categorized into two types: mechanism-driven and data-driven approaches. The former primarily relies on rigorous mathematical derivations and employs numerical methods, such as the finite element method (FEM), to simulate the mechanisms of cement hydration heat release, water pipe cooling, surface heat dissipation, and other factors to obtain the concrete temperature variations over time [7; 8; 9; 10]. This method exhibits strong interpretability, enabling the quantitative analysis of the relationship between various influencing factors and concrete temperature. However, this approach faces bottlenecks such as extensive computational demands and challenges in dynamic parameter calibration under complex scenarios like dam sites [11; 12; 13], often falling short of meeting the requirements for efficient temperature prediction. In recent years, advancements in information technology have significantly enriched the empirical data available for data-driven prediction methods. This progress has catalyzed extensive research in dam structural health monitoring, yielding promising outcomes [14; 15; 16]. In the context of concrete dam temperature prediction, [PERSON] et al. [17; 18; 19; 20] selected pour temperature, pipe spacing, water flow rate, water temperature, and ambient air temperature at the construction site as input vectors, with the maximum temperature as the output vector, to establish a rapid predictive model for the maximum temperature of concrete in blocks using various neural networks, including a random forest, BP, and the radial basis function. The average mean absolute error (\(MAE\)) of the predicted results was 0.32 \({}^{\circ}\)C. [PERSON] [21] proposed a model incorporating the Java optimizer, the salp swarm algorithm, and the least squares support vector machine to simulate temperature effects in dam health monitoring. [PERSON] [22] introduced an interval prediction method for concrete temperature fields based on a hybrid kernel relevance vector machine (HK-RVM) and an improved grasshopper optimization algorithm (IGOA). [PERSON] [2] developed a short-term predictive model for concrete during dam construction using ANN, with an average root mean square error (\(RMSE\)) of 0.15 \({}^{\circ}\)C for prediction accuracy. The instantaneous temperature field of concrete during the construction of arch dams is the result of the combined effects of various dynamic factors, including environmental conditions, cement hydration, and construction techniques. Offline models typically treat these dynamic factors as static, making it difficult for such models to capture the evolving characteristics of the temperature field [23].