沥青混合料的动态模量是沥青路面设计的一个重要参数。利用集成方法从大量的沥青混凝土数据集中提取材料特性、动态模量和相位角信息，对优化沥青路面性能具有重要意义。极限梯度提升模型（extreme gradient boost, XGBoost）通过加权求和聚合一系列决策树模型，构建了一个强大的预测模型，同时通过优化损失函数将预测误差降至最低。为了进一步提高动态模量和相位角预测的准确性，使用启发式算法对模型进行了优化。最初，基于样本初始化基础模型，并计算训练数据的损失函数的梯度。随后，XGBoost利用梯度细节构建决策树模型，优化叶节点权重，并通过加权求和更新模型的预测。在此过程中，使用启发式算法对整个XGBoost模型的最佳参数进行优化。实验结果表明，改进的XGBoost模型在所有性能评价指标上都优于原模型，提高了预测沥青混合料动态模量和相位角的准确性。

The dynamic modulus of asphalt mixture is an important parameter in the design of asphalt pavement. Extracting material characteristics, dynamic modulus, and phase angle information from a large amount of asphalt concrete datasets using integrated methods is of great significance for optimizing the performance of asphalt pavement. The extreme gradient boost (XGBoost) model aggregated a series of decision tree models through weighted summation to construct a powerful prediction model, while optimizing the loss function to minimize prediction errors. In order to further improve the accuracy of dynamic modulus and phase angle prediction, heuristic algorithms were used to optimize the model. Initially, the basic model was initialized based on samples and the gradient of the loss function of the training data was calculated. Subsequently, XGBoost utilized gradient details to construct a decision tree model, optimized leaf node weights, and updated the model's predictions through weighted summation. During this process, heuristic algorithms are used to optimize the optimal parameters of the entire XGBoost model. The experimental results show that the improved XGBoost model outperforms the original model in all performance evaluation indicators, improving the accuracy of predicting the dynamic modulus and phase angle of asphalt mixtures.