This paper presents a digital twin (DT) platform integrated with an online optimization algorithm that combines Bayesian Optimization (BO), Categorical Boosting (CatBoost), and the Nondominated Sorting Genetic Algorithm (NSGA)-III. The platform enables multi-objective dynamic optimization of shield parameters under varying geological conditions. Using the Wuhan Metro as a case study, the effectiveness of the method is validated. The results demonstrate that: (1) the DT model accurately estimates shield machine performance, with an R2 of no less than 0.957 on the test set across three geological conditions; (2) the online optimization significantly enhances shield machine performance, with a comprehensive optimization improvement of over 25 % across all conditions; (3) comparison of the constructed algorithm's accuracy, along with Shapley additive explanations, confirms the accuracy, interpretability, and universality of the proposed algorithm.

中文翻译：

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使用混合算法和数字孪生平台对盾牌参数进行智能动态控制


本文提出了一个数字孪生 （DT） 平台，该平台与在线优化算法集成，该算法结合了贝叶斯优化 （BO）、分类提升 （CatBoost） 和非支配排序遗传算法 （NSGA）-III。该平台支持在不同地质条件下对盾构参数进行多目标动态优化。以武汉地铁为例，验证了该方法的有效性。结果表明：（1）DT模型准确估计了盾构机的性能，在3种地质条件下，测试集上的R2不小于0.957;（2） 在线优化显著提高了盾构机的性能，在所有条件下的综合优化提高了 25% 以上;（3） 对构建算法的准确性进行比较，以及 Shapley 加法解释，证实了所提算法的准确性、可解释性和普遍性。