为提高变深度水下曲线顶管施工中推力预测的精度，并解决掘进参数优化过度依赖人工经验的问题，以“长江口二号”古沉船打捞工程为切入点，探究融合物理-数据驱动的智能辅助决策方法。依托现场实际工程，提取包含贯入度、推力切深指数（FPI）等关键参数的2 698组有效施工数据，通过混合异常值清洗策略构建标准数据集； 选用随机森林（RF）与遗传算法优化神经网络（GA-NN）构建多输入、单输出的推力预测土-机映射模型，并通过5折交叉验证评估其泛化能力； 以推力（≤4 000 kN）与刀盘转速的安全控制范围为物理约束边界，以最大化掘进速度为优化目标，引入蜣螂优化算法（DBO）进行参数全局寻优。在推力预测方面，随机森林映射模型在测试集上的平均绝对误差（EMA）为68.41 kN，决定系数（R2）达0.912，显著优于GA-NN（EMA=86.12 kN）及传统理论模型，表明集成学习对水下复合地层及非线性表格数据的特征捕捉能力更强； 在参数优化方面，对于选取的测试样本，混合智能模型（RF+DBO）能在不突破安全推力上限的前提下，将掘进速度均值由实际操作的30.44 mm/min提升至设计上限的35.00 mm/min，平均提效达14.99%。受地层力学特性影响，青灰泥地层中的优化提升空间（约17.2%）显著高于铁板砂地层（约11.3%）。该物理-数据双驱动模型能够精准刻画水下变深度曲线顶管的非线性动力响应规律，在保障极高环境安全标准的前提下，通过智能寻优有效纠正人工操作的保守性，在安全与高效之间实现最优博弈。
Based on the salvage project of the Yangtze Estuary No. 2 shipwreck, this study investigates an intelligent decision-making method that integrates physical priors and data-driven models. The aim of the study is to improve the accuracy of thrust prediction in variable-depth underwater curved pipe jacking and solve the over-reliance on manual experience for tunneling parameter optimization under multiple complex constraints. A series of 2 698 sets of valid construction data, including key parameters such as penetration rate and field penetration index, are extracted, and a standard dataset is constructed using a hybrid outlier cleaning strategy. Random forest (RF) and genetic algorithm-neural network (GA-NN) are used to build multi-input, single-output soil-machine mapping models for thrust prediction, and their generalization abilities are evaluated via fivefold cross-validation. Taking the safety control ranges of thrust (≤4 000 kN）and cutterhead rotational speed as physical constraint boundaries, and maximizing the advance rate as the objective, the dung beetle optimizer (DBO) is introduced for global parameter optimization. For thrust prediction, the RF mapping model achieves a mean absolute error (EMA) of 68.41 kN and an R2of 0.912 on the testing set, notably outperforming GA-NN (EMA=86.12 kN) and traditional theoretical models. These results demonstrate the superiority of ensemble learning in capturing nonlinear features in underwater composite strata. For parameter optimization, the hybrid intelligent model (RF+DBO) successfully increases the mean advance rate of the test samples from the actual 30.44 mm/min to the design upper limit of 35.00 mm/min without exceeding the safety thrust limit, achieving an average efficiency improvement of 14.99%. Influenced by stratum mechanics, the optimization improvement space in the soft bluish-gray mud stratum (~17.2%) is markedly higher than that in the hard iron-sand stratum (~11.3%). This physics-data-driven model accurately depicts the nonlinear dynamic response laws of variable-depth curved pipe jacking. This method corrects the conservatism of manual operations through intelligent optimization under extremely high environmental safety standards, thereby achieving an optimal tradeoff between safety and efficiency.
