Abstract: This paper constructs a surrogate model for load inversion and response prediction of tubular joints based on sparse measurement data by integrating two-dimensional Chebyshev orthogonal polynomials and a radial basis function (RBF) neural network, to address the challenge of dynamic monitoring and structural response prediction for the service status of tubular joints in offshore jacket platforms and achieve accurate load identification and rapid response prediction. First, given the complexity of the surfaces at key tubular joints of the jacket platform, two-dimensional Chebyshev orthogonal polynomials are used to mathematically represent the distributed loads acting on these surfaces. Combined with truncated singular value decomposition (TSVD) and generalized cross-validation (GCV), high-precision and stable load identification is achieved from pre-set sparse measurement response data. Then, based on the inversion results, a high-quality training sample set consisting of 100 working conditions, covering spatial coordinates and load characteristic coefficients, is generated. Each sample uses spatial node coordinates and load coefficients as input features, with the corresponding equivalent stress as output. Finally, the RBF surrogate model is constructed and trained, with the coefficient of determination (R²) used to evaluate model performance. The results show that the model achieves an R² of 0.913 78 on the validation set, enabling high-precision and high-generalization prediction of the stress field at key tubular joints of the jacket platform, providing effective technical support for real-time condition assessment and fatigue damage accumulation prediction of the jacket platform.

Key words: jacket, tubular joint, response prediction, load identification, surrogate model

摘要： 针对海洋导管架平台管节点服役状态的动态感知与响应预测难题,该文通过融合二维切比雪夫正交多项式和径向基函数（radial basis function,RBF）神经网络,构建了一种基于稀疏测点响应数据的导管架平台管节点的载荷反演与响应预测代理模型,实现管节点载荷的准确识别与结构响应的快速预测。首先,针对导管架平台关键管节点处曲面的复杂性,通过二维切比雪夫正交多项式数学表征作用在其表面的分布载荷,并结合截断奇异值正则化（truncated singular value decomposition,TSVD）与广义交叉验证（generalized cross-validation,GCV）方法,从预设的稀疏测点响应数据中实现载荷的高精度、稳定反演;进而,基于反演结果生成包含100组工况、涵盖空间坐标与载荷特征系数的高质量训练样本集,每组样本以空间节点坐标与载荷系数为输入特征,以对应等效应力为输出;最后,构建并训练RBF神经网络代理模型,并通过决定系数（R²）来评估模型性能。结果表明,该模型在验证集上的R²达到0.913 78,能够实现导管架平台关键管节点应力场的高精度、高泛化能力预测,为导管架平台的实时状态评估与疲劳损伤累积预测提供了有效技术支撑。

关键词: 导管架, 管节点, 响应预测, 载荷识别, 代理模型