Long-span bridges, often exposed to challenging harsh natural environments with severe weather conditions, necessitate real-time examination of load-deformation characteristics to ensure structural integrity and safety. Previous studies have primarily focused on investigating the causes of deformation in bridge structures under different single-load conditions during severe natural disasters, utilizing physics-based, mechanics-based, and data-driven methods. However, these methods cannot achieve fully achieve effective analysis of the real-time effects of multi-factor loads on bridge deformation, particularly in the presence of dynamic and simultaneous loads such as wind or temperature variations. A novel data-driven method is proposed based on a state-of-the-art real-time updating artificial neural networks (ANNs) algorithm to investigate the real-time coupling relationship between multi-loads and bridge deformation, enabling real-time prediction of bridge deformations. Additionally, the real-time characteristics between structural deformation and multi-loads are explained by incorporating SHapley Additive exPlanation (SHAP) in harsh natural environments. The proposed method has been validated on the 1,006-meter Forth Bridge in Scotland, showing high accuracy in real-time displacement prediction. The 9-day testing dataset demonstrated the values for Y and Z direction deformations were found to be 0.98 and 0.87, respectively. The performance metrics for each day indicated that the majority of Y and Z direction deformations had values exceeding 0.8, with RMSE and MAE values below 30 mm. The SHAP analysis revealed that an increase in wind speed leads to intensified Y direction deformation (larger SHAP values), while temperature has a significant impact on Z direction deformation (smaller SHAP values). Moreover, the weight influences of each load on the deformation are not fixed. The study's findings demonstrate that the proposed method enables accurate long-term prediction and assessment, allowing precise monitoring and prevention of abnormal risks in bridges under harsh environmental conditions.

中文翻译：

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基于实时更新人工神经网络的恶劣自然环境下大跨桥梁荷载变形特性检验

大跨度桥梁经常暴露在恶劣的自然环境和恶劣的天气条件下，需要实时检查荷载变形特性，以确保结构的完整性和安全性。以往的研究主要集中于利用基于物理、基于力学和数据驱动的方法，研究严重自然灾害期间不同单荷载条件下桥梁结构变形的原因。然而，这些方法无法完全实现多因素荷载对桥梁变形的实时影响的有效分析，特别是在存在风或温度变化等动态和同时荷载的情况下。基于最先进的实时更新人工神经网络（ANN）算法，提出了一种新颖的数据驱动方法，以研究多荷载与桥梁变形之间的实时耦合关系，从而实现实时预测的桥梁变形。此外，通过在恶劣的自然环境中结合 SHapley Additive exPlanation (SHAP) 来解释结构变形和多载荷之间的实时特性。该方法已在苏格兰1006米福斯大桥上得到验证，显示出实时位移预测的高精度。 9 天的测试数据集显示 Y 方向和 Z 方向变形值分别为 0.98 和 0.87。每天的性能指标表明，大多数 Y 和 Z 方向变形的值超过 0.8，RMSE 和 MAE 值低于 30 毫米。 SHAP分析表明，风速的增加会导致Y方向变形加剧（SHAP值较大），而温度对Z方向变形影响显着（SHAP值较小）。此外，每个载荷对变形的重量影响不是固定的。研究结果表明，所提出的方法能够实现准确的长期预测和评估，从而能够在恶劣的环境条件下精确监测和预防桥梁的异常风险。