Structural health monitoring (SHM) systems have been widely implemented in long‐span bridges to measure various structural responses. It is difficult to directly perform condition assessment from the variations in structural responses because of initial residual stress and defaults, coupling effects of structure damage and external loads, and others. To fix these problems, this study proposes global and partial bidirectional long short‐term memory (BiLSTM) models to establish a relationship between girder vertical deflection (GVD) and cable tension (CT). First, a global BiLSTM model is built using all the measured signals of GVD and CT, and the test results show that the average root mean square error (RMSE) and relative RMSE (RRMSE) between the predicted and ground‐truth CTs are 1.83 kN and 3.19%, respectively. Second, the effects of both traffic volume and noise level on the model prediction error are investigated, indicating that the proposed method is robust to different noise levels and traffic volumes under normal operational conditions. Finally, to customize a prediction model for a certain CT, a group of partial BiLSTM models is further constructed with only a few GVDs as inputs and a single CT as output. Sobol's sensitivity index is adopted as the indicator to select the most significant inputs among all the GVD sensors. The partial model is more efficient for training because of significantly fewer channels in each layer and model parameters. The prediction results show that the partial models can achieve an average RMSE and RRMSE of 1.86 kN and 3.24%, respectively, which are similar to the prediction accuracy of the global model. In addition, the partial model can be applied if certain sensors are out of order when compared to the global model.

中文翻译：

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利用斜拉桥的现场监测数据通过BiLSTM建立车辆引起的大梁竖向挠度与拉力之间的关系模型

结构健康监测（SHM）系统已广泛应用于大跨度桥梁中，以测量各种结构响应。由于初始残余应力和默认值，结构损坏和外部载荷的耦合效应等原因，很难根据结构响应的变化直接进行状态评估。为了解决这些问题，本研究提出了全局和局部双向长短期记忆（BiLSTM）模型，以建立大梁垂直挠度（GVD）和电缆张力（CT）之间的关系。首先，使用GVD和CT的所有测量信号建立一个全局BiLSTM模型，测试结果表明，预测CT和地面真实CT之间的平均均方根误差（RMSE）和相对RMSE（RRMSE）为1.83 kN和3.19％。第二，研究了交通量和噪声水平对模型预测误差的影响，表明该方法在正常运行条件下对不同的噪声水平和交通量具有鲁棒性。最后，为了为某个CT定制预测模型，进一步构建了一组部分BiLSTM模型，仅将几个GVD作为输入，将单个CT作为输出。Sobol的灵敏度指标被用作选择所有GVD传感器中最重要输入的指标。由于每个层和模型参数中的通道明显较少，因此局部模型对于训练更有效。预测结果表明，部分模型可以分别达到1.86 kN和3.24％的平均RMSE和RRMSE，这与全局模型的预测精度相似。此外，