Estimating the load distribution of a bridge structure enables to evaluate the in-service state and predict the structural responses. This paper develops an iterative strategy to inversely estimate the traffic load distribution of a bridge from limited measurements. The computer vision technologies, including the YOLO network-based object detection and a pixel coordinate-based positioning approach, are used to locate the vehicle positions on the bridge deck and form a prior information vector of the input positions. Then, a generalized Tikhonov regularization method is proposed to estimate the load distribution using the bridge response and prior information. The regularization parameter is determined by the L-curve method. The fusion of computer vision and regularization can improve the load identification accuracy and reduce the overfitting effect. The developed approach is applied to numerical and experimental examples under various load conditions. The load can be accurately identified in all cases, and the full-field responses of the structures can be reconstructed with minor errors.

中文翻译：

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考虑计算机视觉提取先验信息的桥梁一般吉洪诺夫正则化荷载估计

估算桥梁结构的载荷分布可以评估在役状态并预测结构响应。本文开发了一种迭代策略，从有限的测量中逆向估计桥梁的交通载荷分布。计算机视觉技术，包括基于 YOLO 网络的目标检测和基于像素坐标的定位方法，用于定位车辆在桥面上的位置，并形成输入位置的先验信息向量。然后，提出了一种广义的 Tikhonov 正则化方法，以使用桥梁响应和先验信息来估计负载分布。正则化参数由 L 曲线方法确定。计算机视觉与正则化的融合可以提高负载识别精度，减少过拟合效应。开发的方法应用于各种负载条件下的数值和实验示例。在所有情况下都可以准确识别载荷，并且可以重建结构的全场响应，误差很小。