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Hybrid‐data‐driven bridge weigh‐in‐motion technology using a two‐level sequential artificial neural network
Computer-Aided Civil and Infrastructure Engineering ( IF 8.5 ) Pub Date : 2025-01-06 , DOI: 10.1111/mice.13415
Wangchen Yan, Hao Ren, Xin Luo, Shaofan Li
Computer-Aided Civil and Infrastructure Engineering ( IF 8.5 ) Pub Date : 2025-01-06 , DOI: 10.1111/mice.13415
Wangchen Yan, Hao Ren, Xin Luo, Shaofan Li
For existing bridge weigh‐in‐motion technologies, the main challenge in accurate weight estimation is to overcome the difficulty of identifying the closely spaced axles. To do so, many field test data are generally required for each bridge in application. To address such a challenge, a novel two‐level sequential artificial neural network (ANN) model trained by the hybrid simulated‐experimental data was proposed in this study to identify the gross weight and axle weight. For this, simulations and scaled experiments were conducted for the vehicle–bridge interaction system to develop the sequential ANN model. The sequential ANN model was realized by a special data looping strategy, in which the outputs of the global‐level ANN served as partial inputs to the following local‐level ANN to predict the axle weight. The optimized size of the training data and the appropriate hybrid ratio of the sequential ANN model were also explored. Finally, the proposed algorithm was applied to a real bridge application via transfer learning, as the optimized hybrid sequential ANN model serves as the pre‐trained model. The results showed that for the small training datasets with only 5% experimental data, the proposed algorithm significantly improved the accuracy in weight estimation of moving vehicles with closely spaced axles. The field test demonstrated that the proposed algorithm also applies to different bridges within a gross weight identification error of 5%, showing the promise of the proposed algorithm in practical applications.
中文翻译:
使用两级顺序人工神经网络的混合数据驱动型桥式动态称重技术
对于现有的动态桥式称重技术,准确估计重量的主要挑战是克服识别紧密间隔的车轴的困难。为此,应用中的每座桥通常需要许多现场测试数据。为了应对这一挑战,本研究提出了一种由混合模拟实验数据训练的新型两级顺序人工神经网络 (ANN) 模型来识别总重和轴重。为此,对车桥交互系统进行了仿真和缩放实验,以开发顺序 ANN 模型。顺序 ANN 模型是通过一种特殊的数据循环策略实现的,其中全局级 ANN 的输出作为下一个局部级 ANN 的部分输入来预测轴重。还探讨了训练数据的优化大小和顺序 ANN 模型的适当混合比率。最后,通过迁移学习将所提出的算法应用于真实的桥梁应用,因为优化的混合顺序 ANN 模型作为预训练模型。结果表明,对于只有 5% 实验数据的小训练数据集,所提算法显著提高了车轴紧密移动车辆的重量估计精度。现场测试表明,所提算法也适用于不同桥梁,毛重识别误差为 5%,表明所提算法在实际应用中的前景广阔。
更新日期:2025-01-06
中文翻译:
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使用两级顺序人工神经网络的混合数据驱动型桥式动态称重技术
对于现有的动态桥式称重技术,准确估计重量的主要挑战是克服识别紧密间隔的车轴的困难。为此,应用中的每座桥通常需要许多现场测试数据。为了应对这一挑战,本研究提出了一种由混合模拟实验数据训练的新型两级顺序人工神经网络 (ANN) 模型来识别总重和轴重。为此,对车桥交互系统进行了仿真和缩放实验,以开发顺序 ANN 模型。顺序 ANN 模型是通过一种特殊的数据循环策略实现的,其中全局级 ANN 的输出作为下一个局部级 ANN 的部分输入来预测轴重。还探讨了训练数据的优化大小和顺序 ANN 模型的适当混合比率。最后,通过迁移学习将所提出的算法应用于真实的桥梁应用,因为优化的混合顺序 ANN 模型作为预训练模型。结果表明,对于只有 5% 实验数据的小训练数据集,所提算法显著提高了车轴紧密移动车辆的重量估计精度。现场测试表明,所提算法也适用于不同桥梁,毛重识别误差为 5%,表明所提算法在实际应用中的前景广阔。