Quick Weighing of Passing Vehicles Using the Transfer-Learning-Enhanced Convolutional Neural Network  

作　　者：Wangchen Yan Jinbao Yang Xin Luo 

机构地区：[1]College of Civil Engineering,Xiangtan University,Xiangtan,411105,China [2]Energy&Environmental Engineering Department,China CEC Engineering Corporation,Changsha,410114,China

出　　处：《Computer Modeling in Engineering & Sciences》2024年第6期2507-2524,共18页工程与科学中的计算机建模（英文）

基　　金：the financial support provided by the National Natural Science Foundation of China(Grant No.52208213);the Excellent Youth Foundation of Education Department in Hunan Province(Grant No.22B0141);the Xiaohe Sci-Tech Talents Special Funding under Hunan Provincial Sci-Tech Talents Sponsorship Program(2023TJ-X65);the Science Foundation of Xiangtan University(Grant No.21QDZ23).

摘　　要：Transfer learning could reduce the time and resources required by the training of new models and be therefore important for generalized applications of the trainedmachine learning algorithms.In this study,a transfer learningenhanced convolutional neural network(CNN)was proposed to identify the gross weight and the axle weight of moving vehicles on the bridge.The proposed transfer learning-enhanced CNN model was expected to weigh different bridges based on a small amount of training datasets and provide high identification accuracy.First of all,a CNN algorithm for bridge weigh-in-motion(B-WIM)technology was proposed to identify the axle weight and the gross weight of the typical two-axle,three-axle,and five-axle vehicles as they crossed the bridge with different loading routes and speeds.Then,the pre-trained CNN model was transferred by fine-tuning to weigh themoving vehicle on another bridge.Finally,the identification accuracy and the amount of training data required were compared between the two CNN models.Results showed that the pre-trained CNN model using transfer learning for B-WIM technology could be successfully used for the identification of the axle weight and the gross weight for moving vehicles on another bridge while reducing the training data by 63%.Moreover,the recognition accuracy of the pre-trained CNN model using transfer learning was comparable to that of the original model,showing its promising potentials in the actual applications.

关 键 词：Bridge weigh-in-motion transfer learning convolutional neural network 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]