In this weigh-in-motion(WIM)research,a novel fiber Bragg grating(FBG)-based weigh-in-motion(WIM)system was introduced.The design derived from the idea using in-service bridge abutments as the weigh scale.The bridge be...In this weigh-in-motion(WIM)research,a novel fiber Bragg grating(FBG)-based weigh-in-motion(WIM)system was introduced.The design derived from the idea using in-service bridge abutments as the weigh scale.The bridge beam was replaced by a piece of steel plate which supports the weight of the traveling vehicle.All weights would be finally transferred into the tubes where four FBGs were attached and could record the weight-induced strains by shifting their Bragg wavelengths.The system identification algorithm based on parameters estimation was initiated.Over 40-ton load had been applied on the system and the experimental results showed a good repeatability and linearity.The system resolution had been achieved as low as 10 kg.Compared with other designs of fiber-optic WIM systems,this design is easy and reliable.展开更多
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 l...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.展开更多
文摘In this weigh-in-motion(WIM)research,a novel fiber Bragg grating(FBG)-based weigh-in-motion(WIM)system was introduced.The design derived from the idea using in-service bridge abutments as the weigh scale.The bridge beam was replaced by a piece of steel plate which supports the weight of the traveling vehicle.All weights would be finally transferred into the tubes where four FBGs were attached and could record the weight-induced strains by shifting their Bragg wavelengths.The system identification algorithm based on parameters estimation was initiated.Over 40-ton load had been applied on the system and the experimental results showed a good repeatability and linearity.The system resolution had been achieved as low as 10 kg.Compared with other designs of fiber-optic WIM systems,this design is easy and reliable.
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)+1 种基金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).
文摘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.
