Deep Sensing Approach to Single-Sensor Vehicle Weighing System on Bridges

Bridge weigh-in-motion (BWIM) is a technique for detecting overloaded vehicles crossing a bridge without requiring them to stop. It may also be useful for monitoring the structural health of the bridge itself. To achieve accurate weighing of each vehicle, its properties, such as speed, locus, and wheel positions, should be estimated in advance. Conventionally, such information has been obtained via additional sensors such as cameras or via peak-signal detection, using multiple sensors installed across the bridge. This may require substantial computational resources or expensive synchronization between sensors, and the complexity of the overall BWIM system may lead to frequent breakdowns. In this paper, we propose a single-sensor-based BWIM system that utilizes a deep neural network. First, a vehicle’s properties are obtained via feature extraction from the bridge strain response, as sampled by a single strain sensor. BWIM is then performed, using the same response data. The model parameters for vehicle detection are optimized automatically by consulting a surveillance camera while obtaining ground-truth data for a large number of vehicles crossing the bridge. After the model is optimized for the target bridge, the camera may be removed. Our proposal paves the way toward low-cost, compact, and single-sensor BWIM systems.

[1]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[2]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Lu Deng,et al.  Vehicle axle identification using wavelet analysis of bridge global responses , 2017 .

[4]  Wei Dai,et al.  Very deep convolutional neural networks for raw waveforms , 2016, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[5]  Chitoshi Miki,et al.  Simplified Portable Bridge Weigh-in-Motion System Using Accelerometers , 2018 .

[6]  Chudong Pan,et al.  Identification of moving vehicle forces on bridge structures via moving average Tikhonov regularization , 2017 .

[7]  C. S. Cai,et al.  State-of-the-art review on bridge weigh-in-motion technology , 2016 .

[8]  Bin Zhang,et al.  Study on CNN in the recognition of emotion in audio and images , 2016, 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS).

[9]  Jian Sun,et al.  Identity Mappings in Deep Residual Networks , 2016, ECCV.

[10]  Arturo González,et al.  Bridge Damage Detection Using Weigh-In-Motion Technology , 2015 .

[11]  Nasim Uddin,et al.  Identification of Vehicular Axle Weights with a Bridge Weigh-in-Motion System Considering Transverse Distribution of Wheel Loads , 2014 .

[12]  Chitoshi Miki,et al.  MECHANISM FOR DEVELOPING LOCAL STRESS AT THE CONNECTION DETAILS IN STEEL BRIDGE STRUCTURES , 1995 .

[13]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[14]  Rich Caruana,et al.  Multitask Learning , 1998, Encyclopedia of Machine Learning and Data Mining.

[15]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[16]  Sanjiv Kumar,et al.  On the Convergence of Adam and Beyond , 2018 .

[17]  S. K. Leming,et al.  Bridge weigh-in-motion system development using superposition of dynamic truck/static bridge interaction , 2003, Proceedings of the 2003 American Control Conference, 2003..

[18]  Yusuke Kobayashi,et al.  LONG-TERM MONITORING OF TRAFFIC LOADS BY AUTOMATIC REAL-TIME WEIGH-IN-MOTION , 2004 .

[19]  Huiping Jiang,et al.  Classification of EEG signal by WT-CNN model in emotion recognition system , 2017, 2017 IEEE 16th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC).

[20]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Eugene J. O'Brien,et al.  On the use of bridge weigh–in–motion for overweight truck enforcement , 2014 .

[22]  Myra Lydon,et al.  Recent developments in bridge weigh in motion (B-WIM) , 2016 .

[23]  Atsuhiro Takasu,et al.  Traffic Surveillance System for Bridge Vibration Analysis , 2017, 2017 IEEE International Conference on Information Reuse and Integration (IRI).

[24]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[25]  Samir Mustapha,et al.  Non-intrusive schemes for speed and axle identification in bridge-weigh-in-motion systems , 2017 .

[26]  Yoshua Bengio,et al.  Deep Sparse Rectifier Neural Networks , 2011, AISTATS.

[27]  Ken-ichi Funahashi,et al.  On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.

[28]  C. S. Cai,et al.  Novel Virtual Simply Supported Beam Method for Detecting the Speed and Axles of Moving Vehicles on Bridges , 2016 .

[29]  Eugene J. O'Brien,et al.  Contactless Bridge Weigh-in-Motion , 2016 .