• Smart Structures and Systems
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• v.9 no.3
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• pp.287-301
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• 2012

It is well known that overloaded vehicles may cause severe damages to bridges, and how to estimate and evaluate the status of the overloaded vehicles passing through bridges become a challenging problem. Therefore, based on the monitored strain data from a structural health monitoring system (SHM) installed on a bridge, a method is recommended to identify and analyze the probability of overloaded vehicles. Overloaded vehicle loads can cause abnormity in the monitored strains, though the abnormal strains may be small in a concrete continuous rigid frame bridge. Firstly, the abnormal strains are identified from the abundant strains in time sequence by taking the advantage of wavelet transform in abnormal signal identification; secondly, the abnormal strains induced by heavy vehicles are picked up by the comparison between the identified abnormal strains and the strain threshold gotten by finite element analysis of the normal heavy vehicle; finally, according to the determined abnormal strains induced by overloaded vehicles, the statistics of the overloaded vehicles passing through the bridge are summarized and the whole probability of the overloaded vehicles is analyzed. The research shows the feasibility of using the monitored strains from a long-term SHM to identify the information of overloaded vehicles passing through a bridge, which can help the traffic department to master the heavy truck information and do the damage analysis of bridges further.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1794-1799
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• 2022

Recently, the increasing number of overloaded vehicles on the road poses a risk to traffic safety, such as falling objects, road damage, and chain collisions due to the abnormal weight distribution, and can cause great damage once an accident occurs. therefore we propose to build an object detection-based AI model to identify overloaded vehicles that cause such social problems. In addition, we present a simple yet effective method to construct an object detection model for the large-scale vehicle images. In particular, we utilize the large-scale of vehicle image sets provided by open AI-Hub, which include the overloaded vehicles. We inspected the specific features of sizes of vehicles and types of image sources, and pre-processed these images to train a deep learning-based object detection model. Also, we propose an integrated system for tracking the detected vehicles. Finally, we demonstrated that the detection performance of the overloaded vehicle was improved by about 23% compared to the one using raw data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.562-565
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• 2022

Recently, the increasing number of overloaded vehicles on the road poses a risk to traffic safety, such as falling objects, road damage, and chain collisions due to the abnormal weight distribution, and can cause great damage once an accident occurs. However, this irregular weight distribution is not possible to be recognized with the current weight measurement system for vehicles on roads. To address this limitation, we propose to build an object detection-based AI model to identify overloaded vehicles that cause such social problems. In addition, we present a simple yet effective method to construct an object detection model for the large-scale vehicle images. In particular, we utilize the large-scale of vehicle image sets provided by open AI-Hub, which include the overloaded vehicles from the CCTV, black box, and hand-held camera point of view. We inspected the specific features of sizes of vehicles and types of image sources, and pre-processed these images to train a deep learning-based object detection model. Finally, we demonstrated that the detection performance of the overloaded vehicle was improved by about 23% compared to the one using raw data. From the result, we believe that public big data can be utilized more efficiently and applied to the development of an object detection-based overloaded vehicle detection model.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2008.04a
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• pp.548-553
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• 2008

Overloaded Vehicles are one of biggest of hazard in durability decrease of roads and bridges. Thus, regulation was put in force about overloaded vehicles to reserve this problem. However, existing system had many problems. For these reasons, this paper presents solutions of U-intelligent overload vehicles regulation system based on manless and wireless for fixing of problems of existing system and construction of ITS. With this in mind, we studied about composition method of system, applications of USN, design of system controller, WCDMA/ HSDPA, etc in this paper.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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• pp.831-836
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• 2007

Overloaded Vehicles are one of biggest of hazard in durability decrease of roads and bridges. Thus, regulation was put in force about overloaded vehicles to reserve this problem. However, existing system had many problems. For these reasons, this paper presents solutions of U-intelligent overload vehicles regulation system based on manless and wireless for fixing of problems of existing system and construction of ITS. With this in mind, we studied about composition method of system. applications of USN, design of system controller, WCDMA/HSDPA, etc in this paper.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.1D
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• pp.127-132
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• 2011

Load managing method on road became necessary since overloaded vehicles occur damage on road facilities and existing systems for preventing this damage still show many problems. Accordingly, efficient managing system for preventing overloaded vehicles could be organized by using the road itself as a scale by applying genetic algorithm to analyze the load and the drive information of vehicles. First of all, accurate analysis of load using the behavior of road itself is needed for solving illegal axle manipulation problems of overloaded vehicles and for installing intelligent embedded load analyzing system. Accordingly in this study, to use the behavior of road, the transformation was measured by installing underground box type indoor model and indoor experiment was held using genetic algorithm and 10% error were checked.

• Journal of Drive and Control
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• v.18 no.1
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• pp.9-16
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• 2021

Overloaded vehicles increase the maintenance cost of road structures, and they are a major factor in causing damage to the roads and bridges. In addition, overloaded vehicles compromise the braking capability of the vehicle; thus, threatening the safety of the driver. In order to prevent overloading of vehicles, the government is cracking down on the roads by using a device that measures the weight of vehicles. But this process is inconvenient because the place where the equipment is installed is far away from where the cargo is loaded. Due to the limitations of these fixed weighing devices, there is a growing need for technology that can monitor vehicle weight distribution and overload conditions in real time. In this work, we develop an onboard scale that can measure the load (weight) of trucks in real time. The onboard scale consists of high sensors, a signal processing unit, and a display, and it measures the load using height-displacement of the vehicle's leaf spring suspension.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1992.04a
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• pp.126-133
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• 1992

The structural soundness of bridges is mainly damaged by overloaded heavy vehicles. The increasing volumes of overloaded heavy vehicles has been indicated as serious state. As results several countries have revised their bridge load codes. However, because of variety of truck types and their weights it is difficult to develop rational standard truck loads. In addition the common practice that only one design configuration of standard truck is adopted to design variety of bridges causes further difficulties. The objective of the study is to investigate the statistical characteristics of vehicle loadings based on survey data col looted, in which some major factors, such as vehicle configurations, vehicle freights, traffic modes, etc. are incorporated.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.20 no.3
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• pp.387-392
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• 2007

Overloaded Vehicles are one of biggest of hazard in durability decrease of roads and bridges. Thus, regulation was put in force about overloaded vehicles to reserve this problem. However, existing system had many problems. For these reasons, this paper presents solutions of U-intelligent overload vehicles regulation system based on manless and wireless for fixing of problems of existing system and construction of u-lTS. With this in mind, we studied about composition method of system, applications of USN, design of system controller, WCDMA/HSDPA and we verified performance of WIM Sensors in this paper.

• Journal of the Korean Society for Precision Engineering
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• v.34 no.3
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• pp.179-184
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• 2017

Recently, traffic accidents and damage on the highway have increased because of overloaded vehicles. The existing overload-detecting system has a low accuracy rate. An overload-detecting system using a weigh-in-motion (WIM) system has been developed to solve this problem. The WIM system can be used to detect overloaded vehicles by measuring the weight of the vehicles. The WIM system is divided into high-speed and low-speed types. The inaccuracy rate in the low-speed WIM system results mainly from the low response rate of the sensor when the velocity is moving at more than 20 km/h. In this study, a low-speed overload-detecting pad with a hydraulic structure using a WIM system was developed to make the system more accurate. The structural and formal analysis was carried out by using a finite element method (FEM) in order to analyze the structural stability and the extrusion velocity of the system. In addition, a static load test was performed to confirm the linearity and accuracy of the pad.