Effects of Sensor Location on Dynamic Load Estimation in Weigh-in-Motion System
In recent years, weigh-in-motion systems based on embedded sensor networks have received a lot of attention. However, how to improve the accuracy of multi-sensor weigh-in-motion (WIM) systems while keeping costs low remains a challenge. In this paper, a numerical simulation method is presented to an...
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doaj-b71a022b659345a3b3e490a834d4d2dd2020-11-24T21:21:10ZengMDPI AGSensors1424-82202018-09-01189304410.3390/s18093044s18093044Effects of Sensor Location on Dynamic Load Estimation in Weigh-in-Motion SystemTianhao Qin0Mengxiang Lin1Ming Cao2Kaiya Fu3Rong Ding4State Key Laboratory of Software Development Environment, School of Mechanical Engineering & Automation, Beihang University, Beijing 100083, ChinaState Key Laboratory of Software Development Environment, School of Mechanical Engineering & Automation, Beihang University, Beijing 100083, ChinaState Key Laboratory of Software Development Environment, School of Mechanical Engineering & Automation, Beihang University, Beijing 100083, ChinaState Key Laboratory of Software Development Environment, School of Mechanical Engineering & Automation, Beihang University, Beijing 100083, ChinaState Key Laboratory of Software Development Environment, School of Mechanical Engineering & Automation, Beihang University, Beijing 100083, ChinaIn recent years, weigh-in-motion systems based on embedded sensor networks have received a lot of attention. However, how to improve the accuracy of multi-sensor weigh-in-motion (WIM) systems while keeping costs low remains a challenge. In this paper, a numerical simulation method is presented to analyze the relationship between sensor location and the accuracy of static weight estimation. The finite element model of a WIM system is developed, which consists of three parts: a pavement model, a moving load model and two types of sensor models. Analysis of simulation results shows that the ability of sensing dynamic load is closely related to the installation depth of sensors and pavement material. Moreover, the distance between the moving wheel and sensors has a great impact on estimating performance. Gaussian curve fitting could be used to reduce weighing error within a limited range. Our work suggests that much more attention should be paid to the design of the sensor layout of a WIM system.http://www.mdpi.com/1424-8220/18/9/3044weigh-in-motionembedded sensor networksensor layout |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Tianhao Qin Mengxiang Lin Ming Cao Kaiya Fu Rong Ding |
spellingShingle |
Tianhao Qin Mengxiang Lin Ming Cao Kaiya Fu Rong Ding Effects of Sensor Location on Dynamic Load Estimation in Weigh-in-Motion System Sensors weigh-in-motion embedded sensor network sensor layout |
author_facet |
Tianhao Qin Mengxiang Lin Ming Cao Kaiya Fu Rong Ding |
author_sort |
Tianhao Qin |
title |
Effects of Sensor Location on Dynamic Load Estimation in Weigh-in-Motion System |
title_short |
Effects of Sensor Location on Dynamic Load Estimation in Weigh-in-Motion System |
title_full |
Effects of Sensor Location on Dynamic Load Estimation in Weigh-in-Motion System |
title_fullStr |
Effects of Sensor Location on Dynamic Load Estimation in Weigh-in-Motion System |
title_full_unstemmed |
Effects of Sensor Location on Dynamic Load Estimation in Weigh-in-Motion System |
title_sort |
effects of sensor location on dynamic load estimation in weigh-in-motion system |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2018-09-01 |
description |
In recent years, weigh-in-motion systems based on embedded sensor networks have received a lot of attention. However, how to improve the accuracy of multi-sensor weigh-in-motion (WIM) systems while keeping costs low remains a challenge. In this paper, a numerical simulation method is presented to analyze the relationship between sensor location and the accuracy of static weight estimation. The finite element model of a WIM system is developed, which consists of three parts: a pavement model, a moving load model and two types of sensor models. Analysis of simulation results shows that the ability of sensing dynamic load is closely related to the installation depth of sensors and pavement material. Moreover, the distance between the moving wheel and sensors has a great impact on estimating performance. Gaussian curve fitting could be used to reduce weighing error within a limited range. Our work suggests that much more attention should be paid to the design of the sensor layout of a WIM system. |
topic |
weigh-in-motion embedded sensor network sensor layout |
url |
http://www.mdpi.com/1424-8220/18/9/3044 |
work_keys_str_mv |
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1726000656973561856 |