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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Main Authors: Tianhao Qin, Mengxiang Lin, Ming Cao, Kaiya Fu, Rong Ding
Format: Article
Language:English
Published: MDPI AG 2018-09-01
Series:Sensors
Subjects:
Online Access:http://www.mdpi.com/1424-8220/18/9/3044
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spelling 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 AT tianhaoqin effectsofsensorlocationondynamicloadestimationinweighinmotionsystem
AT mengxianglin effectsofsensorlocationondynamicloadestimationinweighinmotionsystem
AT mingcao effectsofsensorlocationondynamicloadestimationinweighinmotionsystem
AT kaiyafu effectsofsensorlocationondynamicloadestimationinweighinmotionsystem
AT rongding effectsofsensorlocationondynamicloadestimationinweighinmotionsystem
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