Analysis of axle and vehicle load properties through Bayesian Networks based on Weigh-in-Motion data

Morales-Nápoles, Oswaldo; Steenbergen, R.D.J.M.

doi:10.1016/j.ress.2014.01.018

Cited by 38 publications

(19 citation statements)

References 13 publications

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“…WIM merupakan sebuah solusi inovatif dalam manajemen lalu lintas yang memungkinkan kendaraan ditimbang pada saat dalam perjalanan, membantu mengendalikan jumlah kendaraan yang mengalami kelebihan beban di jalan, meningkatkan keselamatan dijalan raya.WIM terdiri dari weight sensor system dan antar muka komputer untuk deteksi, pengambilan, perhitungan serta analisis data [10,11]. Terdapat berbagai macam sensor yang telah dikembangkan untuk menganalisis sebuah berat kendaraan agar dapat menghasilkan data lalu lintas, salah satunya adalah sensor Load Cell [12,14]. Gambar…”

Section: Weigh In Motionunclassified

Perancangan dan Implementasi Sistem Monitoring Beban dan Kecepatan Kendaraan Menggunakan Teknologi Weigh in Motion

Septiana

Zaini

2018

JNTE

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Weight in Motion (WIM) merupakan salah satu solusi inovatif dalam manajemen lalu lintas yang memungkinkan kendaraan ditimbang pada saat dalam perjalanan. Pada penelitian ini dirancang sebuah sistem monitoring yang mampu mengolah dan menghitung data kendaraan berupa beban dan kecepatan kendaraan melalui sistem WIM. Untuk mendukung sistem ini digunakan perangkat keras berupa sensor WIM yang terdiri dari Load Cell, modul penguat HX711 dan Arduino serta untuk data sinyal beban yang telah dihasilkan sistem WIM menggunakan metode analisa pengolahan sinyal. Pengujian sistem ini dilakukan menggunakan sebuah mobil penumpang dengan kecepatan yang berbedabeda. Dari hasil pengujian didapatkan sistem WIM mampu melakukan pengukuran kendaraan berjalan dengan nilai rata-rata error yang dihasilkan untuk kecepatan 8.94%, jarak sumbu kendaraan 14.64%, dan beban kendaraan 10.21%.

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Section: Weigh In Motionunclassified

Perancangan dan Implementasi Sistem Monitoring Beban dan Kecepatan Kendaraan Menggunakan Teknologi Weigh in Motion

Septiana

Zaini

2018

JNTE

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“…In [24] the same data coming from a WIM installation is input to model multidimensional distribution of axle loads together with other related quantities. A thorough investigation of dependencies between these quantities through a copula representation is presented.…”

Section: Traffic and Load Datamentioning

confidence: 99%

“…As only the mechanism of fatigue for orthotropic steel bridges is investigated, loading coming from fluctuating stresses caused by vehicles is in general the most important factor and is seen as a random variable whose distribution is yearly stationary. The nature of traffic intensity influencing the loading behaviour is also stochastic [24]. Both distributions of loading and traffic are computed given sample distributions bootstrapped from WIM data.…”

Section: Traffic and Load Datamentioning

confidence: 99%

A Two‐Dimension Dynamic Bayesian Network for Large‐Scale Degradation Modeling with an Application to a Bridges Network

Torre

Yeung

Morales-Nápoles

et al. 2017

Computer aided Civil Eng

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Modelling the stochastic evolution of a largescale fleet or network generally proves to be challenging due to the large number of variables and their interactions. This difficulty may be compounded through complex relationships between various assets in the network. Although a great number of probabilistic graph-based models (e.g., Bayesian networks) have been developed recently to describe the behaviour of single assets, one can find significantly fewer approaches addressing a fully integrated network. An extension to the standard dynamic Bayesian network is proposed by introducing an additional dimension for multiple elements. These elements are then linked through a set of covariates which translate the probabilistic dependencies. A Markov chain is utilized to model the elements and develop a distribution-free mathematical framework to parametrize the transition probabilities without previous data. This is achieved by borrowing from Cooke's method for structured expert judgement and also applied to the quantification of the covariate relationships. Some metrics are also presented for evaluating the sensitivity of information inserted into the covariate-DBN where the focus is given on two specific type of configurations. The model is applied to a real-world example of steel bridge network in the Netherlands which are related through traffic load patterns. Numerical examples highlight the inference mechanism and show the sensitivity of information inserted in various ways. It is shown that information is most valuable very early and decreases substantially over time. Resulting observations entail the reduction of inference combinations and by extension a computational gain to select the most sensitive pieces of information.

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“…Therefore, strains are measured on other locations and the measurements are compared with the results of a finite element model of the bridge deck. The axle loads applied onto the model are obtained from weigh in motion (WIM) measurements approximately 50 km away from the bridge (see Morales-Nápoles & Steenbergen, 2014). The stress ranges obtained from the measurements at the bridge were approximately 20% lower than the stress ranges obtained by the finite element model.…”

Section: Loadingmentioning

confidence: 99%

Maintenance decision model for steel bridges: a case in the Netherlands

Attema

Acharige

Morales-Nápoles

et al. 2016

Structure and Infrastructure Engineering

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A probabilistic model is developed to investigate the crack growth development in welded details of orthotropic bridge decks. Bridge decks may contain many of these vulnerable details and bridge reliability cannot always be guaranteed upon the attainment of a critical crack. Therefore, insight into the crack growth development is crucial in guaranteeing bridge reliability and scheduling efficient maintenance schemes. The probabilistic nature of the crack growth development model and the dependence of this model on many interdependent random variables result in significant uncertainties regarding model outcome. To reduce some of these uncertainties, the probabilistic model is combined with a monitoring system installed on a part of the bridge. In addition, a Bayesian network is used to determine the dependence structure between the different details (monitored and non-monitored) of the bridge. This dependence structure enables us to make more accurate crack growth predictions for all details of the bridge while monitoring only a limited number of those details and updating the remaining uncertainties.

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Analysis of axle and vehicle load properties through Bayesian Networks based on Weigh-in-Motion data

Cited by 38 publications

References 13 publications

Perancangan dan Implementasi Sistem Monitoring Beban dan Kecepatan Kendaraan Menggunakan Teknologi Weigh in Motion

Perancangan dan Implementasi Sistem Monitoring Beban dan Kecepatan Kendaraan Menggunakan Teknologi Weigh in Motion

A Two‐Dimension Dynamic Bayesian Network for Large‐Scale Degradation Modeling with an Application to a Bridges Network

Maintenance decision model for steel bridges: a case in the Netherlands

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