Application of artificial neural network for prediction of track geometry degradation

Document identifier: oai:DiVA.org:ltu-76750
Keyword: Engineering and Technology, Civil Engineering, Other Civil Engineering, Teknik och teknologier, Samhällsbyggnadsteknik, Annan samhällsbyggnadsteknik, Artificial neural network, Prediction, Track geometry, Degradation, Drift och underhållsteknik, Operation and Maintenance
Publication year: 2019
Relevant Sustainable Development Goals (SDGs):
SDG 11 Sustainable cities and communitiesSDG 9 Industry, innovation and infrastructure
The SDG label(s) above have been assigned by OSDG.ai

Abstract:

Track geometry degradation is a function of several interacting factors (e.g. traffic, environmental conditions, track structure, and maintenance history) and each interaction may follow several interrelating rules. Artificial Neural Network (ANN) showed a high capability in prediction of track geometry degradation, by analysing a set of features that explain the behaviour of geometry evolution. The aim of this study is to develop an approach using ANN to predict the degradation of track geometry along a track line. In the ANN model, maintenance history, curvature, annual traffic, presence of level cross, speed, frequency of trains passing along the track and material type and age are inputs to the model and track geometry degradation rate is the output of the model. A set of data from railway network in Sweden are used to train and validate the model. The results indicate that ANN is able to predict the degradation of track geometry.

Authors

Mohammad Haddadzade

Luleå tekniska universitet; Drift, underhåll och akustik
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Hamid Khajehei

Luleå tekniska universitet; Drift, underhåll och akustik
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Alireza Ahmadi

Luleå tekniska universitet; Drift, underhåll och akustik
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Iman Soleimanmeigouni

Luleå tekniska universitet; Drift, underhåll och akustik
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A. Nissen

Trafikverket, Luleå, Sweden
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