R
Reza Mohammadi
Researcher at State University of New York System
Publications - 12
Citations - 348
Reza Mohammadi is an academic researcher from State University of New York System. The author has contributed to research in topics: Computer science & Track geometry. The author has an hindex of 6, co-authored 8 publications receiving 217 citations. Previous affiliations of Reza Mohammadi include University at Buffalo & Amirkabir University of Technology.
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Prepositioning emergency earthquake response supplies: A new multi-objective particle swarm optimization algorithm
TL;DR: A multi-objective stochastic programming model for developing an earthquake response plan, which integrates pre-and post-disaster decisions, is proposed and a new multi-Objective particle swarm optimization (MOPSO) algorithm is developed to solve this model.
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Data-driven optimization of railway maintenance for track geometry
TL;DR: A data-driven condition-based policy for the inspection and maintenance of track geometry is developed and results in an approximately 10% savings in the total maintenance costs for every 1 mile of track.
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A new hybrid evolutionary based RBF networks method for forecasting time series
TL;DR: The results show that the proposed evolving RBF based method can be applied to forecast the emergency supply demand time series successfully with the automatically selected nodes and inputs and is able to predict time series more accurately than others.
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Exploring the impact of foot-by-foot track geometry on the occurrence of rail defects
TL;DR: This study develops a Recursive Feature Elimination (RFE) algorithm for feature selection and compares its results with Singular Value Decomposition (SVD) and employs an extreme gradient boosting (XGBoost) algorithm in which the hyper-parameters are optimized using a Bayesian optimization method.
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Predicting rail defect frequency: An integrated approach using fatigue modeling and data analytics
TL;DR: In maintenance planning of rail track, it is imperative to assess the potential and frequency of rail defects as mentioned in this paper, and this problem has been mainly studied in the literature by either datalink or data mining.