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Sakdirat Kaewunruen

Researcher at University of Birmingham

Publications -  127
Citations -  1412

Sakdirat Kaewunruen is an academic researcher from University of Birmingham. The author has contributed to research in topics: Track (rail transport) & Prestressed concrete. The author has an hindex of 16, co-authored 127 publications receiving 864 citations. Previous affiliations of Sakdirat Kaewunruen include Riga Technical University & Massachusetts Institute of Technology.

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Digital twin aided sustainability-based lifecycle management for railway turnout systems

TL;DR: This research establishes and analyses the world's first 6D BIM for life cycle management of a railway turnout system, and reveals that embodied material emission is the main contributor towards carbon footprint, especially produced during the manufacturing stage.
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A Digital-Twin Evaluation of Net Zero Energy Building for Existing Buildings

TL;DR: In this paper, both technical and financial viability of Net Zero Energy Buildings (NZEB) for "existing" buildings is highlighted. And a future model for NZEB applications in existing buildings, which applies renewable technologies to the building by aiming to identify ultimate benefit of the building especially in terms of effectiveness and efficiency in energy consumption.
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Digital twin for sustainability evaluation of railway station buildings

TL;DR: In this paper, a specific BIM application within the context of railway station buildings using a Revit-based simulation of construction work for King's Cross station in London is discussed.
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Enhancement of Dynamic Damping in Eco-Friendly Railway Concrete Sleepers Using Waste-Tyre Crumb Rubber

TL;DR: It is recommended to consider the use of rubberised concrete (up to 10 wt. % crumb rubber) in designing railway sleepers as this will improve the service life of railway track systems and reduce wastes to the environment.
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State-of-the-Art Review of Railway Track Resilience Monitoring

TL;DR: The insight into track monitoring for railways serving traffic with extreme features will not only improve the track inspection and damage detection but also enable a predictive track maintenance regime in order to assist the decision-making process towards more cost-effective management in the railway industry.