T
Trilok Singh
Researcher at Indian Institute of Technology Bombay
Publications - 396
Citations - 13468
Trilok Singh is an academic researcher from Indian Institute of Technology Bombay. The author has contributed to research in topics: Slope stability & Rock mass classification. The author has an hindex of 54, co-authored 373 publications receiving 10286 citations. Previous affiliations of Trilok Singh include Indian Institute of Technology Delhi & University of Cologne.
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Thickness dependence of optoelectronic properties in ALD grown ZnO thin films
TL;DR: In this article, a thin ZnO thin film with high conductivity and high transparency was grown on Si (1.0.0) substrates by atomic layer deposition.
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Tailoring surface states in WO3 photoanodes for efficient photoelectrochemical water splitting
TL;DR: In this paper, the surface properties of WO3 thin films were tailored by hydrogen plasma treatment and anchoring plasmonic nanoparticles (Au and Ag), which significantly altered the light harvesting and charge separation/transport processes of photoanodes.
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Study of Strain Rate and Thermal Damage of Dholpur Sandstone at Elevated Temperature
TL;DR: In this article, the effect of temperature on the physical and mechanical properties of Dholpur sandstone from upper Bhander subgroup has been studied and the results of this study will be helpful for restoration and redesign of fire-damaged sandstone buildings and monuments.
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The stability of road cut cliff face along SH-121: a case study
TL;DR: In this article, a combination of field study and 2D computer simulation was performed to assess surface characteristics of the cliff face and the result showed that the rock face is highly unstable taking into consideration the environmental condition and daily traffic.
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A comparative study of ANN and Neuro-fuzzy for the prediction of dynamic constant of rockmass
TL;DR: In this article, the authors have developed and compared two different models, Neuro-fuzzy systems (combination of fuzzy and artificial neural network systems) and Artificial Neural Network systems, for the prediction of compressional wave velocity.