Influence of geogrid reinforcement on dynamic characteristics and response analysis of Panki pond ash

Swaraj Chowdhury () and Nihar Ranjan Patra ()
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Swaraj Chowdhury: Indian Institute of Technology Kanpur
Nihar Ranjan Patra: Indian Institute of Technology Kanpur

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2023, vol. 119, issue 1, No 17, 435-461

Abstract: Abstract In the present study, pond ash from Panki thermal power plant, India (seismic zone III), has been reinforced with geogrid layers and the influence of reinforcement on dynamic shear modulus, material damping ratio, degradation index and resistance to liquefaction of pond ash samples has been investigated. The static and dynamic properties of ash samples without and with geogrid reinforcement have been determined by laboratory experiments. Further, these properties have been used in the dynamic response analysis of the two-dimensional domain of the Panki pond ash deposit that is pond ash column reinforced without and with geogrid. The OpenSees (Open System for earthquake engineering simulation) software is used to perform the analysis. Three moderate magnitude earthquakes (Chamba, Chamoli and Uttarkashi) of Himalayan origin have been considered to study the variations of acceleration, displacement and excess pore water pressure ratio with time for different layers of pond ash columns without and with geogrid reinforcement. Cyclic triaxial experiments show that due to the provision of geogrid reinforcement, the dynamic shear modulus increases about 13% to 81.6% and the liquefaction resistance increases about 91–162%. The dynamic response analysis shows that for geogrid-reinforced pond ash column, the peak ground acceleration (PGA) value decreases about 32–33%, 17–22% and 13.5–18% and the peak ground displacement (PGD) value decreases about 23.5–39%, 18.5–20% and 13–17% as compared to unreinforced pond ash column for Chamba, Chamoli and Uttarkashi earthquakes, respectively.

Keywords: Geogrid reinforcement; Cyclic triaxial experiments; Dynamic response analysis; PGA; PGD (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11069-023-06136-1

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