Site amplification in the Kathmandu Valley during the 2015 M7.6 Gorkha, Nepal earthquake

Tallett-Williams, S; Gosh, B.; Wilkinson, Sean; Fenton, Clark; Burton, Paul W.; Whitworth, Michael R. Z.; Datla, S; Franco, Guillermo; Trieu, A; DeJong, Matthew J.; Novellis,; White, T.; Lloyd, T

doi:10.1007/s10518-016-0003-8

Cited by 27 publications

(12 citation statements)

References 12 publications

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“…Similarly, Gautam and Chamlagain (2016) provided distributions of spectral acceleration and calculation of the predominant period, assigning a V S of 700 m/s at the base of 49 borehole logs to 30 m. Alternative methods for the derivation of V S have been employed, with numerous studies providing micro-zonation of the Kathmandu Valley through microtremor and horizontal-to-vertical spectral ratio (HVSR) methods (e.g. Molnar et al, 2017;Paudyal et al, 2012Paudyal et al, , 2013Pokhrel et al, 2019;Poovarodom et al, 2017;Tallett-Williams et al, 2016). Pagliaroli et al (2018) used a mix of HVSR curves and 2D circular array microtremor measurements to estimate the depths to impedance contrast and attain velocity profiles for 1D and 2D site response analysis.…”

Section: Introductionmentioning

confidence: 99%

The SAFER geodatabase for the Kathmandu Valley: Geotechnical and geological variability

et al. 2020

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The Kathmandu Valley is within a seismically active region with only few recorded strong-motion data. Geophysical information in the Valley is also sparse. In addition, the absence of an open database which compiles in situ geophysical tests, borehole records, and geotechnical laboratory data is affecting the advancement of knowledge in the region. This article presents SAFER/GEO-591 database, named after the Engineering and Physical Science Research Council (EPSRC)-funded project Seismic Safety and Resilience of Schools in Nepal (SAFER). SAFER/GEO-591 contains data from groundwater wells and boreholes originally commissioned for research and commercial purposes. This work describes (1) the quality assessment and harmonization process conducted on the dataset, (2) the variation of shear-wave velocity ( VS) measurements and geotechnical parameters with depth and elevation in the Valley, (3) the current understanding of the Valley sediment/bedrock topography, and finally (4) new geological cross sections. A companion article presents an updated VS30 map across the Valley based on the contributions of this article. The database can be downloaded from the University of Bristol repository via DOI: https://doi.org/10.5523/bris.3gjcvx51lnpuv269xsa1yrb0rw

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Section: Introductionmentioning

confidence: 99%

The SAFER geodatabase for the Kathmandu Valley: Geotechnical and geological variability

et al. 2020

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“…This study and several others (Poovarodom et al 2017, Tallett-Williams et al 2016) performed microtremor field campaigns in a post-earthquake reconnaissance role. Microtremors were collected using ultraportable three-component Tromino ® sensors borrowed from the University of British Columbia Earthquake Engineering Research Facility.…”

Section: Site Amplification Compared To Geology and Observed Earthquake Damagementioning

confidence: 99%

Rapid Post-Earthquake Microtremor Measurements for Site Amplification and Shear Wave Velocity Profiling in Kathmandu, Nepal

2017

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This paper presents application of microtremor (ambient vibration) and surface wave field techniques for post-earthquake geotechnical reconnaissance purposes in Kathmandu, Nepal. Horizontal-to-vertical spectral ratios (HVSR) are computed from microtremor recordings at 16 individual measurement locations to obtain an estimate of fundamental frequency (site period) of the subsurface soils. A combination of active- and passive-source surface wave array testing was accomplished at five key sites including Kathmandu's Durbar Square and Airport. Joint inversion of each site's higher frequency dispersion and lower frequency HVSR data sets provides an estimate of subsurface material stiffness [i.e., shear wave velocity ( V S) depth profiles]. Direct comparison of our V S profiling at Kathmandu Durbar Square and that accomplished by downhole V S and/or standard penetration testing (SPT) profiling yield similar results. Classification of the five sites based on average V S, site period, and/or basin depth is presented. There is little differentiation in these site classification designations amongst the five sites, which does not capture significant differences in observed earthquake damage.

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“…Surveys conducted by the researchers from Purdue University and Nagoya Institute of Technology in association with the ACI resulted in collecting and compiling data of damage of 135 low-rise reinforced concrete buildings with or without masonry infill walls [43]. The Kathmandu valley has a soil profile consisting primarily a heterogeneous mixture of clays, sands, and silts with thicknesses ranging up to nearly 400 m [50]. The U.S. Geological Survey ShakeMap recorded a ground motion intensity of VIII, with the epicenter being 19 Km to the South-East of Kodari.…”

Section: Background Of the Selected Earthquakesmentioning

confidence: 99%

A Synthesized Study Based on Machine Learning Approaches for Rapid Classifying Earthquake Damage Grades to RC Buildings

et al. 2021

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A vast number of existing buildings were constructed before the development and enforcement of seismic design codes, which run into the risk of being severely damaged under the action of seismic excitations. This poses not only a threat to the life of people but also affects the socio-economic stability in the affected area. Therefore, it is necessary to assess such buildings’ present vulnerability to make an educated decision regarding risk mitigation by seismic strengthening techniques such as retrofitting. However, it is economically and timely manner not feasible to inspect, repair, and augment every old building on an urban scale. As a result, a reliable rapid screening methods, namely Rapid Visual Screening (RVS), have garnered increasing interest among researchers and decision-makers alike. In this study, the effectiveness of five different Machine Learning (ML) techniques in vulnerability prediction applications have been investigated. The damage data of four different earthquakes from Ecuador, Haiti, Nepal, and South Korea, have been utilized to train and test the developed models. Eight performance modifiers have been implemented as variables with a supervised ML. The investigations on this paper illustrate that the assessed vulnerability classes by ML techniques were very close to the actual damage levels observed in the buildings.

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Site amplification in the Kathmandu Valley during the 2015 M7.6 Gorkha, Nepal earthquake

Cited by 27 publications

References 12 publications

The SAFER geodatabase for the Kathmandu Valley: Geotechnical and geological variability

The SAFER geodatabase for the Kathmandu Valley: Geotechnical and geological variability

Rapid Post-Earthquake Microtremor Measurements for Site Amplification and Shear Wave Velocity Profiling in Kathmandu, Nepal

A Synthesized Study Based on Machine Learning Approaches for Rapid Classifying Earthquake Damage Grades to RC Buildings

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