Damage Reconnaissance of Unreinforced Masonry Bearing Wall Buildings after the 2015 Gorkha, Nepal, Earthquake

Brando, Giuseppe; Rapone, Davide; Spacone, Enrico; O'Banion, Matt; Olsen, Michael J.; Barbosa, André R.; Faggella, Marco; Gigliotti, Rosario; Liberatore, Domenico; Russo, Salvatore; Sorrentino, Luigi; Bose, Supratik; Stravidis, Andreas

doi:10.1193/010817eqs009m

Cited by 64 publications

(41 citation statements)

References 26 publications

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“…The knowledge of a building is essential to define the necessary action and intervention to improve the seismic capacity of the building [19][20][21] . For this reason, the diagnostic, especially non-destructive tests [22][23][24] , plays a fundamental role 25,26 .…”

Section: Introduction and Methodologymentioning

confidence: 99%

Damage assessment of Nepal heritage through ambient vibration analysis and visual inspection

Russo

Spoldi

2020

Struct Control Health Monit

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Summary The aim of this paper is to identify, both through microtremor analysis and visual inspection, the collapse mechanisms of the Nepalese wood‐masonry monuments damaged by the 2015 seismic event that struck Kathmandu and its valley. The research analyses two case studies as the “Radha Krishna” temple located in Teku, a district in Kathmandu, and the “Pancha Deval complex” in Pashupati area. More specifically, after a careful anamnesis based on visual inspection and hypotheses on the temple's structural behaviour, global nondestructive testing (microtremor) was carried out for qualitative characterization of the structural system. The visual damage survey allowed to identify the recurring collapse mechanisms in the two case studies with the identification of typical Nepali expected damage. The case of Radha Krishna temple denotes a Nepali collapse mechanism typical in the corner of temples made of timber masonry, in which the mechanical contribution of the timber is manifested through columns and windows. The ambient vibration analysis carried out by tromograph device and microtremor evaluation allowed to dynamically characterize the two bases by identifying the peak frequencies both for Radha Krishna and for Pancha Deval complex. With the same device, the two historic constructions have been also studied in evaluating local modes and frequency. In the Pancha Deval complex, a relationship between damage, frequencies, and the amplification of the base was observed. In detail, the five buildings have similar damage and similar first frequencies (2.72–2.9 Hz). The most damaged sides are those with the frequencies close to the base (2.05–2.38 Hz).

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Section: Introduction and Methodologymentioning

confidence: 99%

Damage assessment of Nepal heritage through ambient vibration analysis and visual inspection

Russo

Spoldi

2020

Struct Control Health Monit

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“…In this case, empirical methods can be applied for assessing the potential damage (expressed in terms of percentages of buildings that would experience certain limit states) under different earthquake intensities. The engineering judgements should be given for those structural characteristics that contribute positively or negatively to the buildings response, also in view of the observations carried out after earthquakes of the past on buildings that are similar to the ones of the studied stock (Brando et al, 2017b;Sorrentino et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Seismic Vulnerability of Buildings in Historic Centers: From the “Urban” to the “Aggregate” Scale

Cocco

D'Aloisio

Spacone

et al. 2019

Front. Built Environ.

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Seismic vulnerability assessment of urban centers is a challenging issue that needs to be faced accurately for the earthquake risk of large territorial areas. The selection of suitable methods is a crucial aspect that must be treated according to different evaluation processes, depending on the size of the problem and on the available calculation capacities. A possible strategy consists in analyzing large stocks of buildings, so to include in the analyses all those structural parameters that characterize their response and to involve the variability of the considered features. This would require a high computational effort that should be addressed to the investigation of the response of a large number of models. For this reason, simplified procedures based on engineeristic judgements, are commonly considered a viable way to be undertaken in order to predict damage scenarios. Alternatively, the attention could be focused on a limited number of buildings that are judged to be representative of the whole stock. In this case, more sophisticated analyses could be carried out and the obtained results could be extended to the whole urban center. Based on this premise, this paper presents the results obtained through the application of two different seismic vulnerability methodologies on the historic center of Campotosto, in Italy, which was hit by the last 2016 Central Italy earthquake. The first is an empirical method, applied considering a large stock of 130 buildings, which was calibrated by the authors after the 2009 L'Aquila earthquake for historical centers that are similar to the one studied in this paper. The latter, is a method based on analytical formulations dealt with by the Vulnus software, developed at the University of Padua in Italy, which was used for evaluating the seismic vulnerability of an aggregate building, which has been considered representative of the historic center. The final aim is to compare, also in the light of the damage provoked by the 2016 earthquake, the observed post-seismic scenarios, expressed in terms of fragility curves, derived from the two applied methodologies, in order to prove their reliability and to stress the possible issues related to their implementation at different scales.

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After a disaster, teams of structural engineers collect vast amounts of images from damaged buildings to obtain new knowledge and extract lessons from the event. As part of these procedures to enable the learning process, post-event reconnaissance teams are dispatched after disasters to investigate damaged buildings and to collect perishable data (Brando et al, 2017; Kijewski-Correa, Prevatt et al, Comput Aided Civ Inf. When damage is severe, it may be quite difficult to even recognize the building.

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confidence: 99%

“…There is strong agreement in the engineering community that engineers can and should learn much more from the consequences of each disaster than we do today (Kettl, 2006;Silva et al, 2019). As part of these procedures to enable the learning process, post-event reconnaissance teams are dispatched after disasters to investigate damaged buildings and to collect perishable data (Brando et al, 2017; Kijewski-Correa, Prevatt et al, Comput Aided Civ Inf. 2020;35:241-257. wileyonlinelibrary.com/journal/mice 241 How to cite this article: Lenjani A, Yeum CM, Dyke S, Bilionis I.…”

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confidence: 99%

Automated building image extraction from 360° panoramas for postdisaster evaluation

Lenjani

Yeum

Dyke

et al. 2019

Computer aided Civil Eng

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After a disaster, teams of structural engineers collect vast amounts of images from damaged buildings to obtain new knowledge and extract lessons from the event. However, in many cases, the images collected are captured without sufficient spatial context. When damage is severe, it may be quite difficult to even recognize the building. Accessing images of the predisaster condition of those buildings is required to accurately identify the cause of the failure or the actual loss in the building. Here, to address this issue, we develop a method to automatically extract pre‐event building images from 360° panorama images (panoramas). By providing a geotagged image collected near the target building as the input, panoramas close to the input image location are automatically downloaded through street view services (e.g., Google or Bing in the United States). By computing the geometric relationship between the panoramas and the target building, the most suitable projection direction for each panorama is identified to generate high‐quality 2D images of the building. Region‐based convolutional neural networks are exploited to recognize the building within those 2D images. Several panoramas are used so that the detected building images provide various viewpoints of the building. To demonstrate the capability of the technique, we consider residential buildings in Holiday Beach in Rockport, Texas, United States, that experienced significant devastation in Hurricane Harvey in 2017. Using geotagged images gathered during actual postdisaster building reconnaissance missions, we verify the method by successfully extracting residential building images from Google Street View images, which were captured before the event.

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Damage Reconnaissance of Unreinforced Masonry Bearing Wall Buildings after the 2015 Gorkha, Nepal, Earthquake

Cited by 64 publications

References 26 publications

Damage assessment of Nepal heritage through ambient vibration analysis and visual inspection

Damage assessment of Nepal heritage through ambient vibration analysis and visual inspection

Seismic Vulnerability of Buildings in Historic Centers: From the “Urban” to the “Aggregate” Scale

Automated building image extraction from 360° panoramas for postdisaster evaluation

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