Technical guidelines for the assessment of earthquake induced liquefaction hazard at urban scale

Lai, Carlo G.; Bozzoni, Francesca; Conca, Daniele; Famà, A.; Özcebe, Ali Güney; Zuccolo, Elisa; Meisina, Claudia; Bonì, Roberta; Bordoni, Massimiliano; Cosentini, Renato Maria; Martelli, Luca; Poggi, Valerio; Fonseca, António Viana da; Ferreira, Cristiana; Rı́os, Sara; Cordeiro, Diana; Ramos, Catarina; Molina-Gómez, Fausto; Coelho, Carina Marques; Logar, Janko; Maček, Matej; Oblak, Aleš; Özçep, Ferhat; Bozbey, İlknur; Öztoprak, Sadık; Sargin, S.; Aysal, Namık; Öser, Cihan; Keleşoğlu, M. Kubilay

doi:10.1007/s10518-020-00951-8

Cited by 20 publications

(8 citation statements)

References 43 publications

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“…Furthermore, the geotechnical data and the geophysical tests of Vs 30 , Nakamura and down-hole tests, allow to build a geological section of the subsoil of the city of Jama, being the interface depth between quaternary soils and tertiary rocks (as to the volcanic basement) at 90 m depth. The response of the terrain has been modeled using the linear equivalent model (analysis of effective stresses), while the response of the soil columns was subsequently calculated for each layer [44,45]. Furthermore, we have used information of stratigraphic units and shear-wave velocity as these data have been required in order to model the spectra for the city of Jama, including soil profiles and the ground motion data in accordance with the national regulations entitled NEC-11 (Norma Ecuatoriana de la Construcción, [46]).…”

Section: Introductionmentioning

confidence: 99%

Seismically Induced Soil Liquefaction and Geological Conditions in the City of Jama due to the M7.8 Pedernales Earthquake in 2016, NW Ecuador

et al. 2020

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Seismically induced soil liquefaction has been documented after the M7.8, 2016 Pedernales earthquake. In the city of Jama, the acceleration recorded by soil amplification yielded 1.05 g with an intensity of VIII to IXESI-07. The current study combines geological, geophysical, and geotechnical data in order to establish a geological characterization of the subsoil of the city of Jama in the Manabi province of Ecuador. Then, the liquefaction potential index (LPI) has been evaluated considering the PGA-rock values calculated from deterministic methods applied to nearby geological faults, as well as the soil acceleration records for the city of Jama since the Pedernales megathrust earthquake. The importance of conducting geotechnical evaluations of particular colluvial, alluvial, and floodplain deposits, for which the liquefaction probability profiles have been additionally obtained, may serve as a useful tool for edifices foundations or earthquake resistant designs. Finally, the site response analysis is presented using a linear equivalent analysis, where previously seismic records compatible with the target spectrum have been selected. Hereby, the results of ground surface effects have been compared with the spectra of the Ecuadorian Regulation of Construction (NEC) in the context of local seismic amplification.

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Section: Introductionmentioning

confidence: 99%

Seismically Induced Soil Liquefaction and Geological Conditions in the City of Jama due to the M7.8 Pedernales Earthquake in 2016, NW Ecuador

et al. 2020

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“…6). The CPTu test was carried out within the LIQUEFACT project (Lai et al 2021) and the data of the SPT and laboratory tests and the velocity profile in the first 30 m (V s;30 ) were provided by the Sakarya Municipality. Fig.…”

Section: Soil Conditions and Ground Motionmentioning

confidence: 99%

Macromechanism Approach for Vulnerability Assessment of Buildings on Shallow Foundations in Liquefied Soils

Fonseca

Millen

Quintero

et al. 2023

J. Geotech. Geoenviron. Eng.

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The damage caused by seismic shaking and liquefaction-induced permanent ground deformation has conventionally been assessed as two separate problems often by different engineers. However, the two problems are inherently linked, since ground shaking causes liquefaction, and liquefaction-induced soil softening affects ground shaking. Modelling both problems within a single numerical model is complex for both the engineer and the software, and most finite element software only have the capabilities to address one of them. To improve the estimates of the seismic performance of buildings on liquefiable soil, a new sub-structuring approach is proposed called the macro-mechanism approach. This approach allows the soil-liquefaction-foundation-structure interaction to be considered in a series of sub-models accounting for the major nonlinear mechanisms of the system at a macro level. The proposed approach was implemented in the open-source finite element software OpenSees and then applied to a case study of a building where significant liquefaction-and shaking-induced damage was observed after the 1999 Mw 7.4 Kocaeli Earthquake. The case study building was also simulated using two different commercial software programs, the finite difference software FLAC, and the finite element software PLAXIS, by two different research teams. A comparison between the results from the macromechanism approach compared to full numerical models shows that the macro-mechanism approach can capture the extent of the foundation deformation and provide more realistic estimates of the building damage than full approaches since the FLAC and PLAXIS models consider elastic elements for the building.

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“…Interest has thus grown in geospatial models, which are well suited for regional-scale applications, and which have recently been adopted by agencies in the European Union and United States (e.g. Allstadt et al, 2022; Lai et al, 2019). The US Geological Survey, for example, utilizes the seminal model of Zhu et al (2017) in scenario and post-event data products.…”

Section: Applications and Models That Should Not Be Ignoredmentioning

confidence: 99%

Why “AI” models for predicting soil liquefaction have been ignored, plus some that shouldn’t be

Maurer

Sanger

2023

Earthquake Spectra

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Soil liquefaction remains an important and interesting problem that has attracted the development of enumerable prediction models. Increasingly, these models are utilizing algorithmic learning or “artificial intelligence” (AI). The rapid growth of AI in the liquefaction literature is unsurprising, given its ease of implementation and potential advantages over traditional statistical methods. However, AI liquefaction models have been widely ignored by practitioners and researchers alike; the objective of this article is to investigate “why?” Through a sample review of 75 publications, we identify several good reasons. Namely, these models frequently (1) are not compared to state-of-practice models, making it unclear why they should be adopted; (2) depart from best practices in model development; (3) use AI in ways that may not be useful; (4) are presented in ways that overstate their complexity and make them unapproachable; and (5) are discussed but not actually provided, meaning that no one can use the models even if they wanted to. These prevailing problems must be understood, identified, and remedied, but this does not mean that AI itself is problematic or that all prior efforts have been without merit or utility. Instead, understanding these recurrent shortcomings can help improve the direction and perceptions of this growing body of work. Toward this end, we highlight papers that are generally free from these shortcomings, and which demonstrate applications where AI is more likely to provide value in the near-term: permitting new modeling approaches and potentially improving predictions of liquefaction phenomena.

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Technical guidelines for the assessment of earthquake induced liquefaction hazard at urban scale

Cited by 20 publications

References 43 publications

Seismically Induced Soil Liquefaction and Geological Conditions in the City of Jama due to the M7.8 Pedernales Earthquake in 2016, NW Ecuador

Seismically Induced Soil Liquefaction and Geological Conditions in the City of Jama due to the M7.8 Pedernales Earthquake in 2016, NW Ecuador

Macromechanism Approach for Vulnerability Assessment of Buildings on Shallow Foundations in Liquefied Soils

Why “AI” models for predicting soil liquefaction have been ignored, plus some that shouldn’t be

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