Seismic risk at urban scale: the role of site response analysis

Risi, Raffaele De; Penna, Augusto; Simonelli, Armando Lucio

doi:10.1016/j.soildyn.2019.04.011

Cited by 22 publications

(12 citation statements)

References 101 publications

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“…The results show well agreements between the calculated and theoretical values with the maximum error less than 1 Hz, which means the adjustment to fit SA rock and has limited disturbance on . The distribution of PGA amplification factors [15] as a function of with different shaking intensities and site conditions are plotted in Figure 12A. Regardless of the PGA rock level and site condition, similar second-order polynomial relationships between the mean PGA surf and mean can be fitted (Figure 12A).…”

Section: Frequency Contentsmentioning

confidence: 83%

“…This can be achieved by directly simulating ground motions using hazard spectrum with adjusted amplitude. However, De Risi et al [15] showed that a simplified method for site effects could be unreliable. Besides, for some region like China, the local ground-motion prediction equation (GMPE) only provide ground-motion intensities on bedrock [16].…”

Section: Introductionmentioning

confidence: 99%

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A new energy‐compatible nonstationary stochastic ground‐motion simulation method

Wang

Risi

et al. 2021

Earthq Engng Struct Dyn

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A new time-frequency modulating function with a novel equivalent averageintensity envelope based on the instantaneous energy is proposed to simulate fully nonstationary ground motions. The new model utilizes simple, uncoupled, and statistically well-studied temporal and spectral nonstationary parameters. Then, a period-by-period simulation method based on the proposed stochastic model is developed to match multiple targets. A numerical comparison shows that the new method replicates well both the target temporal and spectral nonstationarities and it is efficient and easy to use in engineering practice. This study also demonstrates that for obtaining site-specific seismic input by means of site response analysis: (1) the frequency contents of bedrock motion, including the energy concentration in terms of frequency and frequency decaying with respect to time, have a significant impact on the site response with moderate and large PGA levels; (2) using the proposed instantaneous-frequency-related time modulating function to approximate the real ground motion leads to results sufficiently close to the record for nonlinear site response regardless PGA levels and site condition. Therefore, attention should be paid to the change of nonstationary characteristics of ground-motion frequency according to the source, path, and site condition when simulating ground motion on bedrock. As the proposed methods can approximately control various ground-motion characters, different targets of ground-motion simulation can be matched for different engineering purpose such as site response analysis, time-history analysis of special structures, etc.

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Section: Frequency Contentsmentioning

confidence: 83%

Section: Introductionmentioning

confidence: 99%

A new energy‐compatible nonstationary stochastic ground‐motion simulation method

Wang

Risi

et al. 2021

Earthq Engng Struct Dyn

Self Cite

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“…Then, an equivalent linear analysis was performed using the code DEEPSOIL 82 to deconvolve the selected records through the soil to establish the 'bedrock motion' 78 . This was done considering an initial damping ratio of 2% in softer layers and 0.5% in harder layers 83 and using the Seed and Idriss 1991 shear modulus reduction and material damping curves for sand as per DEEPSOIL option 84 . Figure 6A depicts the acceleration response spectra of the deconvolved input motions used in the experimental tests, in the unscaled (solid lines) and scaled (dashed line) case, respectively.…”

Section: Physical Model Developmentmentioning

confidence: 99%

Integral abutment bridges: Investigation of seismic soil‐structure interaction effects by shaking table testing

Fiorentino

Cengiz

Luca

et al. 2020

Earthq Engng Struct Dyn

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In recent years there has been renewed interest on integral abutment bridges (IABs), mainly due to their low construction and maintenance cost. Owing to the monolithic connection between deck and abutments, there is strong soil‐structure interaction between the bridge and the backfill under both thermal action and earthquake shaking. Although some of the regions where IABs are adopted qualify as highly seismic, there is limited knowledge as to their dynamic behaviour and vulnerability under strong ground shaking. To develop a better understanding on the seismic behaviour of IABs, an extensive experimental campaign involving over 75 shaking table tests and 4800 time histories of recorded data, was carried out at EQUALS Laboratory, University of Bristol, under the auspices of EU‐sponsored SERA project (Seismology and Earthquake Engineering Research Infrastructure Alliance for Europe). The tests were conducted on a 5 m long shear stack mounted on a 3 m × 3 m 6‐DOF earthquake simulator, focusing on interaction effects between a scaled bridge model, abutments, foundation piles and backfill soil. The study aims at (a) developing new scaling procedures for physical modelling of IABs, (b) investigating experimentally the potential benefits of adding compressible inclusions (CIs) between the abutment and the backfill and (c) exploring the influence of different types of connection between the abutment and the pile foundation. Results indicate that the CI reduces the accelerations on the bridge deck and the settlements in the backfill, while disconnecting piles from the cap decreases bending near the pile head.

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“…For the assumed earthquake events, the BSSA14 model with the median value shows the PGA in the range of 0.1-0.4 g considering the site amplification effects with average shear wave velocity from surface to 30-m depth (V s30 ) of 330 m/s. If the larger area than Chiang Mai Municipality is a case study, the variation of the site amplification effects should be considered (Bonilla et al, 1997;De Risi et al, 2019).…”

Section: Seismic Building Damage Calculation Using Capacity Spectrum mentioning

confidence: 99%

Seismic Building Damage Prediction From GIS-Based Building Data Using Artificial Intelligence System

Hansapinyo

Latcharote

Limkatanyu

2020

Front. Built Environ.

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The estimation of seismic damage to buildings is complicated due to the many sources of uncertainties. This study aims to develop a new approach using an artificial intelligence system called adaptive neuro-fuzzy inference system (ANFIS) model to predict the damage of buildings at urban scale considering input uncertainties. First, the study performed seismic damage evaluation of buildings utilizing the capacity spectrum method (CSM) to obtain a set of 57,648 training data from a combination of three main parameters, i.e., 6 earthquake magnitudes, 8 structural types, and 1,201 distances. Next, the data was used to develop a practical ANFIS model for the seismic damage prediction. The variables of the fuzzy system are earthquake magnitudes, structural types, and distance between epicenter and building. To validate the applicability of the proposed model, analyses of spatial seismic building damage under five possible earthquakes in Chiang Mai Municipality were performed by using the proposed methodology. From the comparison of the damaged urban area, small discrepancies between the CSM and the ANFIS results could be observed. It should be noted that the proposed ANFIS model can predict the seismic building damage reasonably well compared with the CSM. Using the method proposed herein, it is possible to create damage scenarios for earthquake-prone areas where only a few seismic data are available, such as developing countries.

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Seismic risk at urban scale: the role of site response analysis

Cited by 22 publications

References 101 publications

A new energy‐compatible nonstationary stochastic ground‐motion simulation method

A new energy‐compatible nonstationary stochastic ground‐motion simulation method

Integral abutment bridges: Investigation of seismic soil‐structure interaction effects by shaking table testing

Seismic Building Damage Prediction From GIS-Based Building Data Using Artificial Intelligence System

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