Slip-Correlated High-Frequency Scaling Factor for Stochastic Finite-Fault Modeling of Ground Motion

Dang, Pengfei; Cui, Jie; Liu, Qifang; Xia, Songlin

doi:10.1785/0120210293

Cited by 10 publications

(5 citation statements)

References 16 publications

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“…For example, the technique adopted here uses the kinematic source model proposed in Gallovič, 35 which allows for a distribution of the asperities on the fault plane consistent with the properties of the crust, and a modeling of the rupture process superior to the radial rupture models typically coupled to the original representation of the source in earlier application of the SGMSM, for example, 17,18 because it considers a depth-dependent rupture propagation velocity (thus the importance of Z hyp ). Furthermore, the representation of the source is also updated to account for the evolution of the corner frequency during the rupture phenomenon, as per the proposal of Dang et al 36 Finally, the SGMSM incorporates a modified version of the post processing procedure recommended by Wang et al, 37 to include the inter-frequency correlation structure of spectral accelerations empirically derived from recorded ground motions.…”

Section: Ground Motion Simulation Methodologymentioning

confidence: 99%

Hazard‐consistent simulated earthquake ground motions for PBEE applications on stiff soil and rock sites

Alvarez‐Sanchez,

Iñárritu,

Šipčić

et al. 2023

Earthq Engng Struct Dyn

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Ground motion record selection is a standard step in state‐of‐the‐art performance‐based earthquake engineering (PBEE) applications. It links the structural response to seismic hazard of the site of interest. In this process, suites of hazard‐consistent ground motion recordings of a wide range of intensity levels are selected (and often scaled) from a database of ground motions to be used as input to nonlinear dynamic response analysis. The ideal practice would be to select ground motions from a database of recorded accelerograms in such a way that they are consistent with the seismic hazard at as many intensity measure (IM) levels as possible and for all important causative scenarios. However, the available databases are not heavily populated, especially for the large‐magnitude short‐distance scenarios. Therefore, synthetic ground motions are the natural candidates to enlarge the database of recordings for scenarios that are important to the site hazard but lack, completely or partially, recorded accelerograms. The usage of such recordings, however, can be recommended only after they are proven to be “realistic” through a battery of seismological and statistical tests. In this study, we propose and implement a multi‐fold testing framework for such an evaluation. We first generate a ground motion database of simulated ground motions (SDB) whose values of the parameters of the causative earthquakes are, to the extent possible, the same of those that caused the ground motions in a mirror database of recorded ground motions (RDB). We then utilize the conditional spectrum (CS) approach computed for a hypothetical rock site in Perugia, Italy, to select hazard consistent records separately from the RDB and SDB databases. A comprehensive set of comparisons of distributions of several IMs, computed from the selected sets, and of distributions of engineering demand parameters (EDPs), that such record sets induce on simple SDoF structures suggests the adequacy of using the proposed simulated ground motions for structural response analyses of structures on stiff and rock sites.

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Section: Ground Motion Simulation Methodologymentioning

confidence: 99%

Hazard‐consistent simulated earthquake ground motions for PBEE applications on stiff soil and rock sites

Alvarez‐Sanchez,

Iñárritu,

Šipčić

et al. 2023

Earthq Engng Struct Dyn

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“…Analyzing buildings from different perspectives is of great importance for safe design [1][2][3]. Concrete is among the most widely used materials for construction purposes [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Estimating Compressive Strength of Concrete Using Neural Electromagnetic Field Optimization

Akbarzadeh,

Ghafourian,

Anvari

et al. 2023

Materials

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Concrete compressive strength (CCS) is among the most important mechanical characteristics of this widely used material. This study develops a novel integrative method for efficient prediction of CCS. The suggested method is an artificial neural network (ANN) favorably tuned by electromagnetic field optimization (EFO). The EFO simulates a physics-based strategy, which in this work is employed to find the best contribution of the concrete parameters (i.e., cement (C), blast furnace slag (SBF), fly ash (FA1), water (W), superplasticizer (SP), coarse aggregate (AC), fine aggregate (FA2), and the age of testing (AT)) to the CCS. The same effort is carried out by three benchmark optimizers, namely the water cycle algorithm (WCA), sine cosine algorithm (SCA), and cuttlefish optimization algorithm (CFOA) to be compared with the EFO. The results show that hybridizing the ANN using the mentioned algorithms led to reliable approaches for predicting the CCS. However, comparative analysis indicates that there are appreciable distinctions between the prediction capacity of the ANNs created by the EFO and WCA vs. the SCA and CFOA. For example, the mean absolute error calculated for the testing phase of the ANN-WCA, ANN-SCA, ANN-CFOA, and ANN-EFO was 5.8363, 7.8248, 7.6538, and 5.6236, respectively. Moreover, the EFO was considerably faster than the other strategies. In short, the ANN-EFO is a highly efficient hybrid model, and can be recommended for the early prediction of the CCS. A user-friendly explainable and explicit predictive formula is also derived for the convenient estimation of the CCS.

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“…There are strong correlations between earthquakes, climate changes, and mining activities [6][7][8]. Many scientists have predicted that the frequency of earthquakes will keep increasing [9].…”

Section: Introductionmentioning

confidence: 99%

Early Detection of Earthquakes Using IoT and Cloud Infrastructure: A Survey

Abdalzaher,

Krichen,

Yiltas-Kaplan

et al. 2023

Sustainability

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Earthquake early warning systems (EEWS) are crucial for saving lives in earthquake-prone areas. In this study, we explore the potential of IoT and cloud infrastructure in realizing a sustainable EEWS that is capable of providing early warning to people and coordinating disaster response efforts. To achieve this goal, we provide an overview of the fundamental concepts of seismic waves and associated signal processing. We then present a detailed discussion of the IoT-enabled EEWS, including the use of IoT networks to track the actions taken by various EEWS organizations and the cloud infrastructure to gather data, analyze it, and send alarms when necessary. Furthermore, we present a taxonomy of emerging EEWS approaches using IoT and cloud facilities, which includes the integration of advanced technologies such as machine learning (ML) algorithms, distributed computing, and edge computing. We also elaborate on a generic EEWS architecture that is sustainable and efficient and highlight the importance of considering sustainability in the design of such systems. Additionally, we discuss the role of drones in disaster management and their potential to enhance the effectiveness of EEWS. Furthermore, we provide a summary of the primary verification and validation methods required for the systems under consideration. In addition to the contributions mentioned above, this study also highlights the implications of using IoT and cloud infrastructure in early earthquake detection and disaster management. Our research design involved a comprehensive survey of the existing literature on early earthquake warning systems and the use of IoT and cloud infrastructure. We also conducted a thorough analysis of the taxonomy of emerging EEWS approaches using IoT and cloud facilities and the verification and validation methods required for such systems. Our findings suggest that the use of IoT and cloud infrastructure in early earthquake detection can significantly improve the speed and effectiveness of disaster response efforts, thereby saving lives and reducing the economic impact of earthquakes. Finally, we identify research gaps in this domain and suggest future directions toward achieving a sustainable EEWS. Overall, this study provides valuable insights into the use of IoT and cloud infrastructure in earthquake disaster early detection and emphasizes the importance of sustainability in designing such systems.

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Slip-Correlated High-Frequency Scaling Factor for Stochastic Finite-Fault Modeling of Ground Motion

Cited by 10 publications

References 16 publications

Hazard‐consistent simulated earthquake ground motions for PBEE applications on stiff soil and rock sites

Hazard‐consistent simulated earthquake ground motions for PBEE applications on stiff soil and rock sites

Estimating Compressive Strength of Concrete Using Neural Electromagnetic Field Optimization

Early Detection of Earthquakes Using IoT and Cloud Infrastructure: A Survey

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