Correlation between ground motion intensity measures and seismic damage of 3D R/C buildings

2015 ◽  
Vol 82 ◽  
pp. 151-167 ◽  
Author(s):  
Konstantinos Kostinakis ◽  
Asimina Athanatopoulou ◽  
Konstantinos Morfidis
Keyword(s):  
Ground Motion ◽  
Seismic Damage ◽  
Intensity Measures

Real-time seismic damage prediction and comparison of various ground motion intensity measures based on machine learning

2021 ◽  
Author(s):  
Yongjia Xu ◽  
Xinzheng Lu ◽  
Yuan Tian ◽  
Yuli Huang
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Case Studies ◽  
Ground Motion ◽  
Emergency Response ◽  
Prediction Method ◽  
Seismic Damage ◽  
Intensity Measures ◽  
Damage Prediction ◽  
Model Training

<p>After earthquakes, an accurate and efficient seismic damage prediction is indispensable for emergency response. Existing methods face the dilemma between accuracy and efficiency. A real-time and accurate seismic damage prediction method based on machine-learning is proposed here. 48 intensity measures are used as input to represent the ground motion comprehensively. Besides, the workload of the NLTHA method is replaced by model training/testing and moved to a non-urgent stage to promote efficiency. Case studies with various building cases prove the accuracy and efficiency of the proposed method. Key intensity measures for each building are identified by iteratively using the proposed framework.</p>

scholarly journals Encoding Time-Series Ground Motions as Images for Convolutional Neural Networks-Based Seismic Damage Evaluation

2021 ◽  
Vol 7 ◽  
Author(s):  
Xinzhe Yuan ◽  
Dustin Tanksley ◽  
Pu Jiao ◽  
Liujun Li ◽  
Genda Chen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Ground Motion ◽  
Convolutional Neural Networks ◽  
Ground Motions ◽  
Seismic Damage ◽  
Damage Evaluation ◽  
Image Size ◽  
Intensity Measures ◽  
Image Encoding

Traditional methods for seismic damage evaluation require manual extractions of intensity measures (IMs) to properly represent the record-to-record variation of ground motions. Contemporary methods such as convolutional neural networks (CNNs) for time series classification and seismic damage evaluation face a challenge in training due to a huge task of ground-motion image encoding. Presently, no consensus has been reached on the understanding of the most suitable encoding technique and image size (width × height × channel) for CNN-based seismic damage evaluation. In this study, we propose and develop a new image encoding technique based on time-series segmentation (TS) to transform acceleration (A), velocity (V), and displacement (D) ground motion records into a three-channel AVD image of the ground motion event with a pre-defined size of width × height. The proposed TS technique is compared with two time-series image encoding techniques, namely recurrence plot (RP) and wavelet transform (WT). The CNN trained through the TS technique is also compared with the IM-based machine learning approach. The CNN-based feature extraction has comparable classification performance to the IM-based approach. WT 1,000 × 100 results in the highest 79.5% accuracy in classification while TS 100 × 100 with a classification accuracy of 76.8% is most computationally efficient. Both the WT 1,000 × 100 and TS 100 × 100 three-channel AVD image encoding methods are promising for future studies of CNN-based seismic damage evaluation.

Comparison of real and simulated records using ground motion intensity measures

2021 ◽  
Vol 147 ◽  
pp. 106796
Author(s):  
Shaghayegh Karimzadeh ◽  
Koray Kadas ◽  
Aysegul Askan ◽  
Ahmet Yakut
Keyword(s):  
Ground Motion ◽  
Intensity Measures

Effects of ground motion duration on the seismic damage to and collapse capacity of a mid-rise woodframe building

2019 ◽  
Vol 197 ◽  
pp. 109451 ◽  
Author(s):  
Yuxin Pan ◽  
Carlos E. Ventura ◽  
W.D. Liam Finn ◽  
Haibei Xiong
Keyword(s):  
Ground Motion ◽  
Seismic Damage

scholarly journals Selection of Ground Motion Prediction Equations for the Global Earthquake Model

2015 ◽  
Vol 31 (1) ◽  
pp. 19-45 ◽  
Author(s):  
Jonathan P. Stewart ◽  
John Douglas ◽  
Mohammad Javanbarg ◽  
Yousef Bozorgnia ◽  
Norman A. Abrahamson ◽  
...  
Keyword(s):  
Ground Motion ◽  
Selection Process ◽  
Selection Procedure ◽  
Empirical Models ◽  
Motion Prediction ◽  
Ground Motion Prediction Equations ◽  
Prediction Equations ◽  
Ground Motion Prediction ◽  
Intensity Measures ◽  
Earthquake Model

Ground motion prediction equations (GMPEs) relate ground motion intensity measures to variables describing earthquake source, path, and site effects. From many available GMPEs, we select those models recommended for use in seismic hazard assessments in the Global Earthquake Model. We present a GMPE selection procedure that evaluates multidimensional ground motion trends (e.g., with respect to magnitude, distance, and structural period), examines functional forms, and evaluates published quantitative tests of GMPE performance against independent data. Our recommendations include: four models, based principally on simulations, for stable continental regions; three empirical models for interface and in-slab subduction zone events; and three empirical models for active shallow crustal regions. To approximately incorporate epistemic uncertainties, the selection process accounts for alternate representations of key GMPE attributes, such as the rate of distance attenuation, which are defensible from available data. Recommended models for each domain will change over time as additional GMPEs are developed.

scholarly journals Near-Source Simulation of Strong Ground Motion in Amatrice Downtown Including Site Effects

Geosciences ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 186
Author(s):  
Alessandro Todrani ◽  
Giovanna Cultrera
Keyword(s):  
Ground Motion ◽  
Site Effects ◽  
Central Italy ◽  
Strong Motion ◽  
Seismic Source ◽  
Spectral Ratio ◽  
Intensity Measures ◽  
Standard Spectral Ratio ◽  
Heavy Damage ◽  
Strong Ground

On 24 August 2016, a Mw 6.0 earthquake started a damaging seismic sequence in central Italy. The historical center of Amatrice village reached the XI degree (MCS scale) but the high vulnerability alone could not explain the heavy damage. Unfortunately, at the time of the earthquake only AMT station, 200 m away from the downtown, recorded the mainshock, whereas tens of temporary stations were installed afterwards. We propose a method to simulate the ground motion affecting Amatrice, using the FFT amplitude recorded at AMT, which has been modified by the standard spectral ratio (SSR) computed at 14 seismic stations in downtown. We tested the procedure by comparing simulations and recordings of two later mainshocks (Mw 5.9 and Mw 6.5), underlining advantages and limits of the technique. The strong motion variability of simulations was related to the proximity of the seismic source, accounted for by the ground motion at AMT, and to the peculiar site effects, described by the transfer function at the sites. The largest amplification characterized the stations close to the NE hill edge and produced simulated values of intensity measures clearly above one standard deviation of the GMM expected for Italy, up to 1.6 g for PGA.

Relational database used for ground-motion model development in the NGA-sub project

2021 ◽  
pp. 875529302110552
Author(s):  
Silvia Mazzoni ◽  
Tadahiro Kishida ◽  
Jonathan P Stewart ◽  
Victor Contreras ◽  
Robert B Darragh ◽  
...  
Keyword(s):  
Subduction Zone ◽  
Ground Motion ◽  
Data Storage ◽  
Relational Database ◽  
Model Development ◽  
Intensity Measures ◽  
Motion Models ◽  
Ground Motion Model ◽  
Ground Motion Models ◽  
Pseudo Spectral

The Next-Generation Attenuation for subduction zone regions project (NGA-Sub) has developed data resources and ground motion models for global subduction zone regions. Here we describe the NGA-Sub database. To optimize the efficiency of data storage, access, and updating, data resources for the NGA-Sub project are organized into a relational database consisting of 20 tables containing data, metadata, and computed quantities (e.g. intensity measures, distances). A database schema relates fields in tables to each other through a series of primary and foreign keys. Model developers and other users mostly interact with the data through a flatfile generated as a time-stamped output of the database. We describe the structure of the relational database, the ground motions compiled for the project, and the means by which the data can be accessed. The database contains 71,340 three-component records from 1880 earthquakes from seven global subduction zone regions: Alaska, Central America and Mexico, Cascadia, Japan, New Zealand, South America, and Taiwan. These data were processed on a component-specific basis to minimize noise effects in the data and remove baseline drifts. Provided ground motion intensity measures include peak acceleration, peak velocity, and 5%-damped pseudo-spectral accelerations for a range of oscillator periods.

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