Insufficient Statistical Model Development of Ground-Motion Relations for Regions with Low Seismicity

2014 ◽  
Vol 104 (2) ◽  
pp. 1002-1005 ◽  
Author(s):  
M. Raschke
Keyword(s):  
Statistical Model ◽  
Ground Motion ◽  
Model Development ◽  
Statistical Model Development

Statistical model development for high-dimensional big data analytics

2020 ◽  
Vol 31 (6) ◽  
pp. 1009-1020
Author(s):  
Dae Heung Jang ◽  
Il Do Ha ◽  
Dong Jun Park ◽  
In Ho Park ◽  
Seung Jae Lee
Keyword(s):  
Big Data ◽  
Statistical Model ◽  
Data Analytics ◽  
Model Development ◽  
Big Data Analytics ◽  
High Dimensional ◽  
Statistical Model Development

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.

A Statistical Model of Bubble Coalescence and Its Application to Boiling Heat Flux Prediction—Part II: Experimental Validation

2009 ◽  
Vol 131 (12) ◽  
Author(s):  
Wen Wu ◽  
Barclay G. Jones ◽  
Ty A. Newell
Keyword(s):  
Heat Transfer ◽  
Heat Flux ◽  
Statistical Model ◽  
Mechanistic Model ◽  
Model Development ◽  
Critical Diameter ◽  
Bubble Coalescence ◽  
Average Error ◽  
Static Contact ◽  
Lift Off

A mechanistic model for the boiling heat flux prediction proposed in Part I of this two-part paper (2009, “A Statistical Model of Bubble Coalescence and Its Application to Boiling Heat Flux Prediction—Part I: Model Development,” ASME J. Heat Transfer, 131, p. 121013) is verified in this part. In the first step, the model is examined by experiments conducted using R134a covering a range of pressures, inlet subcoolings, and flow velocities. The density of the active nucleation sites is measured and correlated with critical diameter Dc and static contact angle θ. Underlying submodels on bubble growth and bubble departure/lift-off radii are validated. Predictions of heat flux are compared with the experimental data with an overall good agreement observed. This model achieves an average error of ±25% for the prediction of R134a boiling curves, with the predicted maximum surface heat flux staying within ±20% of the experimentally measured critical heat flux. In the second step, the model is applied to water data measured by McAdams et al. (1949, “Heat Transfer at High Rates to Water With Surface Boiling,” Ind. Eng. Chem., 41(9), pp. 1945–1953) in vertical circular tubes. The consistency suggests that the application of this mechanistic model can be extended to other flow conditions if the underlying submodels are appropriately chosen and the assumptions made during model development remain valid.

scholarly journals Coupling Sensing Hardware with Data Interrogation Software for Structural Health Monitoring

2006 ◽  
Vol 13 (4-5) ◽  
pp. 519-530 ◽  
Author(s):  
Charles R. Farrar ◽  
David W. Allen ◽  
Gyuhae Park ◽  
Steven Ball ◽  
Michael P. Masquelier
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Software Package ◽  
Model Development ◽  
General Purpose ◽  
Statistical Pattern Recognition ◽  
Structural Health ◽  
Statistical Pattern ◽  
Statistical Model Development ◽  
Processing Platform

The process of implementing a damage detection strategy for aerospace, civil and mechanical engineering infrastructure is referred to as structural health monitoring (SHM). The authors' approach is to address the SHM problem in the context of a statistical pattern recognition paradigm. In this paradigm, the process can be broken down into four parts: (1) Operational Evaluation, (2) Data Acquisition and Cleansing, (3) Feature Extraction and Data Compression, and (4) Statistical Model Development for Feature Discrimination. These processes must be implemented through hardware or software and, in general, some combination of these two approaches will be used. This paper will discuss each portion of the SHM process with particular emphasis on the coupling of a general purpose data interrogation software package for structural health monitoring with a modular wireless sensing and processing platform. More specifically, this paper will address the need to take an integrated hardware/software approach to developing SHM solutions.

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