19 research outputs found
Neural Network-Based Equations for Predicting PGA and PGV in Texas, Oklahoma, and Kansas
Parts of Texas, Oklahoma, and Kansas have experienced increased rates of
seismicity in recent years, providing new datasets of earthquake recordings to
develop ground motion prediction models for this particular region of the
Central and Eastern North America (CENA). This paper outlines a framework for
using Artificial Neural Networks (ANNs) to develop attenuation models from the
ground motion recordings in this region. While attenuation models exist for the
CENA, concerns over the increased rate of seismicity in this region necessitate
investigation of ground motions prediction models particular to these states.
To do so, an ANN-based framework is proposed to predict peak ground
acceleration (PGA) and peak ground velocity (PGV) given magnitude, earthquake
source-to-site distance, and shear wave velocity. In this framework,
approximately 4,500 ground motions with magnitude greater than 3.0 recorded in
these three states (Texas, Oklahoma, and Kansas) since 2005 are considered.
Results from this study suggest that existing ground motion prediction models
developed for CENA do not accurately predict the ground motion intensity
measures for earthquakes in this region, especially for those with low
source-to-site distances or on very soft soil conditions. The proposed ANN
models provide much more accurate prediction of the ground motion intensity
measures at all distances and magnitudes. The proposed ANN models are also
converted to relatively simple mathematical equations so that engineers can
easily use them to predict the ground motion intensity measures for future
events. Finally, through a sensitivity analysis, the contributions of the
predictive parameters to the prediction of the considered intensity measures
are investigated.Comment: 5th Geotechnical Earthquake Engineering and Soil Dynamics Conference,
Austin, TX, USA, June 10-13. (2018
Comparative Assessment of Soil-Structure Interaction Regulations of ASCE 7-16 and ASCE 7-10
This paper evaluates the consequences of practicing soil structure
interaction (SSI) regulations of ASCE 7-16 on seismic performance of building
structures. The motivation for this research stems from the significant changes
in the new SSI provisions of ASCE 7-16 compared to the previous 2010 edition.
Generally, ASCE 7 considers SSI as a beneficial effect, and allows designer to
reduce the design base shear. However, literature shows that this idea cannot
properly capture the SSI effects on nonlinear systems. ASCE 7-16 is the first
edition of ASCE 7 that considers the SSI effect on yielding systems. This study
investigates the consequences of practicing the new provisions on a wide range
of buildings with different dynamic characteristics on different soil types.
Ductility demand of the structure forms the performance metric of this study,
and the probability that practicing SSI provisions, in lieu of fixed-base
provisions, increases the ductility demand of the structure is computed. The
analyses are conducted within a probabilistic framework which considers the
uncertainties in the ground motion and in the properties of the soil-structure
system. It is concluded that, for structures with surface foundation on
moderate to soft soils, SSI regulations of both ASCE 7-10 and ASCE 7-16 are
fairly likely to result in a similar and larger structural responses than those
obtained by practicing the fixed-base design regulations. However, for squat
and ordinary stiff structures on soft soil or structures with embedded
foundation on moderate to soft soils, the SSI provisions of ASCE 7-16 result in
performance levels that are closer to those obtained by practicing the
fixed-base regulations. Finally, for structures on very soft soils, the new SSI
provisions of ASCE 7-16 are likely to rather conservative designs.Comment: ASCE Structures Congress, Fort Worth, TX, USA, April 19-21 (2018
Seismic Vulnerability and Post-Event Actions for Texas Bridge Infrastructure
0-6916The research investigates the seismic vulnerability of bridges in Texas by characterizing seismic hazards in the State, developing computational tools to estimate the likelihood of seismic damage to various bridge types, and providing the Texas Department of Transportation (TxDOT) tools to inform post-earthquake response planning and decision-making
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Machine-learning-based models, methods, and software for intensity, vulnerability, and risk assessment of Central U.S. induced earthquakes
Since 2009, the Central U.S. has been subjected to a new type of seismic hazard attributed to human activities from the petroleum industry. Since then, there has been an increase in the number of earthquakes in the Central U.S. from an average of 25 per year in 2008 to 365 in 2017. These earthquakes can adversely affect the safety of infrastructure in the region, considering most were designed with minimal to no seismic detailing considerations due to the historically low seismicity in the region.
The main objective of this dissertation is threefold: 1) To characterize the seismic demand of these earthquakes by developing region-specific ground motion models. 2) To evaluate the vulnerability of the built environment (in particular, bridge portfolios and residential buildings with masonry façades) to these recent earthquakes by developing fragility functions. 3) To integrate the ground motion and fragility models with other region-specific information to investigate regional consequences (i.e., potential economic loss) on the built environment for future seismic events. This information is now used by the Texas Department of Transportation to inform decision-making in terms of post-earthquake response and planning for future events.
For each objective, the present study combines machine learning science with structural and earthquake engineering knowledge into a data-driven, state-of-the-art framework to develop more reliable prediction models compared to the conventional methods in the literature. This dissertation comparatively investigates the advantages of using machine learning techniques instead of conventional methods in developing each model (i.e., ground motion and fragility models).
Moreover, this study investigates the seismic characteristics, vulnerability, and risk associated with these earthquakes, compared with those associated with other seismic hazards in the U.S. The comparison includes similar magnitude natural earthquakes in the Western U.S., New Madrid seismic hazards (i.e., the historical seismic hazard of interest in the Central U.S.), and estimates from HAZUS (i.e., the software provided by Federal Emergency Management Agency for disaster risk assessment).
As part of this study, open-source application software named ShakeRisk is developed for risk, reliability, and resilience assessment of the built environment to natural hazards. ShakeRisk provides a platform to integrate artificial intelligence, systems engineering, structural and earthquake engineering research fields to simulate civil infrastructure responses at both structural and system scales in a reliable and computationally efficient way. Adopting clean architecture principles and object-oriented programming language in the design of ShakeRisk, it can be readily extended by adding features (i.e., new data sources, models, analyses, and user interfaces) and customizing existing ones without the need to modify existing code.Civil, Architectural, and Environmental Engineerin
Fragility of masonry veneers to human-induced Central U.S. earthquakes using neural network models
Artificial Neural Network‐Based Framework for Developing Ground‐Motion Models for Natural and Induced Earthquakes in Oklahoma, Kansas, and Texas
Updated evaluation metrics for optimal intensity measure selection in probabilistic seismic demand models
Seismic Vulnerability and Post-Event Actions for Texas Bridge Infrastructure
Report that investigates the seismic vulnerability of bridges in Texas and provides tools to aid post-earthquake response