Development and Evaluation of Acceleration-based Earthquake Damage Detection and Classification Algorithms
Development and Evaluation of Acceleration-based Earthquake Damage Detection and Classification Algorithms

Author: Konstantinos Balafas

Publisher:

Published: 2015

Total Pages:

ISBN-13:

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Structural Health Monitoring has a significant impact, both economic and in terms of life safety, in a wide variety of industries such as civil, manufacturing and aerospace. The timely detection of defects can prevent economic losses due to malfunctioning equipment or infrastructure and avoid potential life-threatening failures. This is especially true in the aftermath of an extreme loading event such a major earthquake where the identification of the presence, and potentially extent, of damage in a portfolio of structures can prevent further losses and allow for more informed decision making with regards to the recovery of the affected region. This dissertation presents three damage detection and classification algorithms that aim to provide reliable information on the damage state of a civil structure within a matter of minutes following an earthquake. While each algorithm corresponds to a different use case, a common objective is to keep the algorithms as simple and data driven as possible, which allows the application of the algorithms to different structure and loading types. The first algorithm presents a methodology for estimating the residual displacement of structures. It builds on previous work and utilizes stationary and multi-dimensional acceleration measurements to calculate the rotation of the sensor with respect to the direction of gravity and estimate the residual displacement based on the calculated rotation. The proposed algorithm utilizes the measurements from several sensors and estimates the residual displacement along the height of the structure using only the sensor measurements and locations as input. The optimal configuration of the algorithm with respect to parameters such as the number and location of sensors is determined, and the accuracy of the algorithm is evaluated, both using Monte Carlo simulation. In order to provide further validation of the algorithm, the effect of sensor noise and measurement error on the accuracy of the algorithm is evaluated, and recommendations on the minimum number of samples required to obtain a reliable measurement are provided. A series of Damage Sensitive Features based on the Continuous Wavelet Transform of acceleration measurements are developed. The proposed features take into account both the input excitation and the output structural response. A mathematical formulation for the combination of the input and output signals is presented, and methodologies for the extraction of the features are outlined. The correlation of the features with the extent of damage is established via frequently used damage metrics such as hysteretic energy. A damage detection scheme based on the proposed features is presented that utilizes ambient vibration measurements for establishing an undamaged baseline. The damage detection scheme is also validated using Monte Carlo simulation. Finally, a damage classification scheme is proposed where established damage indices are utilized to classify the damage sustained in different categories depending on the extent. The damage classification scheme is also validated through Monte Carlo simulation. A statistical model for the wavelet coefficients of the acceleration structural response is presented. The fundamental assumption behind the proposed model is that the wavelet coefficients at each time sample are transformed realizations of a Gaussian Process that depends only on the damage state of the structure. The model parameters are estimated using Maximum Likelihood Estimation and a systematic methodology for the implementation is proposed and validated. The statistical model is applied to experimental data as a proof of concept and a damage detection scheme based on statistical hypothesis testing is proposed. The capabilities of the rotation algorithm and the Gaussian Process statistical model are illustrated in an actual sensor deployment. These algorithms are applied to the data that were acquired from a series of shake table tests conducted on two steel frames at the National Taiwan University. The results from the algorithm applications are shown and compared to the actual damage state of the specimens.

Deep Learning Applications, Volume 2
Deep Learning Applications, Volume 2

Author: M. Arif Wani

Publisher: Springer

Published: 2020-12-14

Total Pages: 300

ISBN-13: 9789811567582

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This book presents selected papers from the 18th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2019). It focuses on deep learning networks and their application in domains such as healthcare, security and threat detection, fault diagnosis and accident analysis, and robotic control in industrial environments, and highlights novel ways of using deep neural networks to solve real-world problems. Also offering insights into deep learning architectures and algorithms, it is an essential reference guide for academic researchers, professionals, software engineers in industry, and innovative product developers.

Advances in Assessment and Modeling of Earthquake Loss
Advances in Assessment and Modeling of Earthquake Loss

Author: Sinan Akkar

Publisher: Springer Nature

Published: 2021-06-02

Total Pages: 315

ISBN-13: 3030688135

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This open access book originates from an international workshop organized by Turkish Natural Catastrophe Insurance Pool (TCIP) in November 2019 that gathered renown researchers from academia, representatives of leading international reinsurance and modeling companies as well as government agencies responsible of insurance pricing in Turkey. The book includes chapters related to post-earthquake damage assessment, the state-of-art and novel earthquake loss modeling, their implementation and implication in insurance pricing at national, regional and global levels, and the role of earthquake insurance in building resilient societies and fire following earthquakes. The rich context encompassed in the book makes it a valuable tool not only for professionals and researchers dealing with earthquake loss modeling but also for practitioners in the insurance and reinsurance industry.

Damage Diagnosis Algorithms Using Statistical Pattern Recognition for Civil Structures Subjected to Earthquakes
Damage Diagnosis Algorithms Using Statistical Pattern Recognition for Civil Structures Subjected to Earthquakes

Author: Hae Young Noh

Publisher:

Published: 2011

Total Pages:

ISBN-13:

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In order to prevent catastrophic failure and reduce maintenance costs, the demands for the automated monitoring of the performance and safety of civil structures have increased significantly in the past few decades. In particular, there has been extensive research in the development of wireless structural health monitoring systems, which enable dense installation of sensors on structural systems with low installation and maintenance costs. The main challenge of these wireless sensing units is to reduce the amount of data that need to be transmitted wirelessly because the wireless data transmission is the major source of power consumption. This dissertation introduces various damage diagnosis algorithms that use statistical pattern recognition methods at sensor level. Therefore, these algorithms do not require massive transmission of data, and thus are particularly beneficial for use in wireless sensing units. Although damage diagnosis algorithms for structural health monitoring have existed for several decades, statistical pattern recognition techniques have been applied in this field only in the past decade. This approach is receiving increasing recognition for its computational efficiency, which is required when embedding such algorithms in wireless sensing units. These algorithms can use either stationary ambient vibration responses before and after the damage or non-stationary strong motion responses such as earthquake responses. In the first part of this dissertation, three algorithms are introduced for damage diagnosis using ambient vibration responses. Each vibration response is modeled as a time-series with distinct parameters, which are closely related to the structural parameters. Damage diagnosis is performed by classifying the combinations of these parameters into damage states using three statistical pattern recognition methods. The algorithms are validated using the experimental data obtained from the benchmark structure of the National Center for Research on Earthquake Engineering (NCREE) in Taipei, Taiwan, and the results show that these algorithms can detect damage while more improvement is necessary for damage localization. The second part of the dissertation introduces a wavelet-based damage diagnosis algorithm that uses non-stationary strong motion responses. Wavelet energies of each response are extracted from various frequencies at different instances, and three damage sensitive features are defined on the basis of the extracted wavelet energies. These features are probabilistically mapped to damage states using fragility functions. The framework to develop these wavelet damage sensitive feature-based fragility functions is also discussed. The efficiency and robustness of the damage sensitive features are validated using the two sets of experimental data: 30% scaled reinforced concrete bridge column tests in Reno, Nevada, and 1:8 scale model of a four-story steel special moment-resisting frame tests at the State University of New York at Buffalo. The performance of the fragility functions to classify damage is validated using the numerically simulated data obtained from the analytical model of the four-story steel special moment-resisting frame. The results show that the wavelet-based features are closely related to structural damage and the fragility functions can efficiently classify the damage state from the features. The last part of the dissertation discusses a data compression method using a sparse representation algorithm. This method constructs a set of bases to represent each structural response as their weighted sum. By creating an over-complete set of bases, the responses can be represented using a few number of bases (i.e., sparse representation). This method can reduce the amount of data to transmit and save the power consumption of the wireless sensing units. This method enables the entire transmission of response data to a server computer, and more sophisticated analysis of the data can be performed in global level. The method is validated using the white noise experimental data collected from the four-story steel special moment-resisting frame tests at the State University of New York at Buffalo, and significant compression ratio is achieved for upper floors while maintain the information.

Classification and Evaluation of Earthquake Records for Design
Classification and Evaluation of Earthquake Records for Design

Author: Farzad Naeim

Publisher:

Published: 1994-06-01

Total Pages: 288

ISBN-13: 9780788108778

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One of the most difficult questions that the designer of earthquake resistant structures must address is the determination of the critical earthquake. This is the earthquake ground motion which will drive the structure being designed to its critical response. This study provides processed data which will help the design engineer address this problem by identification of the severity and damage potential of components of a large database containing 1,500 records of earthquake ground motion. Over 100 charts and tables.

Analysis, Evaluation, and Improvement of Performance-based Earthquake Engineering Damage and Loss Predictions
Analysis, Evaluation, and Improvement of Performance-based Earthquake Engineering Damage and Loss Predictions

Author: Gemma Joyce Cremen

Publisher:

Published: 2019

Total Pages:

ISBN-13:

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Performance-based earthquake engineering (PBEE) has in many ways revolutionized the thinking about seismic engineering design and acceptable performance of buildings in earthquakes. It is now making its way into commercial engineering design and risk analysis practice, as engineers aim to design better-performing buildings, and holders of mortgage or insurance instruments try to better understand the risk they face from damage to associated buildings. Some parts of the calculations (e.g. structural response) have been extensively assessed and validated. There are few similar studies, however, that focus on the damage and loss predictions. The purpose of this dissertation is to address this, by analyzing, evaluating, and improving the damage and loss predictions. The specific PBEE methodology examined in this dissertation is the FEMA P-58 Seismic Performance Assessment Procedure. FEMA P-58 damage and loss predictions are analyzed, to determine how they are impacted by other parts of the calculations. Firstly, variance-based sensitivity analyses are conducted to investigate the interaction of loss predictions with different inputs to the calculations. Of the six inputs considered in the analyses, it is found that predictions of building repair cost (as a fraction of replacement value) are most sensitive to shaking intensity and building age, while building re-occupancy time predictions are most sensitive to shaking intensity and building lateral system. Secondly, a methodology is developed to quantify the impact of available structural response data from seismic instrumentation on the quality of the damage and loss predictions. The density of instrumentation examined using the methodology ranges from the case in which all floors are instrumented to that in which no floors are instrumented and simplified procedures are used to produce structural response predictions. It is found that the quality of the predictions generally improves as the density of seismic instrumentation increases, but it is not crucial for the density to be very high to achieve reasonable accuracy in both damage and loss predictions (although this may depend on the arrangement of instrumentation within a building). Loss predictions are evaluated using data observed in previous seismic events, to understand the degree to which they reflect real-life consequences of earthquakes. A methodology is developed for evaluating the ability of FEMA P-58 component-level losses to predict damage observed for groups of buildings. It is found in applications of the methodology that FEMA P-58 non-structural component-level loss predictions provide more insight into damage than variations in ground shaking between buildings. Finally, this dissertation includes a number of recommendations for improving non-structural mechanical component fragility functions and associated loss predictions used in FEMA-58 calculations. The fitting technique currently used for the functions does not converge in some cases, and the methodology used to predict anchored mechanical component losses can lead to some unexpected results, such as non-smooth variation of repair costs with anchorage capacity. An alternative statistical technique is proposed for fitting the fragility functions that mitigates the non-convergence problems when fitting and makes predictions that better align with damage observed in past events. A more intuitive methodology for predicting anchored mechanical component losses is also suggested. The findings of this dissertation help to enhance understanding of, and improve, the damage and loss predictions used in the FEMA P-58 seismic performance assessment procedure. They ultimately enable various stakeholders, such as building owners, design professionals, lenders, and insurers, to make more informed decisions about seismic risk.

Predictive Earthquake Damage Modeling for Natural Gas Distribution Infrastructure
Predictive Earthquake Damage Modeling for Natural Gas Distribution Infrastructure

Author: Steven B. Link

Publisher:

Published: 2018

Total Pages: 64

ISBN-13:

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The Pacific Gas and Electric Company (PG&E) operates and maintains 48,000 miles of natural gas pipeline, serving over 4.3 million customer accounts. Along with water, electric power, and transportation services, these lifelines serve critical functions throughout multiple communities. Considering PG&E provides services in both densely populated and seismically active areas, the organization has invested extensively in modeling technology to help estimate resource needs and develop resiliency plans in the event of an earthquake. This thesis aimed to develop a damage prediction model to improve emergency response time and restoration efficiency. The machine-learning based model built upon currently used predictive algorithms, while adding features necessary to account for distribution branch lines and above-ground meter sets. Research and analysis showed factors beyond ground-motion prediction equations could be used to estimate pipeline damage and were consequently included. Furthermore, the model incorporated real-time data acquired throughout repair and restoration efforts in order to improve the predictive performance. Historical incidents were examined in the data aggregation phase in order to develop the training set. For this paper, damage was defined as the number of leaks predicted in a given plat, as defined by PG&E's mapping services. Leaks were categorized in three separate bins, ranging from 0 leaks, 1 to 5 leaks, and 6 or greater leaks. Multiple classification algorithms were chosen and evaluated against a custom scoring metric designed to discriminate and penalize false negatives. The best results were achieved using a series of five logistic regression algorithms, executed at 2, 4, 8, 12 and 24 hours following event occurrence. Results were designed to accompany currently used seismic hazard reports in a ranked table, displaying areas with the highest to lowest probability of experiencing damage. An additional web application was designed to query specific plats for prediction results.

Seismic Structural Health Monitoring
Seismic Structural Health Monitoring

Author: Maria Pina Limongelli

Publisher: Springer

Published: 2019-04-24

Total Pages: 446

ISBN-13: 303013976X

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This book includes a collection of state-of-the-art contributions addressing both theoretical developments in, and successful applications of, seismic structural health monitoring (S2HM). Over the past few decades, Seismic SHM has expanded considerably, due to the growing demand among various stakeholders (owners, managers and engineering professionals) and researchers. The discipline has matured in the process, as can be seen by the number of S2HM systems currently installed worldwide. Furthermore, the responses recorded by S2HM systems hold great potential, both with regard to the management of emergency situations and to ordinary maintenance needs. The book’s 17 chapters, prepared by leading international experts, are divided into four major sections. The first comprises six chapters describing the specific requirements of S2HM systems for different types of civil structures and infrastructures (buildings, bridges, cultural heritage, dams, structures with base isolation devices) and for monitoring different phenomena (e.g. soil-structure interaction and excessive drift). The second section describes available methods and computational tools for data processing, while the third is dedicated to hardware and software tools for S2HM. In the book’s closing section, five chapters report on state-of-the-art applications of S2HM around the world.

Singular Spectrum Analysis
Singular Spectrum Analysis

Author: J.B. Elsner

Publisher: Springer Science & Business Media

Published: 2013-03-09

Total Pages: 167

ISBN-13: 1475725140

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The term singular spectrum comes from the spectral (eigenvalue) decomposition of a matrix A into its set (spectrum) of eigenvalues. These eigenvalues, A, are the numbers that make the matrix A -AI singular. The term singular spectrum analysis· is unfortunate since the traditional eigenvalue decomposition involving multivariate data is also an analysis of the singular spectrum. More properly, singular spectrum analysis (SSA) should be called the analysis of time series using the singular spectrum. Spectral decomposition of matrices is fundamental to much the ory of linear algebra and it has many applications to problems in the natural and related sciences. Its widespread use as a tool for time series analysis is fairly recent, however, emerging to a large extent from applications of dynamical systems theory (sometimes called chaos theory). SSA was introduced into chaos theory by Fraedrich (1986) and Broomhead and King (l986a). Prior to this, SSA was used in biological oceanography by Colebrook (1978). In the digi tal signal processing community, the approach is also known as the Karhunen-Loeve (K-L) expansion (Pike et aI., 1984). Like other techniques based on spectral decomposition, SSA is attractive in that it holds a promise for a reduction in the dimen- • Singular spectrum analysis is sometimes called singular systems analysis or singular spectrum approach. vii viii Preface sionality. This reduction in dimensionality is often accompanied by a simpler explanation of the underlying physics.

Performance Based Seismic Design for Tall Buildings
Performance Based Seismic Design for Tall Buildings

Author: Ramin Golesorkhi

Publisher:

Published: 2017-10-30

Total Pages: 116

ISBN-13: 9780939493562

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Performance-Based Seismic Design (PBSD) is a structural design methodology that has become more common in urban centers around the world, particularly for the design of high-rise buildings. The primary benefit of PBSD is that it substantiates exceptions to prescribed code requirements, such as height limits applied to specific structural systems, and allows project teams to demonstrate higher performance levels for structures during a seismic event.However, the methodology also involves significantly more effort in the analysis and design stages, with verification of building performance required at multiple seismic demand levels using Nonlinear Response History Analysis (NRHA). The design process also requires substantial knowledge of overall building performance and analytical modeling, in order to proportion and detail structural systems to meet specific performance objectives.This CTBUH Technical Guide provides structural engineers, developers, and contractors with a general understanding of the PBSD process by presenting case studies that demonstrate the issues commonly encountered when using the methodology, along with their corresponding solutions. The guide also provides references to the latest industry guidelines, as applied in the western United States, with the goal of disseminating these methods to an international audience for the advancement and expansion of PBSD principles worldwide.