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Research paper thumbnail of Applications of shapelet transform to time series classification of earthquake, wind and wave data

Autonomous detection of desired events from large databases using time series classification is b... more Autonomous detection of desired events from large databases using time series classification is becoming increasingly important in civil engineering as a result of continued long-term health monitoring of a large number of engineering structures encompassing buildings, bridges, towers, and offshore platforms. In this context, this paper proposes the application of a relatively new time series representation named "Shapelet transform", which is based on local similarity in the shape of the time series subsequences. In consideration of the individual attributes distinctive to time series signals in earthquake, wind and ocean engineering, the application of this transform yields a new shape-based feature representation. Combining this shape-based representation with a standard machine learning algorithm, a truly "white-box" machine learning model is proposed with understandable features and a transparent algorithm. This model automates event detection without the intervention of domain practitioners, yielding a practical event detection procedure. The efficacy of this proposed shapelet transform-based autonomous detection procedure is demonstrated by examples, to identify known and unknown earthquake events from continuously recorded ground-motion measurements, to detect pulses in the velocity time history of ground motions to distinguish between near-field and far-field ground motions, to identify thunderstorms from continuous wind speed measurements, to detect large-amplitude wind-induced vibrations from the bridge monitoring data, and to identify plunging breaking waves that have a significant impact on offshore structures.

Papers by Monica ArulJayachandran

Research paper thumbnail of Machine learning based automated identification of thunderstorms from anemometric records using shapelet transform

Journal of Wind Engineering and Industrial Aerodynamics, 2022

Detection of thunderstorms is important to the wind hazard community to better understand extreme... more Detection of thunderstorms is important to the wind hazard community to better understand extreme wind field characteristics and associated wind-induced load effects on structures. This paper contributes to this effort by proposing an innovative course of research that uses machine learning techniques, independent of wind statistics-based parameters, to autonomously identify thunderstorms from large databases containing high-frequency sampled continuous wind speed data. In this context, the use of Shapelet transform is proposed to identify key individual attributes distinctive to extreme wind events based on similarity of the shape of their time series signature. This shape-based representation, when combined with machine learning algorithms, yields a practical event detection procedure with minimal domain expertise. In this paper, the shapelet transform along with Random Forest classifier is employed for the identification of thunderstorms from 1-year of data from 14 ultrasonic anemometers that are a part of an extensive in-situ wind monitoring network in the Northern Mediterranean ports. A collective total of 240 non-stationary records associated with thunderstorms were identified using this method. The results lead to enhancing the pool of thunderstorm data for a more comprehensive understanding of a wide variety of thunderstorms that have not been previously detected using conventional gust factor-based methods.

Research paper thumbnail of Identification of Vortex-Induced Vibration of Tall Building Pinnacle Using Cluster Analysis for Fatigue Evaluation: Application to Burj Khalifa

Journal of Structural Engineering, 2020

AbstractPinnacles on top of tall buildings are vulnerable to vortex-induced vibrations (VIVs). Th... more AbstractPinnacles on top of tall buildings are vulnerable to vortex-induced vibrations (VIVs). These structures may undergo large-amplitude vibrations that can lead to fatigue damage accumulation. ...

Research paper thumbnail of Applications of shapelet transform to time series classification of earthquake, wind and wave data

Engineering Structures, 2021

Autonomous detection of desired events from large databases using time series classification is b... more Autonomous detection of desired events from large databases using time series classification is becoming increasingly important in civil engineering as a result of continued long-term health monitoring of a large number of engineering structures encompassing buildings, bridges, towers, and offshore platforms. In this context, this paper proposes the application of a relatively new time series representation named "Shapelet transform", which is based on local similarity in the shape of the time series subsequences. In consideration of the individual attributes distinctive to time series signals in earthquake, wind and ocean engineering, the application of this transform yields a new shape-based feature representation. Combining this shape-based representation with a standard machine learning algorithm, a truly "white-box" machine learning model is proposed with understandable features and a transparent algorithm. This model automates event detection without the intervention of domain practitioners, yielding a practical event detection procedure. The efficacy of this proposed shapelet transform-based autonomous detection procedure is demonstrated by examples, to identify known and unknown earthquake events from continuously recorded ground-motion measurements, to detect pulses in the velocity time history of ground motions to distinguish between near-field and far-field ground motions, to identify thunderstorms from continuous wind speed measurements, to detect large-amplitude wind-induced vibrations from the bridge monitoring data, and to identify plunging breaking waves that have a significant impact on offshore structures.

Research paper thumbnail of Data Anomaly Detection for Structural Health Monitoring of Bridges using Shapelet Transform

With the wider availability of sensor technology, a number of Structural Health Monitoring (SHM) ... more With the wider availability of sensor technology, a number of Structural Health Monitoring (SHM) systems are deployed to monitor civil infrastructure. The continuous monitoring provides valuable information about the structure that can help in providing a decision support system for retrofits and other structural modifications. However, when the sensors are exposed to harsh environmental conditions, the data measured by the SHM systems tend to be affected by multiple anomalies caused by faulty or broken sensors. Given a deluge of high-dimensional data collected continuously over time, research into using machine learning methods to detect anomalies are a topic of great interest to the SHM community. This paper contributes to this effort by proposing the use of a relatively new time series representation named Shapelet Transform in combination with a Random Forest classifier to autonomously identify anomalies in SHM data. The shapelet transform is a unique time series representation ...

Research paper thumbnail of Shapelets for earthquake detection

This paper introduces EQShapelets (EarthQuake Shapelets) a time-series shape-based approach embed... more This paper introduces EQShapelets (EarthQuake Shapelets) a time-series shape-based approach embedded in machine learning to autonomously detect earthquakes. It promises to overcome the challenges in the field of seismology related to automated detection and cataloging of earthquakes. EQShapelets are amplitude and phase-independent, i.e., their detection sensitivity is irrespective of the magnitude of the earthquake and the time of occurrence. They are also robust to noise and other spurious signals. The detection capability of EQShapelets is tested on one week of continuous seismic data provided by the Northern California Seismic Network (NCSN) obtained from a station in central California near the Calaveras Fault. EQShapelets combined with a Random Forest classifier, detected all of the cataloged earthquakes and 281 uncataloged events with lower false detection rate thus offering a better performance than autocorrelation and FAST algorithms. The primary advantage of EQShapelets ove...

Research paper thumbnail of Machine learning based automated identification of thunderstorms from anemometric records using shapelet transform

Journal of Wind Engineering and Industrial Aerodynamics, 2022

Detection of thunderstorms is important to the wind hazard community to better understand extreme... more Detection of thunderstorms is important to the wind hazard community to better understand extreme wind field characteristics and associated wind-induced load effects on structures. This paper contributes to this effort by proposing an innovative course of research that uses machine learning techniques, independent of wind statistics-based parameters, to autonomously identify thunderstorms from large databases containing highfrequency sampled continuous wind speed data. In this context, the use of Shapelet transform is proposed to identify key individual attributes distinctive to extreme wind events based on similarity of the shape of their time series signature. This shape-based representation, when combined with machine learning algorithms, yields a practical event detection procedure with minimal domain expertise. In this paper, the shapelet transform along with Random Forest classifier is employed for the identification of thunderstorms from 1-year of data from 14 ultrasonic anemometers that are a part of an extensive in-situ wind monitoring network in the Northern Mediterranean ports. A collective total of 240 non-stationary records associated with thunderstorms were identified using this method. The results lead to enhancing the pool of thunderstorm data for a more comprehensive understanding of a wide variety of thunderstorms that have not been previously detected using conventional gust factor-based methods.

Research paper thumbnail of Identification of Vortex-Induced Vibration of Tall Building Pinnacle Using Cluster Analysis for Fatigue Evaluation: Application to Burj Khalifa

Journal of Structural Engineering, 2020

Pinnacles on top of tall buildings are vulnerable to vortex-induced vibrations (VIVs). These stru... more Pinnacles on top of tall buildings are vulnerable to vortex-induced vibrations (VIVs). These structures may undergo large-amplitude vibrations that can lead to fatigue damage accumulation. To assess the performance of buildings and its appendages, numerous structural health monitoring (SHM) programs have been installed on tall buildings. This continuous monitoring generates more than 1 trillion data points per year per building. Also, on many occasions, the data generated by SHM programs contain missing observations. The evaluation of fatigue life using conventional methods becomes an impossible task in this case. This paper introduces the use of machine-learning techniques as a potential solution to deal with the burgeoning data generated by tall building monitoring systems. In particular, the present study involves the evaluation of the crosswind fatigue life of the pinnacle of Burj Khalifa subject to VIVs using cluster analysis. This unsupervised machine-learning technique is used to develop a generalized framework robust to missing data to effectively identify and extract VIVs from a large pool of other responses recorded by the monitoring system. The data generated from 2010 to 2014 by the SmartSync monitoring system installed on Burj Khalifa are utilized for this study. The proposed framework is validated using a wind tunnel dataset of a bridge sectional model undergoing VIVs. The VIVs extracted from the SmartSync system through cluster analysis are used to evaluate the crosswind fatigue damage of the pinnacle of Burj Khalifa using conventional closed-form approximations. The proposed cluster analysis framework uses a step-by-step data-driven decision-making approach, thus widening the applicability of the method to other SHM programs.

Research paper thumbnail of Applications of shapelet transform to time series classification of earthquake, wind and wave data

Autonomous detection of desired events from large databases using time series classification is b... more Autonomous detection of desired events from large databases using time series classification is becoming increasingly important in civil engineering as a result of continued long-term health monitoring of a large number of engineering structures encompassing buildings, bridges, towers, and offshore platforms. In this context, this paper proposes the application of a relatively new time series representation named "Shapelet transform", which is based on local similarity in the shape of the time series subsequences. In consideration of the individual attributes distinctive to time series signals in earthquake, wind and ocean engineering, the application of this transform yields a new shape-based feature representation. Combining this shape-based representation with a standard machine learning algorithm, a truly "white-box" machine learning model is proposed with understandable features and a transparent algorithm. This model automates event detection without the intervention of domain practitioners, yielding a practical event detection procedure. The efficacy of this proposed shapelet transform-based autonomous detection procedure is demonstrated by examples, to identify known and unknown earthquake events from continuously recorded ground-motion measurements, to detect pulses in the velocity time history of ground motions to distinguish between near-field and far-field ground motions, to identify thunderstorms from continuous wind speed measurements, to detect large-amplitude wind-induced vibrations from the bridge monitoring data, and to identify plunging breaking waves that have a significant impact on offshore structures.

Research paper thumbnail of Machine learning based automated identification of thunderstorms from anemometric records using shapelet transform

Journal of Wind Engineering and Industrial Aerodynamics, 2022

Detection of thunderstorms is important to the wind hazard community to better understand extreme... more Detection of thunderstorms is important to the wind hazard community to better understand extreme wind field characteristics and associated wind-induced load effects on structures. This paper contributes to this effort by proposing an innovative course of research that uses machine learning techniques, independent of wind statistics-based parameters, to autonomously identify thunderstorms from large databases containing high-frequency sampled continuous wind speed data. In this context, the use of Shapelet transform is proposed to identify key individual attributes distinctive to extreme wind events based on similarity of the shape of their time series signature. This shape-based representation, when combined with machine learning algorithms, yields a practical event detection procedure with minimal domain expertise. In this paper, the shapelet transform along with Random Forest classifier is employed for the identification of thunderstorms from 1-year of data from 14 ultrasonic anemometers that are a part of an extensive in-situ wind monitoring network in the Northern Mediterranean ports. A collective total of 240 non-stationary records associated with thunderstorms were identified using this method. The results lead to enhancing the pool of thunderstorm data for a more comprehensive understanding of a wide variety of thunderstorms that have not been previously detected using conventional gust factor-based methods.

Research paper thumbnail of Identification of Vortex-Induced Vibration of Tall Building Pinnacle Using Cluster Analysis for Fatigue Evaluation: Application to Burj Khalifa

Journal of Structural Engineering, 2020

AbstractPinnacles on top of tall buildings are vulnerable to vortex-induced vibrations (VIVs). Th... more AbstractPinnacles on top of tall buildings are vulnerable to vortex-induced vibrations (VIVs). These structures may undergo large-amplitude vibrations that can lead to fatigue damage accumulation. ...

Research paper thumbnail of Applications of shapelet transform to time series classification of earthquake, wind and wave data

Engineering Structures, 2021

Autonomous detection of desired events from large databases using time series classification is b... more Autonomous detection of desired events from large databases using time series classification is becoming increasingly important in civil engineering as a result of continued long-term health monitoring of a large number of engineering structures encompassing buildings, bridges, towers, and offshore platforms. In this context, this paper proposes the application of a relatively new time series representation named "Shapelet transform", which is based on local similarity in the shape of the time series subsequences. In consideration of the individual attributes distinctive to time series signals in earthquake, wind and ocean engineering, the application of this transform yields a new shape-based feature representation. Combining this shape-based representation with a standard machine learning algorithm, a truly "white-box" machine learning model is proposed with understandable features and a transparent algorithm. This model automates event detection without the intervention of domain practitioners, yielding a practical event detection procedure. The efficacy of this proposed shapelet transform-based autonomous detection procedure is demonstrated by examples, to identify known and unknown earthquake events from continuously recorded ground-motion measurements, to detect pulses in the velocity time history of ground motions to distinguish between near-field and far-field ground motions, to identify thunderstorms from continuous wind speed measurements, to detect large-amplitude wind-induced vibrations from the bridge monitoring data, and to identify plunging breaking waves that have a significant impact on offshore structures.

Research paper thumbnail of Data Anomaly Detection for Structural Health Monitoring of Bridges using Shapelet Transform

With the wider availability of sensor technology, a number of Structural Health Monitoring (SHM) ... more With the wider availability of sensor technology, a number of Structural Health Monitoring (SHM) systems are deployed to monitor civil infrastructure. The continuous monitoring provides valuable information about the structure that can help in providing a decision support system for retrofits and other structural modifications. However, when the sensors are exposed to harsh environmental conditions, the data measured by the SHM systems tend to be affected by multiple anomalies caused by faulty or broken sensors. Given a deluge of high-dimensional data collected continuously over time, research into using machine learning methods to detect anomalies are a topic of great interest to the SHM community. This paper contributes to this effort by proposing the use of a relatively new time series representation named Shapelet Transform in combination with a Random Forest classifier to autonomously identify anomalies in SHM data. The shapelet transform is a unique time series representation ...

Research paper thumbnail of Shapelets for earthquake detection

This paper introduces EQShapelets (EarthQuake Shapelets) a time-series shape-based approach embed... more This paper introduces EQShapelets (EarthQuake Shapelets) a time-series shape-based approach embedded in machine learning to autonomously detect earthquakes. It promises to overcome the challenges in the field of seismology related to automated detection and cataloging of earthquakes. EQShapelets are amplitude and phase-independent, i.e., their detection sensitivity is irrespective of the magnitude of the earthquake and the time of occurrence. They are also robust to noise and other spurious signals. The detection capability of EQShapelets is tested on one week of continuous seismic data provided by the Northern California Seismic Network (NCSN) obtained from a station in central California near the Calaveras Fault. EQShapelets combined with a Random Forest classifier, detected all of the cataloged earthquakes and 281 uncataloged events with lower false detection rate thus offering a better performance than autocorrelation and FAST algorithms. The primary advantage of EQShapelets ove...

Research paper thumbnail of Machine learning based automated identification of thunderstorms from anemometric records using shapelet transform

Journal of Wind Engineering and Industrial Aerodynamics, 2022

Detection of thunderstorms is important to the wind hazard community to better understand extreme... more Detection of thunderstorms is important to the wind hazard community to better understand extreme wind field characteristics and associated wind-induced load effects on structures. This paper contributes to this effort by proposing an innovative course of research that uses machine learning techniques, independent of wind statistics-based parameters, to autonomously identify thunderstorms from large databases containing highfrequency sampled continuous wind speed data. In this context, the use of Shapelet transform is proposed to identify key individual attributes distinctive to extreme wind events based on similarity of the shape of their time series signature. This shape-based representation, when combined with machine learning algorithms, yields a practical event detection procedure with minimal domain expertise. In this paper, the shapelet transform along with Random Forest classifier is employed for the identification of thunderstorms from 1-year of data from 14 ultrasonic anemometers that are a part of an extensive in-situ wind monitoring network in the Northern Mediterranean ports. A collective total of 240 non-stationary records associated with thunderstorms were identified using this method. The results lead to enhancing the pool of thunderstorm data for a more comprehensive understanding of a wide variety of thunderstorms that have not been previously detected using conventional gust factor-based methods.

Research paper thumbnail of Identification of Vortex-Induced Vibration of Tall Building Pinnacle Using Cluster Analysis for Fatigue Evaluation: Application to Burj Khalifa

Journal of Structural Engineering, 2020

Pinnacles on top of tall buildings are vulnerable to vortex-induced vibrations (VIVs). These stru... more Pinnacles on top of tall buildings are vulnerable to vortex-induced vibrations (VIVs). These structures may undergo large-amplitude vibrations that can lead to fatigue damage accumulation. To assess the performance of buildings and its appendages, numerous structural health monitoring (SHM) programs have been installed on tall buildings. This continuous monitoring generates more than 1 trillion data points per year per building. Also, on many occasions, the data generated by SHM programs contain missing observations. The evaluation of fatigue life using conventional methods becomes an impossible task in this case. This paper introduces the use of machine-learning techniques as a potential solution to deal with the burgeoning data generated by tall building monitoring systems. In particular, the present study involves the evaluation of the crosswind fatigue life of the pinnacle of Burj Khalifa subject to VIVs using cluster analysis. This unsupervised machine-learning technique is used to develop a generalized framework robust to missing data to effectively identify and extract VIVs from a large pool of other responses recorded by the monitoring system. The data generated from 2010 to 2014 by the SmartSync monitoring system installed on Burj Khalifa are utilized for this study. The proposed framework is validated using a wind tunnel dataset of a bridge sectional model undergoing VIVs. The VIVs extracted from the SmartSync system through cluster analysis are used to evaluate the crosswind fatigue damage of the pinnacle of Burj Khalifa using conventional closed-form approximations. The proposed cluster analysis framework uses a step-by-step data-driven decision-making approach, thus widening the applicability of the method to other SHM programs.