LiDAR-Based Structural Health Monitoring: Applications in Civil Infrastructure Systems

Kaartinen, Elise; Dunphy, Kyle; Sadhu, Ayan

doi:10.3390/s22124610

Cited by 58 publications

(37 citation statements)

References 145 publications

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“…Figure 1 describes SHM considering their application levels including both at local and global levels. Furthermore, NCT such as computer vision tools including RGB and IR camera and Light Detection and Ranging (LiDAR) has demonstrated many applications both at the local level (e.g., crack, or spalling detection) and global level (e.g., displacement and vibration monitoring) (Catbas and Khuc, 2018;Feng and Feng, 2018;Kaartinen et al, 2022).…”

Section: Introduction 1civil Structural Health Monitoringmentioning

confidence: 99%

A literature review: Generative adversarial networks for civil structural health monitoring

Luleci

Çatbaş

Avcı

2022

Front. Built Environ.

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Structural Health Monitoring (SHM) of civil structures has been constantly evolving with novel methods, advancements in data science, and more accessible technology to address issues related to structural safety, operations, and resiliency. Research and development in the civil SHM field during the last few decades have been progressive due to the increasing use of Artificial Intelligence (AI) methods such as Machine Learning (ML) and Deep Learning (DL). Particularly, Generative Adversarial Networks (GAN), which is a subfamily of Deep Learning has been highly favored in the SHM community within the last couple of years. After its release in 2014, GANs (original GAN and other GAN variants) have been in use for a wide variety of applications in various disciplines, and it has been one of the most popular research topics in the AI-ML domain. While there has not been a review study on the applications of GAN in the civil SHM field, this paper aims to fill this gap by presenting a literature review of the studies that employed GAN specifically in civil SHM applications from 2014 to date, in a condensed format. This study intends to inform SHM practitioners and researchers about GANs and present the highlights of the published work on GANs in the civil SHM field.

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Section: Introduction 1civil Structural Health Monitoringmentioning

confidence: 99%

A literature review: Generative adversarial networks for civil structural health monitoring

Luleci

Çatbaş

Avcı

2022

Front. Built Environ.

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“…An NDT/E consists of more sophisticated tools to detect specific local damages inside the structure, e.g., by using ultrasound, ground penetration radar, infrared cameras, and electromagnetic methods [ 27 , 28 , 29 , 30 , 31 ]. Additionally, NCT, such as computer vision tools including RGB and IR cameras and light detection and ranging (LiDAR), have demonstrated their efficiency in many applications both at the local level (e.g., crack or spalling detection) and global level (e.g., displacement and vibration monitoring) [ 4 , 32 , 33 ]. The condition assessment methods are illustrated in a diagram in Figure 1 .…”

Section: Introductionmentioning

confidence: 99%

Extended Reality (XR) for Condition Assessment of Civil Engineering Structures: A Literature Review

Çatbaş

Luleci

Zakaria

et al. 2022

Sensors

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Condition assessment of civil engineering structures has been an active research area due to growing concerns over the safety of aged as well as new civil structures. Utilization of emerging immersive visualization technologies such as Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) in the architectural, engineering, and construction (AEC) industry has demonstrated that these visualization tools can be paradigm-shifting. Extended Reality (XR), an umbrella term for VR, AR, and MR technologies, has found many diverse use cases in the AEC industry. Despite this exciting trend, there is no review study on the usage of XR technologies for the condition assessment of civil structures. Thus, the present paper aims to fill this gap by presenting a literature review encompassing the utilization of XR technologies for the condition assessment of civil structures. This study aims to provide essential information and guidelines for practitioners and researchers on using XR technologies to maintain the integrity and safety of civil structures.

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“…Structural health monitoring (SHM) system [7,13] is a valuable data-driven technology that involves the integration of sensors, data transmission, data analysis, and decision-making to protect these structures. With the advent of next-generation sensors, SHM has been an emergent and powerful diagnostic tool for damage detection and disaster mitigation of largescale structures [24,34,45]. In the past several decades, SHM systems have been widely applied to different civil infrastructures, including bridges, tunnels, railways, and buildings.…”

Section: Introductionmentioning

confidence: 99%

Multiclass anomaly detection in imbalanced structural health monitoring data using convolutional neural network

Zhao

Sadhu

Capretz

2022

J Infrastruct Preserv Resil

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Structural health monitoring (SHM) system aims to monitor the in-service condition of civil infrastructures, incorporate proactive maintenance, and avoid potential safety risks. An SHM system involves the collection of large amounts of data and data transmission. However, due to the normal aging of sensors, exposure to outdoor weather conditions, accidental incidences, and various operational factors, sensors installed on civil infrastructures can get malfunctioned. A malfunctioned sensor induces significant multiclass anomalies in measured SHM data, requiring robust anomaly detection techniques as an essential data cleaning process. Moreover, civil infrastructure often has imbalanced anomaly data where most of the SHM data remain biased to a certain type of anomalies. This imbalanced time-series data causes significant challenges to the existing anomaly detection methods. Without proper data cleaning processes, the SHM technology does not provide useful insights even if advanced damage diagnostic techniques are applied. This paper proposes a hyperparameter-tuned convolutional neural network (CNN) for multiclass imbalanced anomaly detection (CNN-MIAD) modelling. The hyperparameters of the proposed model are tuned through a random search algorithm to optimize the performance. The effect of balancing the database is considered by augmenting the dataset. The proposed CNN-MIAD model is demonstrated with a multiclass time-series of anomaly data obtained from a real-life cable-stayed bridge under various cases of data imbalances. The study concludes that balancing the database with a time shift window to increase the database has generated the optimum results, with an overall accuracy of 97.74%.

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LiDAR-Based Structural Health Monitoring: Applications in Civil Infrastructure Systems

Cited by 58 publications

References 145 publications

A literature review: Generative adversarial networks for civil structural health monitoring

A literature review: Generative adversarial networks for civil structural health monitoring

Extended Reality (XR) for Condition Assessment of Civil Engineering Structures: A Literature Review

Multiclass anomaly detection in imbalanced structural health monitoring data using convolutional neural network

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