Optimization-Based Evolutionary Data Mining Techniques for Structural Health Monitoring

Gordan, Meisam; Ismail, Zubaidah; Razak, Hashim Abdul; Ghaedi, Khaled; Ghayeb, Haider Hamad

doi:10.32732/jcec.2020.9.1.14

Cited by 16 publications

(13 citation statements)

References 77 publications

(93 reference statements)

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“…Besides, various metaheuristic algorithms, e.g. Particle Swarm Optimization (PSO) [23], Genetic Algorithm (GA) [24], and Imperialist Competitive Algorithm (ICA) [25] have been developed to solve a variety of engineering optimization problems in SHM [26].…”

Section: Remote Sensing Technologymentioning

confidence: 99%

A Brief Overview and Future Perspective of Unmanned Aerial Systems for In-Service Structural Health Monitoring

Miya¹

2021

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Remote sensing with Unmanned Aerial Systems (UASs) is a game-changer in various fields such as environmental monitoring, surveillance, aerial photography, digital communications, search and rescue operations and military. This paper presents one of the most economical and yet the most effective approaches used in Structural Health Monitoring (SHM). Herein, a review of the recent advances, applications and future perspective of UASs, i.e. drones in SHM is discussed. Drones have become popular in several developed countries in recent years. However, the use of drones is still in the infancy stage of development in developing countries. The development of drones in the last decade has marked a new era in remote sensing, providing data of unprecedented spatial, spectral, and temporal resolution. This is due to the fact that UASs are low cost aerial robots, that require little preparation and infrastructure and can be equipped with any number of sensors or cameras making them ideal for monitoring the environment. To this end, drones offer an opportunity to infrastructures the existing gap between field observations and remote sensing by providing high spatial detail over relatively large areas in a cost-effective way.

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Section: Remote Sensing Technologymentioning

confidence: 99%

A Brief Overview and Future Perspective of Unmanned Aerial Systems for In-Service Structural Health Monitoring

Miya¹

2021

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“…GA is an optimization approach based on concepts of natural evolution [19]. A hybrid approach using wavelet multiresolution analysis and GA was developed by [18] as a damage detection system.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

“…According to [19], applicable metaheuristic techniques, which can be applied in SHM, are grouped in various types, such as swarm-, biology-, music-, physics-, sport-, social-, chemistry-, and math-based as shown in Fig. 12.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

From Conventional to Sustainable SHM: Implementation of Artificial Intelligence in The Department of Civil Engineering, University of Malaya

Gordan

Ghaedi²,

Ismail

et al. 2021

2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)

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Computer-based technologies and their applications pervade everywhere in real life, especially in different fields of civil engineering. For example, conventional structural health monitoring (SHM) has been rapidly upgraded to sustainable SHM using artificial intelligence. It is because conventional approaches are challenged by real-time, low-cost, and quality-guaranteed SHM. In this direction, a number of innovative researches have been carried out in the Department of Civil Engineering, University of Malaya. This paper attempts to present the latest developments of SHMbased artificial intelligence in Structural Health Monitoring Research Group (StrucHMRSGroup) and Advance Shock and Vibration Research Group (ASVR). To this end, the applications of artificial neural networks, fuzzy logic, genetic algorithm, data mining, and regression analysis in SHM are presented with the aim of showing the efficiency of these methods.

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“…Besides, different methods and models such as the application of modal reduction and mode superposition technique for efficient estimation of critical (highly stressed) areas in civil structures [24], spatial incompatibility filters [25], data mining [26], [27], artificial neural network [28], clustering analysis [8], genetic algorithm [29], deep learning [30], fuzzy logic [31], principal component analysis [32], Bayesian [33], support vector machine [34], particle swarm optimization [35], decision tree [36], regression analysis [37], remote sensing [38], unmanned aerial systems [39], [40], and anomaly detection [41] have been also applied in SHM.…”

Section: Introductionmentioning

confidence: 99%

Implementation of a Secure Storage Using Blockchain for PCA-FRF Sensor Data of Plate-Like Structures

et al. 2022

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Structural Health Monitoring (SHM) systems have widely been used to guarantee the safe functioning of electrical, mechanical, civil, and aerospace engineering assets. It is required to improve the SHM systems from different aspects in terms of smartification, performance, beneficiation, sustainment, automation, cost-effectivity, and safety using cutting-edge technologies. Blockchain is currently the most revolutionary technology in computer science. Implementation of Blockchain in different fields of academia and industry has recently given very good results. However, application of Blockchain in SHM is still in the infancy stage. Therefore, many challenges are still ahead. One of the main potential applications of Blockchain is to secure the sensor data. In this study, a local Blockchain approach is developed as a secure storage using the extracted Principal Components (PCs) of Frequency Response Function (FRF) data obtained from modal analysis of a plate-like structure. In general, Secure Hash Algorithm (SHA) is one of the most practical hash functions with efficient performance which has been employed in Bitcoin. Therefore, in this research, SHA-256 is considered to generate the hash for each block. To the best of our knowledge, this is the first attempt to develop a secure storage for SHM data using Blockchain.

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Optimization-Based Evolutionary Data Mining Techniques for Structural Health Monitoring

Cited by 16 publications

References 77 publications

A Brief Overview and Future Perspective of Unmanned Aerial Systems for In-Service Structural Health Monitoring

A Brief Overview and Future Perspective of Unmanned Aerial Systems for In-Service Structural Health Monitoring

From Conventional to Sustainable SHM: Implementation of Artificial Intelligence in The Department of Civil Engineering, University of Malaya

Implementation of a Secure Storage Using Blockchain for PCA-FRF Sensor Data of Plate-Like Structures

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