Real-Time Video Surveillance Based Structural Health Monitoring of Civil Structures Using Artificial Neural Network

Medhi, Moushumi; Dandautiya, Aradhana; Raheja, Jagdish Lal

doi:10.1007/s10921-019-0601-x

Cited by 18 publications

(10 citation statements)

References 50 publications

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“…The SHM systems capture continuous data of structural variations caused by the vibrational motions at key building points. The raw sensory data is often transmitted to a central SHM system via a wired or wireless medium [30]. Modern SHM digitize sensory information to extract feature required for structural damage classification.…”

Section: A Snn Based Shm Classification Modelmentioning

confidence: 99%

Spiking Neural Network-based Structural Health Monitoring Hardware System

Javed

Harkin

McDaid

et al. 2021

2021 IEEE Symposium Series on Computational Intelligence (SSCI)

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Structural Health Monitoring (SHM) systems estimate damages that affect structural reliability. Modern SHM requires continuous monitoring to detect damage caused by day-to-day loading and usage. SHM analyzes structural integrity and identifies weaknesses leading to potential building collapse. In disaster situations e.g., earthquakes, SHM systems enable early assessment of building safety and therefore ensure evacuation and prevention of human losses. In an urban area, multi-story constructions in congested areas become a hazard if their structural health is not well monitored and maintained properly. A key challenge is the ability to detect damages in an efficient manner for edge computing. In this work, we propose an SNN based low-cost, energy-efficient, and standalone damage classification model for SHM. The proposed classification model is implemented on FPGAs and results show a classification accuracy of 99.46% on a sensory dataset for earthquake damage on a 7-Story concrete building.

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Section: A Snn Based Shm Classification Modelmentioning

confidence: 99%

Spiking Neural Network-based Structural Health Monitoring Hardware System

Javed

Harkin

McDaid

et al. 2021

2021 IEEE Symposium Series on Computational Intelligence (SSCI)

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“…Therefore, this work proposed an SHM solution based on a SNN hardware system with self-repairing capability that will improve the electronic system reliability and life-span in harsh environments. To the best of the authors’ knowledge, conventional ANN and Probabilistic Neural Networks (PNN) are widely used for structural damage detection [ 24 , 25 , 26 ] but no structural health monitoring application of SNN has been reported in the literature. Therefore, by combining the energy-efficient SNN classification algorithm and the highly compact neural network hardware, the performance and lifetime of the SHM system can be improved.…”

Section: Related Workmentioning

confidence: 99%

Case Study—Spiking Neural Network Hardware System for Structural Health Monitoring

Pang

Liu

Harkin

et al. 2020

Sensors

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This case study provides feasibility analysis of adapting Spiking Neural Networks (SNN) based Structural Health Monitoring (SHM) system to explore low-cost solution for inspection of structural health of damaged buildings which survived after natural disaster that is, earthquakes or similar activities. Various techniques are used to detect the structural health status of a building for performance benchmarking, including different feature extraction methods and classification techniques (e.g., SNN, K-means and artificial neural network etc.). The SNN is utilized to process the sensory data generated from full-scale seven-story reinforced concrete building to verify the classification performances. Results show that the proposed SNN hardware has high classification accuracy, reliability, longevity and low hardware area overhead.

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“…One the one hand, digital image and video processing methods and vision technologies have seen remarkable advancements at more affordable costs thanks to the development of advanced materials for hardware equipment and improvements in new algorithms for more powerful and robust processing [5][6][7][8][9]. On the other hand, conventional methods for structural monitoring usually require a rather difficult and costly equipment set-up, requiring the positioning of a relatively limited number of expensive sensors (e.g., accelerometers and velocimeters) as the measurement points of the structure, which need to be physically reachable by human operators, sometimes even leading to safety concerns [10]. In addition, these conventional contact methods usually require periodical maintenance and potential replacement, which causes additional costs with the passage of time.…”

Section: Introductionmentioning

confidence: 99%

Advanced Video-Based Processing for Low-Cost Damage Assessment of Buildings under Seismic Loading in Shaking Table Tests

Cataldo

Roselli

Fioriti

et al. 2023

Sensors

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This paper explores the potential of a low-cost, advanced video-based technique for the assessment of structural damage to buildings caused by seismic loading. A low-cost, high-speed video camera was utilized for the motion magnification processing of footage of a two-story reinforced-concrete frame building subjected to shaking table tests. The damage after seismic loading was estimated by analyzing the dynamic behavior (i.e., modal parameters) and the structural deformations of the building in magnified videos. The results using the motion magnification procedure were compared for validation of the method of the damage assessment obtained through analyses of conventional accelerometric sensors and high-precision optical markers tracked using a passive 3D motion capture system. In addition, 3D laser scanning to obtain an accurate survey of the building geometry before and after the seismic tests was carried out. In particular, accelerometric recordings were also processed and analyzed using several stationary and nonstationary signal processing techniques with the aim of analyzing the linear behavior of the undamaged structure and the nonlinear structural behavior during damaging shaking table tests. The proposed procedure based on the analysis of magnified videos provided an accurate estimate of the main modal frequency and the damage location through the analysis of the modal shapes, which were confirmed using advanced analyses of the accelerometric data. Consequently, the main novelty of the study was the highlighting of a simple procedure with high potential for the extraction and analysis of modal parameters, with a special focus on the analysis of the modal shape’s curvature, which provides accurate information on the location of the damage in a structure, while using a noncontact and low-cost method.

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Real-Time Video Surveillance Based Structural Health Monitoring of Civil Structures Using Artificial Neural Network

Cited by 18 publications

References 50 publications

Spiking Neural Network-based Structural Health Monitoring Hardware System

Spiking Neural Network-based Structural Health Monitoring Hardware System

Case Study—Spiking Neural Network Hardware System for Structural Health Monitoring

Advanced Video-Based Processing for Low-Cost Damage Assessment of Buildings under Seismic Loading in Shaking Table Tests

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