A Framework of Structural Damage Detection for Civil Structures Using Fast Fourier Transform and Deep Convolutional Neural Networks

He, Yang; Chen, Hongyang; Liu, Die; Zhang, Likai

doi:10.3390/app11199345

Cited by 26 publications

(12 citation statements)

References 36 publications

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“…Recent studies show that even deep learning models can achieve better recognition accuracy if meaningful features are extracted. Among feature extraction techniques, FFT has proven highly effective [ 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 ], including on the DEAP [ 48 ]. In this study, FFT was chosen to extract features from DEAP EEG signals.…”

Section: Methodsmentioning

confidence: 99%

M1M2: Deep-Learning-Based Real-Time Emotion Recognition from Neural Activity

Akter

Prodhan

Pias

et al. 2022

Sensors

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Emotion recognition, or the ability of computers to interpret people’s emotional states, is a very active research area with vast applications to improve people’s lives. However, most image-based emotion recognition techniques are flawed, as humans can intentionally hide their emotions by changing facial expressions. Consequently, brain signals are being used to detect human emotions with improved accuracy, but most proposed systems demonstrate poor performance as EEG signals are difficult to classify using standard machine learning and deep learning techniques. This paper proposes two convolutional neural network (CNN) models (M1: heavily parameterized CNN model and M2: lightly parameterized CNN model) coupled with elegant feature extraction methods for effective recognition. In this study, the most popular EEG benchmark dataset, the DEAP, is utilized with two of its labels, valence, and arousal, for binary classification. We use Fast Fourier Transformation to extract the frequency domain features, convolutional layers for deep features, and complementary features to represent the dataset. The M1 and M2 CNN models achieve nearly perfect accuracy of 99.89% and 99.22%, respectively, which outperform every previous state-of-the-art model. We empirically demonstrate that the M2 model requires only 2 seconds of EEG signal for 99.22% accuracy, and it can achieve over 96% accuracy with only 125 milliseconds of EEG data for valence classification. Moreover, the proposed M2 model achieves 96.8% accuracy on valence using only 10% of the training dataset, demonstrating our proposed system’s effectiveness. Documented implementation codes for every experiment are published for reproducibility.

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Section: Methodsmentioning

confidence: 99%

M1M2: Deep-Learning-Based Real-Time Emotion Recognition from Neural Activity

Akter

Prodhan

Pias

et al. 2022

Sensors

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“…Machine learning and, in particular, the deep learning paradigm, have increasingly gained popularity among scholars for vibration data processing [7]. For instance, in [8], the authors proposed a data-driven SHM method based on convolutional neural networks and fast Fourier transform to identify structural damage conditions from vibration data. An autoencoder architecture targeting nonlinear dimensionality reduction of input vibration signals has been recently introduced for the task of load identification in [9].…”

Section: Related Workmentioning

confidence: 99%

A Machine Learning Enabled Hall-Effect IoT-System for Monitoring Building Vibrations

Lattanzi¹,

Capellacci²,

Freschi³

2023

IJACSA

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Vibration monitoring of civil infrastructures is a fundamental task to assess their structural health, which can be nowadays carried on at reduced costs thanks to new sensing devices and embedded hardware platforms. In this work, we present a system for monitoring vibrations in buildings based on a novel, cheap, Hall-effect vibration sensor that is interfaced with a commercially available embedded hardware platform, in order to support communication toward cloud based services by means of IoT communication protocols. Two deep learning neural networks have been implemented and tested to demonstrate the capability of performing nontrivial prediction tasks directly on board of the embedded platform, an important feature to conceive dynamical policies for deciding whether to perform a recognition task on the final (resource constrained) device, or delegate it to the cloud according to specific energy, latency, accuracy requirements. Experimental evaluation on two use cases, namely the detection of a seismic event and the count of steps made by people transiting in a public building highlight the potential of the adopted solution; for instance, recognition of walking-induced vibrations can be achieved with an accuracy of 96% in real-time within time windows of 500ms. Overall, the results of the empirical investigation show the flexibility of the proposed solution as a promising alternative for the design of vibration monitoring systems in built environments.

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“…Encoding time series into images through various algorithms, such as wavelet transform (WT) (Mangalathu and Jeon 2020), continuous wavelet transform (Chen et al 2021), FFT (He et al 2021a), Fourier amplitude spectra (Duan et al 2019), is another method to employ 2D CNN. Mantawy and Mantawy (2022) encoded time-series data, including accelerations, drift rations, and both, into images using three approaches: Gramian angular summation field, Gramian angular difference field, and Markov transition field (MTF) (see Fig.…”

Section: Damage Scenario Classificationmentioning

confidence: 99%

The application of deep learning in bridge health monitoring: a literature review

Zhang

Wang

et al. 2022

ABEN

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Along with the advancement in sensing and communication technologies, the explosion in the measurement data collected by structural health monitoring (SHM) systems installed in bridges brings both opportunities and challenges to the engineering community for the SHM of bridges. Deep learning (DL), based on deep neural networks and equipped with high-end computer resources, provides a promising way of using big measurement data to address the problem and has made remarkable successes in recent years. This paper focuses on the review of the recent application of DL in SHM, particularly damage detection, and provides readers with an overall understanding of the missions faced by the SHM of the bridges. The general studies of DL in vibration-based SHM and vision-based SHM are respectively reviewed first. The applications of DL to some real bridges are then commented. A summary of limitations and prospects in the DL application for bridge health monitoring is finally given.

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A Framework of Structural Damage Detection for Civil Structures Using Fast Fourier Transform and Deep Convolutional Neural Networks

Cited by 26 publications

References 36 publications

M1M2: Deep-Learning-Based Real-Time Emotion Recognition from Neural Activity

M1M2: Deep-Learning-Based Real-Time Emotion Recognition from Neural Activity

A Machine Learning Enabled Hall-Effect IoT-System for Monitoring Building Vibrations

The application of deep learning in bridge health monitoring: a literature review

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