Convolutional neural network based structural health monitoring for rocking bridge system by encoding time‐series into images

Abstract

Summary Structural health monitoring of infrastructure especially bridges plays a vital role in post‐earthquake recovery. Coupling emerging techniques in machine learning with structural health monitoring can provide unprecedented tools for damage detection and identification. This paper explores the use of time‐series acceleration or displacement data collected from a shake‐table experiment of a two‐span bridge utilizing pretensioned rocking columns to predict the damage state of each bridge bent, where the major identified damage was the fracture of the longitudinal bars. To overcome the limitation of small data size collected during the shake‐table test that hindered the use of artificial neural networks and recurrent neural networks, the time‐series data were encoded into images using three methods Gramian angular summation field, Gramian angular difference field, and Markov transition field. Then, the encoded images were used as an input for convolutional neural network models. Three different data entries for the input layers were used including encoded images from recorded accelerations, drift ratios, and both. Two training/testing scenarios were proposed to test the efficacy of the convolutional neural networks. Convolutional neural network models trained on Markov transition field encoded images from acceleration performed with 100% accuracy during the training phase and more than 94% for the testing phase.

Temperature Effect on Vibration Properties and Vibration-Based Damage Identification of Bridge Structures: A Literature Review

“…The high dependency on empirical knowledge of traditional crack detectors was overcome through comparative studies, and the results demonstrated that the proposed technique could obtain better accuracy. Mantawy and Mantawy [136] overcame the limitations of a small collected data set by encoding the time-series data into images. Then, a CNN was used to classify the image Based on structural response, He et al [125] adopted a recurrence graph to extract damage features to represent different damage cases; then, a convolutional neural network (CNN) was used to identify different graphs and furthermore to output minor structural damage.…”

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

“…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. 3).…”

“…2 The time series integration scheme adopted by Teng et al (2020) Fig. 3 Time series encoding scheme adopted by Mantawy and Mantawy (2022) to classify the structural damage conditions. Compared with traditional methods, the proposed method showed excellent accuracy in identifying the location and degree of minor damages.…”

Damage identification using convolutional neural networks from instantaneous displacement measurements via image processing

“…CNNs have shown promising results in the field of SHM, and several recent works have addressed this topic. 11,[14][15][16] Since the input data for these algorithms must be reliable, successful identification of damage in structures also depends on effective instrumentation. In addition, in order to be able to locate and quantify damage, it is usually necessary to have many instrumented points in the structure, which can add difficulties to the already complex problem of damage identification.…”

“…CNNs have shown promising results in the field of SHM, and several recent works have addressed this topic. 11,14–16…”