A novel machine learning‐based algorithm to detect damage in high‐rise building structures

Abstract

A novel model is presented for global health monitoring of large structures such as high-rise building structures through adroit integration of 2 signal processing techniques, synchrosqueezed wavelet transform and fast Fourier transform, an unsupervised machine learning technique, the restricted Boltzmann machine, and a recently developed supervised classification algorithm called neural dynamics classification (NDC) algorithm. The model extracts hidden features in the frequency domain of the denoised measured response signals recorded by sensors on different elevations or floors of a structure. The extracted features are used as an input of the NDC to detect and classify the global health of the structure into categories such as healthy, light damage, moderate damage, severe damage, and near collapse. The proposed model is validated using the data obtained from a 3D 1:20 scaled 38-story reinforced concrete building structure. The results are compared with 3 other supervised classification algorithms: k-nearest neighbor (KNN), probabilistic neural networks (PNN), and enhanced PNN (EPNN). NDC, EPNN, PNN, and KNN yield maximum average accuracies of 96%, 94%, 92%, and 82%, respectively. KEYWORDS health monitoring, high-rise building, machine learning, neural dynamics model of Adeli and Park, neural networks, tall building 1 | INTRODUCTION | System identification methodsStructural health monitoring (SHM) can be divided into two classes, vision-based SHM [1] and vibration-based SHM. [2,3] The latter often relies on system identification to evaluate the overall health of the structure and detect the damage location and severity. [4] System identification methods can be divided into two categories: (a) parametric system identification [5][6][7] and (b) nonparametric system identification (NSI). [8,9] Parametric system identification methods determine structural static or dynamic properties such as flexibility [10] or stiffness, [11][12][13] damping, [14] natural frequencies, [15,16] and mode shapes [17] usually assuming a linear behavior. [18] In contrast, NSI methods do not provide the aforementioned structural properties and can model nonlinear dynamic systems more effectively. They provide an estimate of the structural time series response based on a given time series input excitation. An NSI method should be able to detect features in time series responses based on the variation of the input signal. [19,20] These features are usually not meaningful from a structural engineering point of view but can be used for structural system identification and vibration-based SHM. [4] Among recent approaches used for NSI are autoregression moving average exogeneous neural networks, [21] dynamic fuzzy wavelet neural network, [22,23] and synchrosqueezed wavelet transform (SWT). [24,25] 1.2 | Vibration-based SHM In general, vibration-based SHM can be divided into five general steps: (a) sensor selection and installation, [26] (b) data collection, (c) data processing, (d) feature extraction, and (e) damage detection. [27][28][29] Th...

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