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Machine Learning in Structural Engineering Scientia Iranica A (2020) 27(6), 2645{2656 Sharif University of Technology Scientia Iranica Transactions A: Civil Engineering http://scientiairanica.sharif.edu Invited/Review Article Machine learning in structural engineering J.P. Amezquita-Sancheza;, M. Valtierra-Rodrigueza, and H. Adelib a. ENAP-RG, CA Sistemas Dinamicos, Faculty of Engineering, Departments of Electromechanical, and Biomedical Engineering, Autonomous University of Queretaro, Campus San Juan del Rio, Moctezuma 249, Col. San Cayetano, 76807, San Juan del Rio, Queretaro, Mexico. b. Department of Civil, Environmental, and Geodetic Engineering, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenude, Columbus, OH 43220, USA. Received 14 November 2020; accepted 18 November 2020 KEYWORDS Abstract. This article presents a review of selected articles about structural engineering applications of Machine Learning (ML) in the past few years. It is divided into the following Civil structures; areas: structural system identi cation, structural health monitoring, structural vibration Machine learning; control, structural design, and prediction applications. Deep neural network algorithms Deep learning; have been the subject of a large number of articles in civil and structural engineering. There Structural are, however, other ML algorithms with great potential in civil and structural engineering engineering; that are worth exploring. Four novel supervised ML algorithms developed recently by the System identi cation; senior author and his associates with potential applications in civil/structural engineering Structural health are reviewed in this paper. They are the Enhanced Probabilistic Neural Network (EPNN), monitoring; the Neural Dynamic Classi cation (NDC) algorithm, the Finite Element Machine (FEMa), Vibration control; and the Dynamic Ensemble Learning (DEL) algorithm. Structural design; Prediction. © 2020 Sharif University of Technology. All rights reserved. 1. Introduction In general, an ML system consists of three components: inputs comprising a dataset of sig- Machine Learning (ML) is a key Arti cial Intelligence nals/images/features, the ML algorithm, and output (AI) technology that is impacting almost every eld which is associated with the phenomenon studied (see in a signi cant way from image recognition, e.g., pupil Figure 1). ML algorithms can be classi ed into three detection [1], multi-object tracking [2], video surveil- broad categories: lance [3], multi-target regression [4], thermal infrared face identi cation [5], and human activity recogni- a) Supervised learning such as Support Vector Ma- tion [6], to various brain and neuroscience applications, chine (SVM) [14], various neural network models, e.g., building functional brain network [7], motor im- statistical regression, Random Forest (RF) [15], agery brain-computer interface [8,9], mapping scalp to fuzzy classi ers [16], and Decision Trees (DTs); intracranial EEG [10], seizure detection [11], diagnosis of the Parkinson's disease [12], and characterization of b) Unsupervised learning such as various clustering al- the modulation of the hippocampal rhythms [13]. gorithms, e.g., k-means clustering and hierarchical clustering [17], autoencoders, self-organizing maps, competitive learning [18], and deep Boltzmann *. Corresponding author. Tel.: +52 (1) 427 274 12 44 machine; E-mail address: [email protected] (J.P. Amezquita-Sanchez) c) Reinforcement learning such as Q-learning, R- learning, and Temporal Di erence (TD) learn- doi :10.24200/sci.2020.22091 ing [19]. 2646 J.P. Amezquita-Sanchez et al./Scientia Iranica, Transactions A: Civil Engineering 27 (2020) 2645{2656 Figure 1. Components of an ML system. The rst journal article on civil engineering appli- associated with the structural behavior. Amezquita- cation of neural networks was published in 1989 [20]. Sanchez et al. [25] presented a robust methodology for Amezquita-Sanchez et al. [21] presented a review of identi cation of modal parameters of large smart struc- research articles on neural networks in civil engineering tures based on the adroit integration of the multiple published from 2001 to 2016. This article presents signal classi cation algorithm, the empirical wavelet a review of selected articles on structural engineering transform, and the Hilbert transform [26] and applied it applications of ML in recent years since 2017. It is to calculate the natural frequencies and damping ratios divided into the following topics where most of the of a 123-story super high-rise building structure, the ML research in structural engineering is published: Lotte World Tower, the tallest building in Korea (see structural system identi cation, structural health mon- Figure 2), subjected to ambient vibrations. The results itoring, structural vibration control, structural design, showed that the proposed approach could identify the and prediction applications. In addition, four novel natural frequencies and damping ratios of large civil supervised ML algorithms developed recently by the structures with high accuracy. senior author and his associates with potential appli- For a more robust SSI strategy capable of deal- cations in civil/structural engineering are introduced. ing with the inherent noise, nonlinearities, and un- certainties present in the acquired samples, Perez- 2. Structural system identi cation Ramirez et al. [27] combined the empirical mode de- Structural System Identi cation (SSI) is an important topic in structural engineering as it allows constructing a mathematical model of a structural system from a set of input-output measurements generated by dynamic time series signals [22]. Perez-Ramirez et al. [23] presented a methodology for identi cation of modal parameters of structures using ambient vibrations and Synchrosqueezed Wavelet Transform (SWT). Jiang et al. [24] introduced a fuzzy stochastic neural network model for nonparametric identi cation of civil struc- tures using the nonlinear autoregressive moving aver- age with exogenous inputs model through the combina- tion of two computational intelligence techniques, i.e., fuzzy logic and neural networks. The proposed model was validated using a 1:20 scaled model of a 38-storey concrete building and a benchmark 4-story 2 2 bay 3D steel frame. Denoising a signal for an e ective SSI scheme can represent a challenging task because this process can also inadvertently remove frequency components Figure 2. Lotte World Tower in Seoul, Korea. J.P. Amezquita-Sanchez et al./Scientia Iranica, Transactions A: Civil Engineering 27 (2020) 2645{2656 2647 composition [28{30], a recurrent neural network model, with the goal of determining the health condition of the Bayesian training [31], and mutual information for structure in an automated manner. response prediction of civil structures subjected to Kosticand Gul[36] combined an autoregressive extreme loadings. The e ectiveness of the proposed model with exogenous inputs with a Multi-Layer Per- approach was validated using the experimental data ceptron Neural Network (MLPNN) for damage detec- of a 1:20-scaled 38-story high-rise building structure tion of a simulated footbridge structure at varying subjected to arti cial seismic excitations and ambient temperatures. Pan et al. [37] evaluated three time- vibrations and a ve-story steel frame subjected to frequency methods, Wavelet Transform (WT), Hilbert- di erent levels of the Kobe earthquake. Huang Transform (HHT), and Teager-Huang Trans- Yao et al. [32] reported blind modal identi cation form (THT) for identifying features in measured signals using limited sensors and a modi ed sparse component in combination with SVM using a simulated cable- analysis. Yuen and Huang [33] introduced a Bayesian stayed bridge. Frequency-domain substructure Identi cation. Yuen Incipient or light damage represents a challenge et al. [34] described self-calibrating Bayesian real-time for identi cation. Yanez-Borjas et al. [38] proposed system identi cation. Tian et al. [35] discussed system the fusion of statistical indices and a decision tree for identi cation of pedestrian bridges using particle detecting damage due to corrosion in a 3D 9-bay and image velocimetry. 169-member truss-type bridge subjected to dynamic excitations. The authors reported that the proposal could identify light damage due to external corrosion, 3. Structural Health Monitoring (SHM) causing 1 mm reduction in the bar element diame- Structural Health Monitoring (SHM) continues to ter. Amezquita-Sanchez [39] integrated the Shannon be the subject of intensive research in structural entropy index with a decision tree for evaluating the engineering. It can be divided into two categories measured responses of a 1:20 scaled model of a 38- of image-based SHM employing the computer vision storey concrete building structure under di erent levels technology and vibration signal-based SHM based of damage produced by cracks. on the signals obtained during dynamic events. The aforementioned works have exhibited ad- The latter in turn can be divided into two general vances in SHM; however, they require a hand-crafted approaches: parametric system identi cation (modal feature extraction approach to e ective classi cation in parameters identi cation) and non-parametric the subsequent step [40]. In recent years, deep learning system identi cation. ML algorithms have been used algorithms such as Convolutional Neural Networks extensively in both types of SHM. (CNNs) have been employed for automatic feature ex- traction in SHM. In these methods, feature
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