Examples of Machine Learning Applications in structural engineering

A board range of relatively recent ML publications in structural engineering field are summarized based on their application into the four major categories: 1) predicting nonlinear structural responses and damage; 2) experimental data interpretation and empirical fitting; 3) information extraction from visual media; and 4) pattern recognition for structural health monitoring.

2.4.1 Predicting Structural Nonlinear Responses and Damage

The ability to use ML to predict using either regression or supervised learning provides a data-driven alternative to determine structural responses or classify damage states for structures under various scenarios. Structural demand data usually comes from a large set of NL-RHA, although it is becoming more common that such data might be retrieved from instrumented buildings. Depending on the objective, these NL-RHA were conducted using models ranging from 1) a single type of structure whose structural characteristics may vary; 2) a single or pool of structure models subjected to a defined hazard at different intensity levels; and 3) a cluster of structure models in a regional assessment scenario.

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To detect structural damage, Zhang et. al. [8] applied random forest for damage detection for a 4-story reinforced concrete building case study using the response demand output from NL- RHA, whereas Bagriacik et. al. [53] applied logistic regression, boosting regression trees and random forest to identify individual structural pipe damage status. To predict structural seismic responses using regression algorithms for a single structure, example applications include [54], where Support Vector Regression (SVR) was used to predict seismic engineering demand parameters using NL-RHA data from a single degree of freedom system, a four-story building and a bridge pile, whereas Soleimani et. al. [55] applied LASSO regression to determine uncertain parameter significance seismic demand prediction for an irregular bridge. To estimate demand parameters that are critically related to structural damage, Burton et. al. proposed a framework for aftershock collapse vulnerability using mainshock intensity, structural responses, and physical damage indicators [38], whereas Mangalathu, et. al. estimated seismic vulnerability and fragility curves for skewed bridges [56].

For a regional setting, Sun et. al. [28] reconstructed peak structural response engineering demand parameters by utilizing structural and spatial dissimilarities within in a seismic event using kriging models, and further extended the framework using kernel-based ML regression algorithms, kernel ridge regression, and kernel SVR to reconstruct a profile of responses for engineering demand parameters [27]. Burton et. al. [10] presents a conceptual framework for modeling post- earthquake housing recovery with building-level damage limit states which could be scaled up to an event scenario at the region level.

2.4.2 Experimental Data Interpretation and Empirical Fitting

One advantage of adopting modern ML algorithms is that it is possible to extract data patterns from complex structural system behavior and identify structure damage without human

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inspection, which is the most common approach used. Ghiasi et. al. [25] conducted an experiment on a four-story prototype structure and mimicked damage scenarios by removing braces at each floor. A SVM classifier with a combined parameter kernel function was applied to predict structural damage scenarios using acceleration sensor response history signals at each floor as input features.

Besides the complex tasks of damage detection mentioned above, which is not viable through traditional empirical data fitting in a laboratory testing, ML also shows superior prediction performance on traditional tasks. For example, Vu and Hoang [57] proposed a kernel-based SVM model to map factors that influenced the punching shear capacity of fiber-reinforced polymer (FRP) from a dataset of laboratory tests performed on FRP-reinforced concrete slabs and demonstrated that the approach outperformed traditional formula-based methods based on RMSE. Naeej et. al. [58] applied a simple ML algorithm, decision tree, to predict lateral confinement coefficients in reinforced concrete columns to show that the approach outperformed traditional empirical formula. These two examples, one that deploys relatively complex SVM algorithm, and one that uses a simple decision tree, both demonstrate that ML algorithms can provide more accurate information than traditional empirical formulas to fit patterns for large datasets.

2.4.3 Information Extraction from Visual Media

Traditionally, structural damage detection at the structural component level through visual media has been accomplished by converting image data into strain distribution data using Digital Image Correlation (DIC), a mechanism based on a technique that captures local deformations using cross-correlation between randomly painted dots on a concrete surface, such as [59], [60] and [61]. The limitation is clear: 1) DIC requires a randomly painted dot field over the surface of interest, which is, for the most part, only practicable in laboratory setup; 2) a consistent length of image

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sequence is necessary for calculating cross-correlations between painted dots; and 3) the converted damage indication data, the local strain distributions, only accounts for a subset of the information available from an image.

Vision-based techniques can extract additional information from image data. For example, Yeum and Dyke [62] deployed HOG and HAAR feature-based object detection schemes with a sliding window search to identify cracks near bolts from structure images. The HOG [34] and HAAR feature [32] classifiers are mainly texture feature extractors and are generally not as successful at image feature extraction as the CNN technique, which is extremely efficient in extracting both marco- and micro-scale features from images. It is superior in terms of unrestrained image input, accessibility and fast inference speed compared to traditional image processing techniques. Due to the flexibility of CNN for supervised learning problems, it has been deployed quite extensively to solve vision-based automatic damage detection problems. Cha et. al. [24,63] applied a region-based deep learning scheme to detect different damage types and concrete cracks in images of structures. Kong and Li [64] applied CNN to detect crack opening in steel bridge components under repetitive loadings from video clips. Other applications for damage detection using images include [65–68].

2.4.4 Pattern Recognition for Structural Health Monitoring

Pattern recognition is an essential part of SHM in order to evaluate the tremendous amount data collected from various sources and to monitor structural condition to enable assessment in a timely manner. One major challenge noted in the literature is the ability to identify critical features that are associated with structure damage. Traditional ways include wavelet transformation and Fast Fourier Transform (FFT) which are limited because these methods are highly sensitivity to outliers. Data-driven approaches using ML are found to be more robust in dealing with complex

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structure systems and various sources of input signals [69]. According to Worden and Manson [70], there are four levels of application for the data-driven approaches in SHM to: 1) detect if damage exists in the structure; 2) extract the probable location of the damage; 3) estimate the extent of the damage, and 4) infer information regarding the safety of structure. ML classification algorithms are suitable for level 1 e.g., [25,26,71], which adopt supervised ML algorithms for damage detection. While [72] applied an unsupervised, nonlinear principle component learning algorithm analysis together with auto-associative neural network to select features to predict stiffness changes from a synthetic bridge model (level 3). Another example of feature extraction using ML is by Rafiei and Adeli [73] where restricted Boltzmann machine was applied for damage related feature extraction followed with a classification neural network to detect damage in a 38- story reinforced concrete building.

ML algorithms may provide superior performance for these damage-related feature extraction examples compared to traditional physics-based methods given its capability in pattern recognition [73,74].