Motivation of Using Machine Learning in Structural Engineering Field

For a long time, the structural engineering community has been developing models for real- world structures by conducting physics-based simulations and constructing prototypes to investigate uncertainties using laboratory tests. The traditional research path involves interpreting

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results from the models and/or tests and combining observations (empirical and/or analytical) to develop guidelines, standards, or codes of practice for structural design, construction and retrofit. The approaches involved range from statistical curve fitting to physics-based analytics using computer simulations. Classic examples include the steel moment connection and steel shea wall system development by Qian and Astaneh-Asl [39,40] and a structural wall and coupling beam development by Wallace [41]. This common research path usually includes considerable engineering judgement and commonly requires some level of consensus to become standard practice. Limitations of this approach are apparent, such as the lack sufficient model complexity to accurately represent the dataset which leads to empirical approximations and, sometimes, cumbersome rules and step functions where small changes in design variables lead to undesirable outcomes. In addition, these approaches often include relatively crude factors (e.g., load and capacity-reduction) to account for uncertainties in the confidence intervals based on incomplete information.

Due to the rapid improvement in computational resources (speed, memory, storage, visualization), current mechanics-based analyses, such as finite element analysis using OpenSees [42] and Perform3D [43], are capable of simulating complicated structure systems from relatively simple nonlinear behavior subjected to loadings from multiple hazard types (earthquakes, hurricanes) at various load intensities [44,45]; these analyses tend to generate a lot of structural response simulation data. A study by Burton et. al. [46], where building seismic performance was assessed for a set of archetypical structural models representing existing wood frame buildings in Los Angeles using four different retrofitting schemes with thousands of Nonlinear Response History Analyses (NL-RHA). In addition, evaluation of structural safety has expanded from collapse prevention of single buildings to more diverse and comprehensive studies that involve

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assessment of a spectrum of damage states for a range of structure system types, which then enables multi-variable risk assessment studies using tools such as HAZUS [11,12,47]. Guan et. al. [48] performed intensive Incremental Dynamic Analysis (IDA) to assess the seismic performance of an innovative self-centering steel moment frame and to assess economic risk using the FEMA P-58 procedure [49]. In addition to evaluating individual structures, the risk assessment could also be applied across the regional geo-spatial dimension by analyzing structural responses at the portfolio scale level. Regional loss assessment is demonstrated by DeBock et. al. in [14] and Sun et. al. [28], where a set of concrete moment frame buildings are subjected to an actual earthquake scenario at a number of sites distributed over an urban region. It is shown that the current structural research in hazard mitigation is moving towards to more sophisticated hazard scenarios that generate extreme volumes of data, which are not suitable for some of the traditional approaches that are less capable or less efficient at extracting information from the multiple dimensional data space.

With the continuing growth of experimental data accumulated in the structural engineering field and the use of more advanced technologies to capture additional data and data types from experiments (i.e., digital image technique to capture stress distribution on concrete slender wall [50]), traditional data interpolation techniques are insufficient to explore and identify data patterns for a broad range of structural parameters among a large set of experiment events. A recent database developed by Abdullah and Wallace [51] collects detailed, organized, and parameterized information from more than 1000 reinforced concrete wall tests available in the literature, which is the largest of its kind. Traditional statistical tools are not ideal to examine data trends in such a large dataset. On the other hand, modern ML techniques have advantages, such as reduced influence of outliers, expanded dimensional parameter spaces (higher model complexity), and

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Another emerging trend is the development and expansion of SHM over the last several decades to the point where rapid assessment based on measured data can provide valuable decision variables. The concept of SHM is to remotely monitor the status (e.g., level of damage) within a structure using advanced sensing technologies based on data collected during an event. The process involves data cleaning, feature selection, and statistical model development for damage detection, all of which can be achieved using modern ML algorithms. As early as 2006, Farrar and Worden discussed some of the challenges facing by SHM, such as over-prediction of damage, which is an application of statistical pattern recognition. They noted that key challenges include structural damage is often localized whereas monitoring is often accomplished using global measurements, where it is difficult to identify what has influenced the minor changes in the system responses (lack of uniqueness, in space and time), especially given the relatively sparse data measurements that were common at the time. With more computation power and with state-of- the-art ML models, these challenges are being overcome. For example, the use of kernels to extract key features from both space and time domain and develop a discriminant model for damage detection by Santos et. al. [26], incorporation of state-of-the-art deep learning CNN models to make use of image data [52], the application of random forest, an advanced decision tree algorithm, for assessing post-earthquake structural safety of buildings [8].

The National Hazards Engineering Research Infrastructure (NHERI) is a platform that provides a network of research laboratories located at universities around the country that conducts experiments and collects experimental data in various forms related to hazards such as images of observed physical damage (photos, video) and measured data from a wide-range of sensor types from tests conducted in that involve water systems, energy and communication systems,

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residential , commercial, and industrial buildings, other infrastructure (dams, bridges, roads, tunnels). This broad range of applications provides a tremendous opportunity for the structural engineering community to search for data-driven approaches to help address some most challenging problems in our field.

The preceding discussion provided a general overview of use of modern ML algorithms, and the opportunities to expand the use of structural response history data obtained from sensors, images to identify structural and non-structural damage, and of ML models. Four critical aspects associated with these challenges are discussed in the following sections.