Previous discussions cover a wide range of ML applications at four major fronts of strucutral engineering community:
1. To predict structural responses and damage for stochastic excitation, such as seismic and wind loading, or stochastic structural characteristics;
2. To interpret experimental data where the test setting and scenarios are complex such that physics-based models either perform poorly or are limited to a particular configuration or problem type (versatility);
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4. To recognize patterns from SHM data to inform structural system status.
In this section, we intend to critically discuss three domain challenges that we are facd in utilizing ML in structural engineering, data source, model interpretability and extrapolation fidelity.
2.5.1 Data Source
One big contributor of the success of ML is the access to more data. Although the amount of data required to achieve ideal performance for ML models depends on the problem scenario and goal, it is essential to have sufficient data that the sampled group could represent the true distribution. Traditional data from structural engineering is often limited in its quantity and diversity, e.g., NL-RHA structural responses from a particular building model, cyclic behavior of a specific structural component. In these cases, it is not ideal to employ ML to capture the entire physical phenomenon behind finite element simulation or laboratory testing. On the other hand, there are ML applications that utilize more diverse structural response data across spatial domain such as [27], which, however; does not cover a diverse range of structural dissimilarities. The challenge is to collect a subset of data from the true data distribution that is representative in diversity and quantity such that ML algorithms are able to discover the underlying patterns of structural system within the domain objective. The shortage of data can be compensated for by incorporating domain knowledge within the ML model such that additional domain knowledge is introduced to reduce the complexity of the model space and consequently reduce necessary amount of data to fit the ML model. In addition, some statistical procedures may also aid through data augmentation, e.g., Monte Carlo simulation, to generate synthetic data. Future efforts are likely to examine both options, i.e., collecting more diverse real and synthetic data and incorporating domain knowledge for model design.
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However, the impact of outlier data might be more critical to certain ML algorithms, suc as logistic regression, due to their low robustness to noise. There are anomaly detection procedures for ML, such as using clustering techniques (DBSCAN [75], K-means [76]) and Z-score [77]; however, it also is necessary to include structural engineering domain-specific outlier detection procedures in ML applications. Ideally, the data-driven anomaly detection should be merged into physics-based outlier detection. In other words, the universal data filtering procedure of ML models should be modified with structural engineering domain knowledge, which has not been demonstrated in most recent research works.
2.5.2 Model Interpretability
One of the most significant challenges associated with a ML model is interpreting the physics meaning of the model parameters. A commonly held view is that a ML model is a black box, i.e., there are no physical bounds that can be derived from a data-driven model. One option to address this issue is to deploy ML evaluation procedures (e.g., k-fold cross validation) over traditional statistical learning models to enable some degree of interpretability [28]. In addition, some recent efforts have demonstrated the potential of introducing domain knowledge into ML algorithms by incorporating a physics-based loss function, e.g., to embed hard conditions with a langrage multiplier into the loss function [78,79]. This approach provides a means to explain some of the ML model by adding a physics-based law into the objective function (Equation (2-1) and (2-2)). In [80], a spectrum of approaches are discussed that leverage the wealth of domain knowledge to improve performance data-driven models by adding domain knowledge, such as including theory guided model design or learning and regularization. However, combining ML and physics-based models remains a challenging problem that will be explored with future research.
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2.5.3 Model Extrapolation Ability
The ability to extrapolate the results of a ML model is another challenge and is also referred to as overfitting. Standard ML procedures involve data-based extrapolation by using training/testing split, k-fold cross validation, bagging and bootstrap, as well as other means. In addition, random forest provides a robust method to avoid overfitting given its stochastic procedure in generating trees in both feature and data space. For structural engineering, it is important to identity and apply domain knowledge to help avoid overfitting issues. The combination of a data-driven procedure and domain knowledge, similar to dealing with data sparsity, may provide a powerful combination. Although extrapolation has been extensively studied in the ML field, it could be more critical in applications of the strucutral engineering field given the high sensitivity of some strucutral responses. For example, to simulate a regular 40-story tall building, thousands of parameters are needed for configuration such as strucutral component geometry dimensions, material properties, external loadings, and construction conditions such that complex ML is almost unavoidable. Consequently, complex ML models require large amounts of data, better noise filtering processes, and careful model tuning to reduce the effects of overfitting.