Abstract
Machine learning (ML)-aided structural health monitoring (SHM) can rapidly evaluate the safety and integrity of the aging infrastructure following an earthquake. The conventional damage features used in ML-based SHM methodologies face the curse of dimensionality. This paper introduces low dimensional, namely, cumulative absolute velocity (CAV)-based features, to enable the use of ML for rapid damage assessment. A computer experiment is performed to identify the appropriate features and the ML algorithm using data from a simulated single-degree-of-freedom system. A comparative analysis of five ML models (logistic regression (LR), ordinal logistic regression (OLR), artificial neural networks with 10 and 100 neurons (ANN10 and ANN100), and support vector machines (SVM)) is performed. Two test sets were used where Set-1 originated from the same distribution as the training set and Set-2 came from a different distribution. The results showed that the combination of the CAV and the relative CAV with respect to the linear response, i.e., RCAV, performed the best among the different feature combinations. Among the ML models, OLR showed good generalization capabilities when compared to SVM and ANN models. Subsequently, OLR is successfully applied to assess the damage of two numerical multi-degree of freedom (MDOF) models and an instrumented building with CAV and RCAV as features. For the MDOF models, the damage state was identified with accuracy ranging from 84% to 97% and the damage location was identified with accuracy ranging from 93% to 97.5%. The features and the OLR models successfully captured the damage information for the instrumented structure as well. The proposed methodology is capable of ensuring rapid decision-making and improving community resiliency.
Highlights
The past decade has seen unprecedented levels of innovation in data science with major advancements in computing and sensing technologies
Five machine learning (ML) models were considered in the computer experiment, namely, logistic regression (LR), ordinal logistic regression (OLR), two artificial neural networks (ANN), and support vector machine (SVM)
As expected, when the dataset was of a big size and the data were representative of the population, the results were not significantly affected by the choice of the ML algorithm
Summary
The past decade has seen unprecedented levels of innovation in data science with major advancements in computing and sensing technologies. Careful engineering and considerable domain knowledge have been utilized to extract damage-sensitive features from the raw data which were subsequently fed into a suitable ML model Many of these features were based on modal properties of the structure, such as modal frequencies, mode shapes, curvature of mode shapes, modal assurance criteria, and power spectral densities [3,4,5,6,7,8]. The issue of the curse of dimensionality arises, i.e., the size of the training dataset needs to be large for such methodologies This is a challenge for the earthquake damage assessment problem as the data from recorded earthquakes are usually limited in size. The study presented in this paper proposes a novel approach that uses low-dimensional damage features in ML classification models on strong motion data of instrumented structures. ML models are developed to detect and assess the condition of the structure
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