In this paper, a nonlinear regression method called a support vector regression (SVR) is presented to establish the relationship between engineering ground motion parameters and macroseismic intensity (MSI). Sixteen ground motion parameters, including peak ground acceleration (PGA), peak ground velocity (PGV), Arias intensity, Housner intensity, acceleration spectrum intensity, velocity spectrum intensity, and others, are considered as candidates for feature selection to generate optimal SVR models. The datasets with both useable strong ground motion records and corresponding investigated MSIs in the Sichuan–Yunnan region, China, are all collected, and these 125 pairs of datasets are used for selecting features and comparing regression results. Nine ground motion parameters are selected as the most relevant features: PGA is the first fundamental one and PGV is the fifth relevant feature. Based on performance measures on the testing dataset, the best SVR model is given when the number of features is one all the way up to nine. According to predicted accuracy, SVR models with Gaussian kernel give much better MSI prediction than linear kernel SVR models and linear regression models. In particular, the Gaussian kernel SVR of PGA gives much higher MSI prediction accuracy than the linear regression model of PGV and PGA. The proposed SVR models are valid for MSI values from VI to IX, and they can be used for rapid mapping damage potential and reporting seismic intensity for this high-seismic-activity region.

Maximum drift ratio (MDR), one of the engineering demand parameters (EDPs), provides fundamental physical value for predicting building damage. Existing machine learning based prediction models mainly rely on numerical simulation data or structural experiments and are not appropriate for prediction of seismic response of real structures. The New Earthquake Data (NDE1.0) is the most comprehensive publicly available dataset of actual structural seismic response observations. Currently the prediction models using NDE1.0 are mainly based on linear or log-linear regression. In this study, based on the NDE1.0 flatfile, we develop a full-feature support vector regression (SVR) based MDR prediction model (SVR-MDR), treating all the available 41 characteristic parameters including structural information as input feature. To improve the model’s efficiency and practical applicability, we also establish a reduced-feature SVR model (RSVR-MDR) by selecting 10 fundamental parameters based on SHapley Additive exPlanations (SHAP) values and the accessibility of features. Our results demonstrate that SVR-MDR model outperform other machine learning models such as kernel ridge regression and decision tree models, and SVR-MDR and RSVR-MDR models outperform conventional loglinear regression and multinomial models, because SVR can map the complex nonlinear function of multiple variables and consider the available information of buildings especially the fundamental frequency. The proposed RSVR-MDR model have promising potential application for post-event seismic damage assessment and post-event emergency response in near real time.

The ground motion characteristics are essential for understanding the structural seismic response. In this paper, the time-frequency analytical method is used to analyze the time-frequency energy distribution of ground motion, and its effect on the dynamic response of nonlinear structure is studied and discussed. The time-frequency energy distribution of ground motion is obtained by the matching pursuit decomposition algorithm, which not only effectively reflects the energy distribution of different frequency components in time, but also reflects the main frequency components existing near the peak ground acceleration occurrence time. A series of artificial ground motions with the same peak ground acceleration, Fourier amplitude spectrum, and duration are generated and chosen as the earthquake input of the structural response. By analyzing the response of the elasto-perfectly-plastic structure excited by artificial seismic waves, it can be found that the time-frequency energy distribution has a great influence on the structural ductility. Especially if there are even multiple frequency components in the same ground motion phrase, the plastic deformation of the elasto-perfectly-plastic structure will continuously accumulate in a certain direction, resulting in a large nonlinear displacement. This paper reveals that the time-frequency energy distribution of a strong ground motion has a vital influence on the structural response.