Estimation of economic seismic loss of steel moment-frame buildings using a machine learning algorithm

In this study, the effect of modeling-related uncertainties on the expected annual losses of modern code-compliant steel moment-frame buildings is analyzed. Probabilistic structural models are initially employed to account for all the critical sources of uncertainty. Then, these structural models are used to develop machine-learning-based prediction models to estimate the expected annual losses and the associated economic contrib-utors; the developed machine-learning-based prediction models exhibit an excellent performance in the pre-diction of the economic seismic losses of steel frame buildings. The effect of structural-modeling-related uncertainties on each loss contributor is also evaluated; the effect of uncertain modeling parameters is observed to be more pronounced on loss contributors such as demolition and structural collapse losses that are controlled primarily by ground motions with a low probability of earthquake occurrence.

Automated summary

This study analyzes the impact of modeling-related uncertainties on the expected annual losses of modern code-compliant steel moment-frame buildings. Probabilistic structural models are used to account for uncertainties and develop machine-learning-based prediction models that show excellent performance in estimating economic seismic losses. The effect of uncertain modeling parameters is observed to be more pronounced on loss contributors controlled by low probability ground motions.