Machine learning-aided scenario-based seismic drift measurement for RC moment frames using visual features of surface damage

Abstract

This paper presents a novel computer vision-based methodology for assessment of the seismic damage in reinforced concrete moment frames using visual characteristics of surface damage following an earthquake. An extensive collected database comprising 974 images associated with 256 cyclic-loaded damaged beam-column joints, providing a set of cracking and crushing progression with increasing the evolution of damage level, is collected and used for the development and validation of the methodology. Employing image processing techniques, the characteristics of the surface damage, including the cracking length and crushing areas, are measured and used in a scenario-based assessment for the seismic peak drift prediction. Based on the availability of the structural information, four scenarios are proposed using input parameters among cracking length, crushing areas, concrete compressive force, and the aspect ratio of the joint. The machine learning regression method is employed for developing nonlinear regression models for each scenario. The proposed models measure the seismic peak drift ratio during the earthquake excitation based on the visual damage features at the external surface of the components. Finally, the seismic peak drift ratio obtained by the proposed methodology of this paper can be used as an input engineering demand parameter in the existing seismic loss measurement frameworks. An example specimen at various drift ratios is also presented as a case study to evaluate the predicted versus actual experimental drift ratio.