This repository presents the STEEL-3dPointClouds, a large-scale database of deformed steel beam-column members obtained using high-fidelity physics-based numerical simulations. These simulations trace the inelastic deformations of hot-rolled wide-flange steel beam-columns under different loading protocols covering a range of responses, starting with no strength loss and up to at least 60% loss of load-bearing capacity for each considered steel member. Each of the ∼323k samples in the database is a unique point extracted from the hysteretic response of the loaded member and consists of the deformed member shape – represented as a 3D point cloud – along with the corresponding reserve capacity, characteristic stress and strain fields, and macroscopic responses such as axial shortening and base end moment. This database was generated as part of a broader research program aimed at quantifying the residual life and reuse potential of structural steel members. It stands out for its use of 3D point clouds which directly provide comprehensive geometric information especially compared to 2D images. The data can be used to map deformed shapes of beam-column members to the response quantities of interest thus facilitating the development of automated inspection methodologies and computer vision tools. Furthermore, the database can be used to characterize beam-column nonlinear deformation characteristics and as well as define geometric tolerances for component reuse, amongst other applications. The primary audience for this project includes researchers in structural engineering, mechanical engineering, machine learning, and computer vision. Additionally, the database can be of interest to professionals involved in infrastructure inspection and maintenance. The data offers a valuable resource for those working to develop automated, quantitative methods for assessing structural health and predicting remaining lifespan of steel components. However, given the comprehensive response data that is directly available or can be readily extracted from the database, the STEEL-3dPointClouds database can also be leveraged for development and benchmarking of other computer vision and deep learning methodologies that are not necessarily aimed at structural inspections.