Current rebar inspection practices in construction projects are manual in nature; hence, they are time-consuming. Rebar drawings are technical; therefore, the accuracy of inspection outcomes depends on the inspector's (supervisor or engineer) experience. In contrast, with the emergence of the fourth industrial revolution, the construction industry has also adopted a few technological solutions for rebar progress monitoring. However, most of the available solutions are detecting qualitative aspects, and quantitative aspects are not much covered. Moreover, available studies have focused on specific rebar monitoring parameters and have not given a complete solution. This study aims to develop an automated smart rebar evaluation model (SREM) giving a complete solution for evaluating on-site rebar quality (rebar spacing, rebar diameter) and quantity (number of rebars, rebar length). The devised system will be able to interpret the 3D point cloud model generated via photogrammetry for rebar quality and quantity parameters easily accessible by project stakeholders. The SREM has the capability to evaluate on-site rebar with the help of images with an accuracy of more than 99 for rebar length and the number of rebars, 97 for rebar spacing, and an accuracy of more than 90 for rebar diameter. The SREM offers an economical, effective, and efficient solution with minimum human involvement. It provides site safety, especially for high-rise buildings, remote progress monitoring to far projects, and minimizes CO2 emission by controlling unnecessary site visits. Most importantly, it is an IoT-supported model. © 2024 Elsevier Ltd