This paper aims at developing a model to measure and evaluate the performance and productivity of the construction of steel structure projects (SSPs). Practitioners and experts comprising a statistically representative sample were invited to participate in a structured questionnaire survey to achieve the objective. The questionnaire consisted of 17 factors that were classified under the following four primary categories, with terms such as feasibility study stage, planning stage, design, and engineering stage, and construction stage. Artificial neural networks (ANNs) were used for designing a model on MATLAB for measuring and evaluating the projects’ performance of the Construction of SSP based on the 14 factors that affect the steel structure process. The results suggest that the proposed ANN model for SSP can produce measures and evaluate the projects’ performance quickly and accurately when actual data is available for model training. The user can enter the values of main factors that affect their projects’ performance to produce an accurate output of the evaluation for the projects’ performance and productivity. The construction industry can use the findings of this paper as a basis for improving the projects’ performance of the construction for SSP.