###### Abstract  
Providing safe, environmentally conscious, and cost-effective designs is the primary duty of civil engineers. To this end, many different algorithms and methods have been developed in parallel with the progress of digital technologies over the past decades. Techniques such as AI-based Metaheuristic Algorithms (MAs), Reliability Analysis, and Building Information Modelling (BIM) are some of those methods that serve this purpose. The present study focuses on establishing a design optimization methodology by implementing the techniques in the open literature on one software environment to create a robust engineering and architectural workflow. The methodology involves multiple stages such as (i) creating parametric trusses employing Visual Programming (VP) software Dynamo (Version: 3.0.4); (ii) performing a First-order Reliability Method (FORM) analysis which includes a Finite Element Method (FEM) analysis as a Limit State Function (LSF); (iii) employing MAs to achieve optimum design variables under uncertain design constraints; (iv) testing the methodology with various real-word examples and scenarios; (v) creating an optimized model on Robot Structural Analysis 2024 (RSA) software in real time in the purpose of further adjustments. The results demonstrated that creating a design optimization workflow by utilizing a BIM environment can enhance the design process by easing the storing, sharing, and utilizing of design data by different branches capable of performing different complicated tasks successfully.  
structural optimization; reliability-based optimization; AI in design; generative modelling; parametric design; visual programming 2020 IEEE/RSJ International Conference on Intelligent Systems (IEEE)  
underdesign, yielding to structural failure, or overdesign, resulting in waste and increased cost. Establishing this critical optimum value for the safety factor presents a significant challenge since it is dependent upon many uncertain parameters, such as material properties, geometric tolerances/imperfections, loading, and analytical model uncertainties [3]. Furthermore, ignoring the impact of uncertainties might result in disasters, as was observed in the Izmit and Kahramanmaras earthquakes, where many of the structures failed, causing a comparable number of casualties to the Nagasaki nuclear attack [4; 5; 6]. Therefore, the use of advanced techniques, such as RBDO via Metaheuristic Algorithms (MAs), may become necessary to account for uncertainties in the design process [7]. Consequently, the present study aims to find optimized designs under reliability constraints by employing MAs. The First-Order Reliability Method (FORM) has been used for reliability analysis, while Monte Carlo Simulation (MCS) has been utilized to test the correctness of reliability analysis. The initial methodology was designed to employ Robot Structural Analysis (RSA), as studied in [8], for Finite Element Method (FEM) analyses; however, long computational times made it inefficient to use. Thus, the methodology has been updated, and the Python library PyNite has been utilized instead of RSA. Nevertheless, the study also provides important insights about the potential use of RSA and the obstacles behind it. Even though a significant bottleneck of the proposed method is the time required for steps such as the FEM and FORM analysis [9; 10], according to the findings, it is possible to achieve safer as well as cost-efficient designs by the proposed method in a reasonable computational time.