## 3 Results
### Phase I: Defining PC On-site Assembly Optimization Principles via BIM-GA Hybrid System
#### 3.2.2 Application of Genetic Algorithm
After entering this crucial information, the system generates an optimal assembly sequence based on the concepts outlined in Section 4. The resulting assembly sequence was stored in an Excel file and simulated using the Revit API. Within the developed Revit API, two critical buttons streamline this process, as shown in Figure 9. First, the assembly sequence planning button organizes the component data according to the generated assembly sequence and conceals them within the model. Secondly, the 'Simulation' button enables users to visualize the sequential installation of these concealed components. This installation sequence prioritizes the consecutive placement of similar components aligned with the column beam-slab progression. However, manual intervention may be required if complete structural integrity is not ensured, considering the interconnections between the PC components. Users, relying on the simulation results from button 2, should review the assembly sequence and finalize the plan through manual adjustments if structural stability is not met. Figure 8: GUI of information input part. (**a**) Calendar interface. (**b**) RC schedule file. (Source: own elaboration). Figure 7: User interface input–output structure. (Source: own elaboration). Phase III: Application and Validation of the Automatic Assembly Sequence Generation System Based on the PC Construction Site

To validate the feasibility of the proposed system, a comprehensive case study was conducted at an actual PC construction site (Figure 10). Table 2 lists the specifics of the chosen site, which served as the primary source for 3D modeling and extracting component details. The 3D modeling stage was concentrated on a designated space covering floors 2-6 (as depicted in Figure 11), which resulted in 208 columns, 340 beams, and 700 slabs. Each floor accommodated 52 columns, 85 beams, and 175 slabs, all of which were obtained using the procedures detailed in Section 3.2.1. Zoning was uniformly allocated across floors, aligned with the site's master plan, and is depicted on the right side of Figure 11.

Figure 10: Case study project. (Source: k construction company). Figure 9: Development of Revit API. (Source: own elaboration). An examination of 208 columns using the extracted data (see Table 3) and the subsequent collection of similar information for beams and slabs were employed in the evaluation conducted using the GA. When applying the GA to generate assembly sequences, standardization was performed for each constraint to prevent prioritization based on differences in the score scales of task complexity, proximity of work radius, and feasibility of component installation. Subsequently, adaptable weights were applied to these constraints and customized to specific on-site conditions for dynamic prioritization. Through extensive Interview FGls, it became evident that the most frequent adjustments in assembly plans revolved around component installation feasibility, leading to the assignment of a weight (\(\gamma\)) of 0.5 to this aspect. Acknowledging the substantial impact of work radius adjacency during disruptions or plan alterations, a weight (\(\beta\)) of 0.3 was designated accordingly. Furthermore, task complexity was attributed a weight (\(\alpha\)) of 0.2, completing the comprehensive weight assignment process. Instead of focusing solely on generating initial assembly sequences, we conducted simulations to envision practical scenarios and generated assembly sequences capable of addressing the interference in actual site situations. In the current scenario, the assembly process progresses along the direction of the arrow, as shown in Figure 12a. A situation arose in which some components on the right side of the 4 th floor remained uninstalled, starting from 23 June 2023, causing interference with the RC process on 24 June for the