A Comparative Study Between a Finetuned Conditional Generative Adversarial Network (cGAN) and the A Algorithm for BIM Clash Coordination - Chong, Yu Cheng; Wong, Richard T. K.; Jasser, Muhammed Basheer; Issa, Bayan; Chua, Hui Na (2025 IEEE 8th International Conference on Electrical, Control and Computer Engineering (InECCE), 2025)
Building Information Modelling (BIM) is a widely used technology in the Architecture, Engineering, and Construction (AEC) industry for integrating non-geometrical and geometrical information of physical buildings and assets. However, the current BIM technology poses a challenge when detecting and resolving clashes between models where it requires manual labor, and it is time-consuming. Hence, this project investigates the use of Generative Artificial Intelligence (Gen AI) to automate the BIM clash resolution process and benchmark its performance against the A^{*} with dynamic cost function algorithm. To implement GenAI in BIM clash resolution, we fine-tune conditional Generative Adversarial Network (cGAN) model using a synthetic image dataset of 2, 0 0 0 images. The performance of the cGAN model is compared with a benchmark A^{*} algorithm that is implemented with a dynamic cost function that can set varying cost. A suite of test cases based on real-world BIM scenarios are used to evaluate the performance of the cGAN model and the A^{*} algorithm with dynamic cost. The results demonstrated that the A^{*} algorithm outperforms the cGAN model with 32.91 % similarity to the optimal solution while the cGAN model performs significantly worse than A^{*} algorithm to determine a viable clash-free path, achieving only 4.78 % similarity to the optimal solution, providing unclear and impractical paths within the solution. In contrast, the A^{*} algorithm with dynamic cost function consistently determined a viable clash-free path. Moreover, the A^{*} algorithm achieved the highest similarity of 61.7 % recorded in low obstacle density scenarios at the expense of high computation load.