Intelligent Design for Component Size of Reinforced Concrete Frame Structures Using Diffusion Models

The dimension design of components in reinforced concrete frame structures heavily relies on engineering experience and iterative calculations, leading to significant inefficiencies. Existing intelligent design methods struggle to conduct component size design because it is challenging to accurately and densely represent information such as component layout, dimensions, and design conditions. This study proposes a method for intelligent component size design based on feature space for accurate and dense representation of design information, as well as a diffusion design process constrained by multi-channel masks. Firstly, the method substitutes the feature space for the traditional RGB space to represent component layout, dimensions, and design conditions, thereby enhancing data representation and neural network learning capabilities. Secondly, the study introduces an image-guided diffusion model with multi-channel mask tensors, and the corresponding training method is derived. Experimental results demonstrate that this model exhibits strong feature extraction capabilities and performs well in component dimension design tasks. Lastly, the study discusses the impact of parameters such as multi-channel masks and different dataset construction methods on the final prediction results.