3 results about "Building code" patented technology

The disclosure is directed at a method and system for generating dynamic building inspection checklists. The system and method compares tasks for a construction project with building code requirements and then generates a building inspection checklist based on the comparison. The system and method may then determine which tasks within the inspection checklist may result in a more negative impact if the task does not pass inspection and updates the checklist accordingly.

The present invention relates to a system and method for automatically generating a complete and precise bill of materials by learning the structural and logical relationships between design components using a graph neural network (GNN) from heterogeneous design data sources such as 2D CAD drawings, 3D BIM models, and specifications, and by predicting and interpolating the actual quantities to be input at the time of final completion from incomplete initial design data using a generative adversarial interpolation neural network (GAIN). The effects according to the present invention are as follows. First, the present invention precisely interpolates missing attributes using a Generative Adversarial Interpolation Network (GAIN) even in incomplete situations where input data, such as drawings, BIM, and specifications, is heterogeneous and some information (thickness, material, specifications, etc.) is missing. Through this, the reliability and integrity of input data, which forms the basis of quantity calculation, can be dramatically improved even in the initial design stage where data quality is low. Second, active risk management and design quality improvement through learning design change trajectories. Unlike existing methods that simply sum dimensions on drawings or predict only the final result, this invention predicts future change risks in advance by learning the trajectory of design changes through LSTM-based time-series historical analysis. By providing real-time feedback to the designer regarding warnings and alternatives for potential change-inducing factors, it fundamentally prevents unnecessary design changes, maximizes the accuracy of completion predictions, and minimizes the risk of increased construction costs and project delays. Third, it structurally learns the physical support, connectivity, inclusion, and spatial adjacency relationships of a building through a Graph Neural Network (GNN). This enables the automatic generation of a consistent bill of materials that aligns with the structural hierarchy and engineering correlations of the entire building (e.g., the relationship between column load and foundation reinforcement quantity), rather than merely calculating the fragmentary quantities of individual objects. Fourth, by utilizing a Large Language Model (LLM), the generated bill of materials items are double-checked to ensure they logically align with text-based specifications and building codes. By comprehensively evaluating semantic similarity and logical validity, legal and technical risks are minimized by preventing errors in advance where the quantity is correct but the specifications are incorrect (e.g., standard gypsum board vs. fire-resistant gypsum board). Fifth, by applying X-ray Influence Analysis (XAI) technology to the results generated by AI, it explains, using 3D heatmaps and causal text, which object (node) on the drawing is the origin of a specific volume increase. This resolves the 'black box' problem of AI and provides a reliable environment where users can clearly trace the basis of calculations. Sixth, regarding scalability, it enables risk-based estimation by providing multiple scenarios in the form of 'average value + range of variation (confidence interval)' rather than a single predicted value. Furthermore, by linking the calculated quantity data with cost and schedule data, it provides full-cycle project management efficiency that allows for the immediate simulation of the impact of design changes on total construction costs (5D) and construction duration (4D).

This application discloses a BIM model compliance review method and system based on deep learning, belonging to the field of building structural defect detection technology. The method involves: processing the BIM model to obtain a 3D point cloud space and region labels; partitioning and resampling the 3D point cloud space according to the connection relationships of various building components to obtain 3D point cloud models of multiple regions to be detected; using preset component keywords and fused region labels, matching building codes and regulations to the regions to be detected based on the fusion results; detecting the component dimensions and relative distances in the regions to be detected to obtain the structural deviation distribution of each region, thereby identifying design defects in the BIM model. Therefore, by implementing this application, the problem of low review efficiency and insufficient accuracy of building design defect review caused by the difficulty of quickly and accurately matching appropriate and complete building codes and regulations to building components of BIM models in existing technologies can be solved.