ENTERPRISE AI ANALYSIS

Enterprise Process Flow

Our framework orchestrates a seamless flow from initial Design Models to AI-ready BPS Applications.

Unmatched Accuracy & Robustness

Our framework demonstrates superior robustness and spatial accuracy, particularly for complex geometries, validated across diverse datasets. With full automatic cleansing enabled, we achieved a 100% processing success rate, significantly outperforming conventional methods.

Key performance highlights:

• Average Topological Similarity (nGED): 0.871 (CCR method), indicating high fidelity in structural alignment with ground-truth topologies.
• Energy Model Accuracy: Achieved an R² of 0.946 for heating and 0.987 for cooling loads, with RMSE values as low as 0.50 kWh/m², demonstrating reliable performance-model generation for EnergyPlus.
• Transformation Robustness: Full automatic cleansing boosts robustness from 89% to 100% and spatial accuracy from 71.9% to 81.4%, effectively handling complex B-rep inputs.

Comparative Performance vs. SEFAIRA

Our framework significantly outperforms commercial software like SEFAIRA in analytical speed and robustness for complex designs, as shown in the table below (sample from Table 5, Dataset 3):

Notably, SEFAIRA often fails or shows significantly higher analysis times for complex cases (e.g., Case 15 with GFA deviation of 638%), while our framework maintains high accuracy and efficiency. This study also achieves analysis times of 0.01s per zone and 0.29s for full DM-to-BEM transformation.

Transforming Early Design with AI

By bridging flexible design geometry and graph-centric AI representations, our framework provides an infrastructure layer that amplifies the reach of graph-based and language-enabled intelligence across early design, optimization, and construction informatics. This unlocks a spectrum of advanced enterprise applications:

• Graph-based Performance Prediction & Surrogate Modeling: Utilize GNNs on Knowledge Graph (KG) / Network Graph (NG) outputs to predict thermal, daylighting, or ventilation performance at room and building scales, enabling ultra-fast evaluation in optimization loops.
• Knowledge-augmented LLM Workflows: Implement KG-RAG pipelines for Large Language Models (LLMs) to answer design queries, generate constraint-aware suggestions, and provide natural-language explanations grounded in explicit building graphs, improving interpretability and traceability.
• Automated Multi-objective Optimization & Control: Combine with grey-box models or reinforcement learning agents for performance-driven design exploration directly from conceptual sketches, optimizing for energy, daylight, and ventilation.
• Transfer Learning & Dataset Augmentation: Convert legacy B-rep datasets into unified graph formats, expanding training corpora for data-driven methods and facilitating domain transfer across projects and scales.
• Urban & Multi-method Workflows: Enable neighborhood-scale analytics (UBEM) and seamless coupling between mesh-based (CFD, RADIANCE) and topology-based (AFN, EnergyPlus) simulations for integrated, multi-physics studies.