Structural engineering improvement

Australia -Accelerating Structural Engineering and Construction Workflows (2025)

Speed up structural and geotechnical engineering processes. By integrating AI and IT solutions, we aim to streamline tasks—from exchanging data between different software platforms to automating defect detection and model optimization.

Pain Points:

Pain Points:

The client relies on multiple specialized tools like ETABS, RAM Concept, and PLAXIS for structural and geotechnical analysis.
Each program has its own data formats and workflows, forcing engineers to spend long hours on manual data entry and validation. In addition, post-tensioning (PT) design often entails repetitive tasks to extract tendon information from RAM models, while defect photo categorization can stall field reporting.

Finally, the lack of a tailored Large Language Model (LLM) makes handling large volumes of documentation cumbersome and prone to human error.
These inefficiencies add up—delaying schedules, increasing project costs, and creating potential quality risks.

How We Solve It:

1-ETABS-RAM Concept Interoperability

We develop a middleware or plugin that translates outputs from ETABS into formats compatible with RAM Concept (and vice versa).
This approach eliminates repeated manual transfers and reduces common errors. Our AI algorithms also detect and correct data inconsistencies on the fly, ensuring smoother back-and-forth between structural design phases.

2-PLAXIS 2D/3D Modeling Automation

By leveraging PLAXIS’s Python API, we script repetitive steps like setting up boundary conditions and material properties.
This automation not only saves time but also guarantees consistent setups across multiple geotechnical scenarios. We enhance this with ML-based soil parameter optimization, leading to quicker convergence and fewer user interventions during the analysis process.

3-Generation of PT Chainage Drapes from RAM Model

Our custom tool pulls tendon geometry and stress data directly from RAM Concept, generating an accurate chainage drape layout in minutes.
We add an intuitive interface for viewing, editing, and optimizing tendon profiles—helping engineers balance safety, cost, and performance more effectively.

4-Automated Defect Photo Categorization

We harness AI-driven computer vision (using models like YOLO or Faster R-CNN) to sort and label thousands of site images.
The system highlights cracks, spalling, or other structural anomalies in seconds, offering consistent categorization and easy reporting.
This drastically reduces the time field personnel spend sifting through endless photos for defect tracking.

5-In House Large Language Model (LLM)

To handle the vast array of project documents—ranging from contracts to design references—we propose a secure, on-premise LLM.
Built on open-source frameworks such as GPT or Llama 2, this custom model can summarize, analyze, and answer queries about engineering documents in real time, all while keeping sensitive data safely within the organization.

Conclusion and Benefits for you Business

Conclusion and Benefits for you Business

This integrated approach frees engineers from tedious data handling, encourages consistency in both design and analysis tasks, and speeds up defect reporting.

Clients gain a unified workflow that cuts down on manual labor, fosters more accurate designs, and produces clearer insights for decision-making.

Ultimately, these solutions can reduce rework by up to 50%, shrink project timelines, and lower overall costs—providing a strong competitive edge in the fast-paced construction sector.

For more information on the technologies used, please follow the link

AI innovations and Open Source initiatives we use

• Computer Vision & Photogrammetry: We integrate OpenCV for real-time image analysis, alongside photogrammetry engines such as OpenMVG and OpenMVS to convert video frames into accurate 3D models.

• Neural Network Architectures: For object detection and segmentation—critical in defect tracking and MEP modeling—we employ libraries like YOLO (for rapid object detection) and Detectron2 (for advanced instance segmentation).

• 3D Point Clouds & BIM: We rely on Open3D for point cloud processing and IFCOpenShell to seamlessly handle BIM formats. These tools help us align as-built data with design models and identify discrepancies in near-real time.

• Large Language Models: When developing an in-house LLM for document analysis, we often build on open source projects like Llama 2 or Hugging Face Transformers. This keeps data secure while enabling domain-specific fine-tuning for engineering workflows.

• Automation & DevOps: To ensure scalable deployment, we use container orchestration (e.g., Docker, Kubernetes) and continuous integration pipelines for quick testing and rollout. This speeds up collaboration between teams and ensures that new AI features reach production smoothly.

Let’s make your workflow smarter

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