The "Window Safety Legislation Impact" function is a conceptual demonstration in Speckle Automate for the AEC industry. Designed to assess compliance with upcoming window safety legislation, it automatically identifies windows in building models that do not meet the new sill height requirements. This tool aids in proactive adjustments to designs, ensuring safety and compliance with the latest standards.

This demo illustrates the integration of Speckle with WillowTwin, a design partner specializing in digital twin technology. The focus is on leveraging digital twins for post-completion building insights and preconstruction data quality checking, enhancing the lifecycle management of AEC projects. Functionality Digital Twin Integration: Demonstrates how WillowTwin's digital twin technology integrates with Speckle, bridging the gap between preconstruction planning and post-completion analysis. Post-Completion Insights: Showcases how data from completed buildings can be analyzed and fed back into Speckle, providing valuable insights for future projects. Preconstruction Data Checking: Highlights the capability to use digital twins for verifying the quality of project data before construction begins, ensuring accuracy and efficiency. Seamless Data Flow: Ensures a seamless flow of data between Speckle and WillowTwin, facilitating continuous lifecycle management of AEC projects. Benefits Enhanced Project Lifecycle Management: Provides a comprehensive view of the project lifecycle, from planning to post-completion. Data-Driven Insights: Offers data-driven insights for both preconstruction and post-completion phases, aiding in decision-making and future project planning. Improved Data Quality: Enhances the reliability of project data, leading to more accurate and efficient construction processes. Integrated Workflow: Creates an integrated workflow that combines the strengths of Speckle's open data platform with WillowTwin's digital twin technology. This demonstration highlights the potential of Speckle in integrating cutting-edge digital twin technology from WillowTwin, showcasing a dynamic approach to managing and analyzing AEC projects throughout their entire lifecycle.

This demo showcases how Speckle's open data platform can integrate external APIs, specifically highlighting the integration with Gaiups RealtimeLCA. It demonstrates the capability to enrich source models with data from third-party tools, providing valuable insights back into the Speckle ecosystem. Functionality External API Integration: Highlights the process of integrating Gaiups RealtimeLCA's API with Speckle, facilitating data exchange and enrichment. Data Re-Enrichment: Demonstrates how results and pointers from Gaiups RealtimeLCA can be fed back into the source models in Speckle, enhancing the original data set. Real-time Feedback Loop: Creates a real-time feedback loop where data from Speckle is analyzed by Gaiups RealtimeLCA, and the results are then reflected back in Speckle. Enhanced Model Information: Showcases how enriched data can provide additional layers of information, like carbon footprint analysis, directly within the Speckle environment. Benefits Informed Decision Making: Facilitates more informed design and construction decisions based on environmental impact assessments. Streamlined Processes: Simplifies and streamlines the process of obtaining and utilizing third-party data analyses within Speckle. Enhanced Collaboration: Promotes collaboration by making detailed, enriched data available to all stakeholders in a unified platform. Sustainable Design Enhancement: Encourages sustainable design practices by integrating environmental impact data directly into the modeling process. This demonstration highlights Speckle's versatility in integrating with external APIs, showcasing a practical example of enhancing project models with valuable third-party data, thus driving innovation and collaboration in the AEC industry.

This is a proposed demo function for Speckle Automate, designed to validate AEC models against established data standards. It aligns models with the BuildingSMART Data Dictionary (bsDD) and Information Delivery Specification (IDS) standards, ensuring that they meet industry-wide data requirements and specifications. Functionality Model Correlation: The function compares elements within a model to corresponding entries in the bsDD and checks for compliance with IDS specifications. IDS XML Upload: Users can upload IDS XML files, which define the required standards and specifications for the project. bsDD Sheets Integration: The tool correlates model data with the bsDD sheets, ensuring that all terminology, classifications, and properties align with international standards. Compliance Reporting: After analysis, the function generates a report highlighting areas of compliance and identifying where the model diverges from the standards. User Interface: A simple and intuitive interface allows users to manage and upload their IDS XMLs and bsDD sheets easily. Benefits Enhanced Compliance: Ensures models adhere to international standards, improving quality and interoperability. Streamlined Process: Simplifies the often complex task of standard compliance, saving time and reducing errors. Increased Transparency: Provides clear insights into how models align with standards, fostering stakeholder trust. This demo would demonstrate Speckle's capabilities in enhancing data integrity and compliance in the AEC industry. It aligns with Speckle's mission of fostering open, collaborative digital delivery through innovative tools and processes. Note that this function is a conceptual demonstration, not a fully operational feature yet.

This is an example function for Speckle Automate, demonstrating how models can interact and share data spatially within the AEC industry. This function enables detailed models to inherit metadata from zonal models, facilitating efficient data management and coordination between strategic project planning and detailed design phases. Operation Zonal Model Creation: Zonal models are developed with specific metadata representing strategic aspects such as fire strategy, tenancy planning, or programming. Detailed Model Development: Detailed models are created independently, focusing on the granular aspects of the project. Spatial Join Process: When elements in the detailed model fall within the zones defined in the strategic model, they automatically inherit relevant metadata from the zonal model. This process is based on spatial relationships. Data Enrichment: As a result, the detailed model is enriched with strategic-level data, providing a comprehensive view that aligns with the broader project objectives. Applications Programming: Assigning functions and uses to different spaces based on strategic planning. Fire Strategy: Incorporating safety measures and protocols into detailed designs based on predefined zones. Tenancy Planning: Aligning detailed architectural designs with tenant requirements and zoning. Benefits Efficiency: Streamlines the integration of strategic and detailed planning phases. Consistency: Ensures uniform application of strategic decisions across detailed models. Flexibility: Allows detailed models to evolve independently while still aligning with strategic objectives. This demo would serve as a powerful example of Speckle's capabilities in fostering collaboration, connectivity, and automation in the AEC industry, aligning with Speckle's mission to innovate through open, collaborative digital delivery. Please note that this is a conceptual demonstration and not a fully developed feature.

Produce stylized renders of your 3D Views and Cameras in your Speckle Models using Blender's rendering engine Cycles

This demonstration showcases the capabilities of Speckle Automate, tailored specifically for the Architecture, Engineering, and Construction (AEC) industry. It provides a simplified scenario for demonstrating clash detection between static reference and dynamic project models. Importantly, this example is conceptual and not intended for production use. It is an educational model to illustrate how Speckle can facilitate automated clash detection in complex AEC projects. Models Involved in the Demonstration: Static Reference Model: Serves as the baseline for comparisons. Dynamic Model: Represents the evolving elements of a project. Users can select specific element types from these models to analyze for potential geometric conflicts. When the dynamic model is updated, the function then automatically compares these selected elements against the static reference. Key Features Highlighted: Element Type Selection: Enables users to specify element categories for clash tests. Automated Detection: Demonstrates real-time detection capabilities as the dynamic model evolves. Clash Reporting: Generates simple reports indicating the elements clashing Flexibility in code Example: An example of integration with Speckle's platform and a custom-built version of PyMesh. Please note: This demonstration is intended solely for educational purposes. It aims to illustrate the principles of automated clash detection using Speckle Automate in a simplified manner and is not equipped to handle the complexity and diversity of real-world AEC projects.

CFD Simulations via OpenFOAM for Pedestrian Wind Comfort.

Create 2d and 3d maps for your Revit project real-world location using OpenStreetMap data.

This Speckle Automate example showcases the automated detection and filtering of sensitive data in AEC projects, using predefined rules to maintain privacy and integrity. Key features include automated scrubbing, customizable rules, real-time processing, and a privacy-focused design. It's a conceptual model, not yet a fully operational tool.

A demonstration function for Speckle Automate to exemplify how mesh density issues in 3D models can be identified and addressed. This conceptual tool is designed for demonstration purposes only and is not intended for production use. It is an educational example to show how Speckle could enhance model performance and health in the AEC industry. This demo function scans 3D models within Speckle for elements with abnormally high mesh density, which can indicate over-design or improper geometry importation. Such issues often lead to decreased model performance. When identifying elements surpassing a preset mesh density threshold, the function flags these for review, providing insights into their complexity and potential impact on overall model efficiency. Key Features for Demonstration: Mesh Density Detection: Illustrates how the function detects and highlights high-mesh-density elements. Customizable Thresholds: Demonstrates users' ability to define what mesh density level is considered problematic. Educational Tool: Provides a visual and interactive way to understand the importance of model optimization. As a demonstration tool, this function aims to illustrate the possibilities within Speckle's platform for maintaining efficient and high-quality digital models.

This function checks the fire rating property on any element having a "Fire Rating" property. It fails if the value is lower than the minimum provided rating which is passed as input.