Computational BIM PDF

Official (Open) 1 WHAT IS COMPUTATIONAL BIM? Computational BIM refers to the innovative problem-solving approach where users define algorithms to have automated generation and manipulation of building-related information for better work effic...

Official (Open) 1 WHAT IS COMPUTATIONAL BIM? Computational BIM refers to the innovative problem-solving approach where users define algorithms to have automated generation and manipulation of building-related information for better work efficiency, effectiveness and productivity gains.  Official (Open) OPTIMIZING CONSTRUCTION PROCESSES USING COMPUTATIONAL BIM  Official (Open) PROFILE 3 Experienced Lecturer with a demonstrated and rich history of working in the built environment industry with the passion for MEP designs and Computational BIM. Skilled in BIM software such as Revit, Computational BIM, MEP Services Design and Sustainable Design.. Qualifications Saw Tun Senior Lecturer ▪ Master of Science (Building Performance and Sustainability), NUS School of MAE ▪ Bachelor of Engineering (Mechanical), NTU Singapore Polytechnic ▪ Diploma in Intelligent Building Technology, Temasek Polytechnic Industry Experiences ▪ Senior Lecturer, School of Mechanical & Aeronautical Engineering, Singapore Polytechnic (2018-present) ▪ Executive Engineer (M&E), Surbana Jurong Consultants Pte Ltd (2015-2018) ▪ Assistant Engineer/Engineer (M&E), Surbana International Consultants Pte Ltd (2013-2015) ▪ Engineering Assistant (M&E), CPG Consultants Pte Ltd (2008-2012)  Official (Open) SP MAE’s CET Specialist Dip in Air-Conditioning & Energy Sustainability Courses Relevant 4 to Industry Computational BIM using Revit Dynamo Short Course Essential & Advanced Revit MEP Modelling Short Courses Project Collaboration using Autodesk Navisworks Short Course  Official (Open) 5 AGENDA ▪ Introduction ▪ Benefits of Computational BIM ▪ Computational BIM Applications ▪ Tools for Computational BIM ▪ Challenges ▪ Case Study 1: ETTV Analysis ▪ Future Trends of Applications  Official (Open) INTRODUCTION 6 ▪ AEC Industry historically slow in digital transformation. ▪ Building Information Modelling (BIM) has become crucial for construction project management and collaboration. ▪ Computational BIM offers design optimization, analysis, and automation of repetitive tasks. ▪ Exploiting the computational BIM capabilities becomes a topic of major interest in the AEC industry. ▪ Integration of computational methods into BIM, enabling automation, analysis, and optimization of building design and construction processes.  Official (Open) BENEFITS OF COMPUTATIONAL BIM 7 Automation: Automating repetitive design tasks and streamlining workflows. Efficiency: Reduces time spent on manual tasks through automation. Optimization: Optimises design from multiple options rapidly. Interoperability: Seamlessly integrates with multiple software platforms for multidisciplinary collaboration.  Official (Open) COMPUTATIONAL BIM APPLICATIONS 8 ▪ Structural Optimization: Optimize material use and building geometry, e.g. concrete usage ▪ Energy Performance Analysis: Analyse and improve energy efficiency of buildings, e.g. ETTV analysis ▪ Generative Design: Explore numerous design alternatives through computational algorithms, e.g. optimising carpark space utilisation ▪ Code Compliance Checking: Automatically verify that the design meets codes and regulations, e.g. fire safety check for minimum distances of egress routes  Official (Open) TOOLS FOR COMPUTATIONAL BIM 9 ▪ Dynamo ▪ Revit API ▪ PyRevit ▪ Grasshopper  Official (Open) TOOLS FOR COMPUTATIONAL BIM 10 Dynamo ▪ BIM-VPL Integration: VPL tools like Dynamo automate BIM processes without coding expertise, ideal for architects and engineers. ▪ Ease of Use: Dynamo’s node-based interface is easy for beginners and allows fast tool development within BIM. ▪ Parametric Interaction: Dynamo works seamlessly with Revit’s parametric geometry and external databases. ▪ Advantages: Open-source with a large node library and active user community offering custom nodes. ▪ Disadvantages: Troubleshooting complex projects is difficult, it's slower than traditional programming, and version compatibility issues can cause node failures.  Official (Open) TOOLS FOR COMPUTATIONAL BIM 11 Revit API ▪ Revit API Capabilities: The Revit API allows users to extend Revit’s functionality by creating custom tools and automating repetitive tasks using programming languages like C#, VB.NET, and C++. ▪ Integration: Revit API plug-ins run directly within Revit, enabling more efficient and faster execution compared to Dynamo, which operates on top of Revit. ▪ Flexibility: The API allows for highly customizable user interfaces and the ability to handle over 50 events in real-time. ▪ Advantages: Revit API offers a powerful IDE with advanced debugging capabilities, proper error handling, and faster computing compared to Dynamo, reducing the risk of file crashes. ▪ Disadvantages: Learning the Revit API requires coding skills, making it difficult for non-programmers like architects or engineers. Debugging requires restarting Revit, and logic changes are slower than in visual programming languages like Dynamo.  Official (Open) TOOLS FOR COMPUTATIONAL BIM 12 PyRevit ▪ Overview: PyRevit is an open-source add-on for Autodesk Revit that allows users to automate tasks and extend Revit’s capabilities using Python scripting. ▪ Integration: PyRevit operates directly within Revit, providing a Python-based environment for creating custom tools, automating workflows, and interacting with BIM data. ▪ Flexibility: It supports quick script testing and modification, enabling real-time feedback within Revit. PyRevit is also lightweight and does not require compiling, making it more accessible than other programming-based tools. ▪ Advantages: PyRevit is easier to learn for those familiar with Python, has a robust library of pre-built scripts, and is highly customizable. It allows rapid development and automation without requiring the more complex coding skills needed for the Revit API. ▪ Disadvantages: PyRevit lacks the deep debugging and performance optimizations available in more advanced APIs like the.NET API. It can also be slower in handling large datasets or complex geometry compared to Revit API programming interfaces.  Official (Open) TOOLS FOR COMPUTATIONAL BIM 13 Grasshopper ▪ Overview: Grasshopper is a visual programming tool integrated with Rhino, widely used in architectural and structural design for creating parametric models through node-based scripting. ▪ Integration: Grasshopper works seamlessly with Rhino’s 3D modeling environment, enabling users to manipulate complex geometries, automate design processes, and perform real-time parametric adjustments. ▪ Flexibility: Grasshopper can handle complex geometries and integrates well with other tools for environmental, structural, and form-finding analysis. It is highly customizable and suitable for both architects and engineers. ▪ Advantages: Grasshopper’s intuitive visual interface is ideal for non-programmers, allowing fast iteration on designs without needing to write code. It also has a large ecosystem of plugins like Ladybug (environmental analysis) and Karamba (structural analysis). ▪ Disadvantages: As models and node networks grow more complex, they can become difficult to manage and troubleshoot. Additionally, Grasshopper may struggle with performance when dealing with very large or highly detailed geometries, especially in real-time.  Official (Open) CHALLENGES 14 ▪ Resistance to Change: Many professionals are comfortable with existing manual processes and adding computational design can seem intimidating and unnecessary. What complicates matters is that traditional workflows do not seamlessly align with computational approaches, often requiring workflow redesign. ▪ High Cost of Implementation: Firms need to develop custom scripts, algorithms, or parametric models tailored to their specific projects, which requires hiring or training staff with specialized programming skills. This custom development adds both cost and complexity. ▪ Advanced Technical Skills: Computational BIM requires knowledge of coding, scripting, and algorithmic thinking. It demands a hybrid skill set, blending traditional AEC expertise with computational and coding skills. Professionals need to be proficient in programming as well as the designs and construction processes. ▪ Fragmented Industry Adoption: Not all firms are adopting C-BIM at the same pace, leading to inconsistencies. Many contractors, subcontractors, and clients may not be prepared to work with computational models, hindering large-scale implementation. ▪ Varied Standards: The lack of standardized workflows or guidelines creates confusion or inefficiency in the industry. Different stakeholders may have varying expectations for how computational design is applied or integrated with traditional BIM.  Official (Open) CASE STUDY 1: ETTV ANALYSIS 15 Problem Statement ▪ In Southeast Asia, air-conditioning systems in buildings consume over 50% of the total energy used in the building ▪ The Envelop Thermal Transfer Value (ETTV) is a crucial energy performance indicator. ▪ ETTV is a prerequisite under the BCA Green Mark Scheme, requiring stakeholders to meet minimum standards for Green Mark certification. ▪ The building envelope, comprising walls, windows, shading devices, and thermal insulation materials, significantly influences the energy consumption of the air- conditioning system. ▪ The project aims to leverage Computational BIM approach for ETTV analysis. ▪ The project aims to enhance productivity and efficiency of the design process.  Official (Open) CASE STUDY 1: ETTV ANALYSIS 16 Development of Solution ▪ The ETTV program was developed using the Revit API and C#. ▪ Identified workflow and requirements per BCA excel submission template. ▪ Able to analyze up to eight facade orientations. ▪ Retrieve geometry and thermal data (U-value, SHGC) of the building envelope elements from the model. ▪ Calculate SC2 values for window shading devices (horizontal, vertical, egg-crate) following BCA’s ETTV guide. ▪ Generate the excel results according to BCA guide.  Official (Open) CASE STUDY 1: ETTV ANALYSIS 17 Development of Solution ETTV Summary Page  Official (Open) CASE STUDY 1: ETTV ANALYSIS 18 Development of Solution Window/Wall Summary Page  Official (Open) CASE STUDY 1: ETTV ANALYSIS 19 Development of Solution Wall Assembly Page  Official (Open) CASE STUDY 1: ETTV ANALYSIS 20 Development of Solution South Façade Summary Page  Official (Open) CASE STUDY 1: ETTV ANALYSIS 21 Development of Solution SC Calculation Page for Egg Crate Window  Official (Open) CASE STUDY 1: ETTV ANALYSIS 22 Challenges The effectiveness of ETTV analysis relies on the quality of the Revit model, which must meet all necessary prerequisites to enable accurate ETTV calculations using the Computational BIM approach ▪ All enclosed spaces in the model must have Room elements. ▪ Mode of ventilation for all rooms must be specified including non-AC rooms before the analysis. ▪ Avoid using room separation lines in the model. ▪ Exterior walls and windows will be constructed with specified thermal properties to be incorporated into the analysis.  Official (Open) CASE STUDY 1: ETTV ANALYSIS 23 Limitations Certain limitations of the Revit software were encountered during the development process. ▪ Revit's curtain wall elements lack thermal properties, and the program cannot perform the analysis if exterior walls are created using curtain walls. ▪ Similarly, Revit’s column elements lack thermal properties, which prevents the program from analysing heat gain through exterior columns that are part of the building’s external façade.  Official (Open) FUTURE TRENDS OF APPLICATIONS 24 ▪ Digital Twins and IoT Integration: Digital twins are digital replicas of physical assets, and they are becoming more prevalent in conjunction with BIM models. With the integration of IoT sensors, BIM models can evolve into dynamic digital twins that enable real-time data collection on information such as building performance, energy consumption, maintenance data, and more. The adoption of digital twins will continue to grow, especially in smart cities and large infrastructure projects, where the integration of real-time data from IoT devices can lead to more efficient, sustainable, and resilient building operations. ▪ Cloud-Based BIM and Collaborative Platforms: Cloud computing is revolutionizing the industry by enabling real-time collaboration across multidisciplinary teams, regardless of geographical location. As cloud infrastructure improves, we will see more sophisticated, integrated platforms that support real-time simulations, construction monitoring, and data-driven decision-making. Interoperability between different BIM tools and platforms will also improve, streamlining workflows. ▪ Integration with Artificial Intelligence and Machine Learning: AI and ML are increasingly being integrated into BIM workflows to enhance automation, predictive analysis, and decision-making processes. Expect further advancements in AI-powered design optimization, error detection, code compliance checking and generative design.  Official (Open) PROFILE 25 Experienced BIM API Developer with over ten years of expertise in optimizing plugin creation, data extraction, and workflow automation. Skilled in developing BIM and Dynamo automation solutions to boost productivity, collaborating effectively with engineers and project managers to drive data-driven decisions. Proficient in Revit data extraction and integration with MongoDB, SQL, and Excel for digital workflows. Known for leveraging API, Dynamo, and computational methods to streamline Than Naing Oo processes, applying engineering and programming knowledge to deliver innovative solutions. Qualifications ▪ Bachelor of Engineering (Mechanical), Yangon Technological University Industry Experiences ▪ (BIM API and Computational Developer) at Tiong Seng Contractors (October 2014 – Present) ▪ AutoCAD Drafter (Technical Department) at Choon Hin Stainless Steel (Pte) Ltd (April 2013 — October 2014 )  Official (Open) CASE STUDY 2: 26 COMPREHENSIVE PROJECT ANALYSIS USING DYNAMO: LENGTH, AREA, AND ELEMENT COUNT CALCULATION  Official (Open) 27 CASE STUDY 2: COMPREHENSIVE PROJECT ANALYSIS USING DYNAMO: LENGTH, AREA, AND ELEMENT COUNT CALCULATION  Official (Open) 28 CASE STUDY 2: COMPREHENSIVE PROJECT ANALYSIS USING DYNAMO: LENGTH, AREA, AND ELEMENT COUNT CALCULATION  Official (Open) CASE STUDY 2: 29 COMPREHENSIVE PROJECT ANALYSIS USING DYNAMO: LENGTH, AREA, AND ELEMENT COUNT CALCULATION  Official (Open) CASE STUDY 2: 30 COMPREHENSIVE PROJECT ANALYSIS USING DYNAMO: LENGTH, AREA, AND ELEMENT COUNT CALCULATION Project Overview This case study explores the use of a Dynamo script in Revit to automate the analysis of key building components by calculating their total length, area, and element count. The focus is on structural elements such as walls, floors, and foundations, providing essential metrics to support accurate material estimation, efficient resource allocation, and enhanced project planning. ▪ Data Extraction: The Dynamo script pulls data from the Revit model for the selected categories (walls, floors, and foundations). ▪ Automated Calculations: Using computational logic, the script calculates total length, area, and element count for each category, eliminating the need for manual computations. ▪ Visualization: The data is represented through pie charts, offering a clear visual breakdown of each metric.  Official (Open) 31 CASE STUDY 3: COMPUTATIONAL DESIGN OF CURVED STRUCTURAL FRAME  Official (Open) 32 CASE STUDY 3: COMPUTATIONAL DESIGN OF CURVED STRUCTURAL FRAME Project Overview A Dynamo script was created to model a curved structural frame in Revit. Using computational design, the script defines geometry and parameters, enabling flexible adjustments to efficiently explore and refine structural options. ▪ Geometry Creation: The script generates precise curvature and grid alignment. ▪ Parameter Control: Adjustable settings within the Dynamo script allow easy modifications to curvature and spacing. ▪ Visualization: Built directly in Revit, providing a clear view of the structural form.  Official (Open) THANK YOU