🗾ITmedia AI+ (日本)•Stalecollected in 81m
Obayashi Corporation validates RICOS AI-CAE for wind load prediction
💡See how AI-CAE is replacing traditional CFD to accelerate structural design in the construction industry.
⚡ 30-Second TL;DR
What Changed
Obayashi Corporation is testing RICOS's AI-CAE solution for structural wind load analysis.
Why It Matters
This integration demonstrates the shift toward AI-driven simulation in civil engineering, potentially reducing design cycles for large-scale infrastructure projects.
What To Do Next
Explore how surrogate modeling via AI can replace traditional CFD solvers in your own engineering simulation workflows.
Who should care:Enterprise & Security Teams
Key Points
- •Obayashi Corporation is testing RICOS's AI-CAE solution for structural wind load analysis.
- •The AI model predicts wind impact by processing complex variables like building shape and wind direction.
- •The initiative aims to reduce the time and computational resources required for traditional CAE simulations.
🧠 Deep Insight
Web-grounded analysis with 17 cited sources.
🔑 Enhanced Key Takeaways
- •RICOS's AI-CAE solution, named 'RICOS Lightning,' utilizes a proprietary AI algorithm called 'IsoGCN' specifically designed for engineering simulation data to achieve high-speed and high-accuracy predictions.
- •The IsoGCN algorithm significantly reduces computational requirements, enabling simulation times to be cut from days to minutes, and simplifies the process by eliminating the need for strict meshing, making advanced CAE accessible to non-specialist designers.
- •Obayashi Corporation's validation of RICOS Lightning is part of a broader digital transformation strategy, which includes a ¥1 billion investment in construction technology R&D over five years (starting 2017) and strategic partnerships with other tech companies like Rescale and Join Digital.
- •RICOS Lightning has demonstrated the ability to predict aerodynamic performance analysis in 10-20 minutes, a task that previously required half a day to several days using conventional CAE methods.
- •The AI-CAE solution incorporates fluid, thermal, and structural analysis methods, allowing it to provide reliable results even for entirely new product shapes, which is crucial for innovative architectural designs.
📊 Competitor Analysis▸ Show
| Feature/Product | RICOS Lightning | Autodesk Forma | Orbital Stack (by RWDI & Neural Concept) |
|---|---|---|---|
| Primary Focus | Wind load prediction on buildings, general CAE acceleration for manufacturing | Early-stage site & massing design, microclimate (sun, wind, noise) analysis | Climate-informed design, pedestrian comfort, wind effects on buildings |
| Core Technology | Proprietary AI algorithm 'IsoGCN' optimized for engineering simulation data | Cloud-based AI platform | AI-powered CFD tool, machine learning trained on CFD simulations |
| Speed Improvement | Reduces simulation time from days to minutes (e.g., aerodynamic analysis in 10-20 mins) | Rapid prototyping, instant feedback on environmental factors | Instant wind predictions, reduces analysis time from days to seconds |
| Ease of Use | Eliminates strict meshing, making it accessible to non-specialists | Powerful site analysis, data-driven suggestions, accelerates iterations | Fast, affordable, understandable, provides actionable insights |
| Application Scope | Wind load, aerodynamic performance, thermal, structural analysis for various industries (automotive, heavy industry, electronics, architecture) | Generates optimal building layouts and massings based on environmental data | Evaluates structural integrity, wind effects, sunlight exposure, pedestrian comfort |
| Pricing | Null (not publicly available) | Subscription costs can be high for small firms | Null (not publicly available) |
| Benchmarks | 100x faster than traditional methods, 8% mean error margin for wind analysis (DiGiLAB, similar tech) | Null (general speed-up mentioned) | Null (general speed-up mentioned) |
🛠️ Technical Deep Dive
- Proprietary AI Algorithm (IsoGCN): RICOS Lightning is built upon RICOS's unique AI algorithm called 'IsoGCN,' which is specifically designed for application to engineering simulation data.
- Optimization for 3D Data: IsoGCN is capable of accurately grasping complex three-dimensional geometries, making it suitable for intricate product designs.
- Computational Efficiency: The algorithm significantly reduces the computational load compared to conventional simulation methods, leading to substantial acceleration of engineering analysis.
- Simplified Workflow: A key technical advantage is the elimination of the strict meshing process traditionally required in CAE simulations, which simplifies operation and allows non-specialists to use the tool.
- Extrapolation Capability: IsoGCN integrates fluid analysis, thermal analysis, and structural analysis techniques, enabling it to provide reliable predictions for entirely new product shapes, not just variations of existing ones.
- Underlying AI Models (General Context): While specific details for IsoGCN are proprietary, the broader field of AI-driven wind load prediction often utilizes machine learning models such as Support Vector Machines (SVM), Random Forests, Gradient Boosting Machines (GBM), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs), sometimes in hybrid models combining physics-based CFD simulations with data-driven approaches.
🔮 Future ImplicationsAI analysis grounded in cited sources
AI-CAE solutions like RICOS Lightning will significantly democratize advanced engineering simulations within the construction industry.
By simplifying the simulation process (e.g., no strict meshing) and drastically reducing computation time, these tools become accessible to a wider range of architects and engineers, not just specialized CAE experts.
The integration of AI-CAE will accelerate the early stages of architectural design, leading to more optimized and sustainable building structures.
Faster and more comprehensive wind load predictions enable rapid iteration and data-informed decision-making earlier in the design process, allowing architects to optimize for environmental performance and structural integrity more efficiently.
Obayashi Corporation will likely expand its use of AI-driven solutions across various construction and engineering domains beyond wind load analysis.
Obayashi has a stated commitment to digital transformation, a significant investment in construction tech R&D, and a history of partnering with AI and HPC companies, indicating a strategic push for technological adoption across its operations.
⏳ Timeline
2015-12
RICOS began developing computational science technologies, including simulation, after receiving an award at the University of Tokyo's Entrepreneur Dojo.
2017
Obayashi Group established Silicon Valley Ventures and Laboratory (SVVL) to foster open innovation and engage with emerging technology ecosystems.
2018-04
RICOS solidified its concept of 'simulation base de AI' and commenced full-scale business development.
2021-09
RICOS completed a Series A funding round, securing 299.7 million yen.
2021-11-03
Obayashi Corporation partnered with and invested in Join Digital to bring digital transformation to construction projects in North America and Japan.
2025-03-24
Obayashi Corporation announced the use of GEN-VIR, a construction simulator jointly developed with Toyota, to improve work efficiency and reduce worker burden on construction sites.
2026-04-24
RICOS announced a strategic validation with Toyota Gazoo Racing Development to apply AI-CAE for transforming racing car aerodynamics.
2026-05-19
Obayashi Corporation officially announced the commencement of validating RICOS's AI-CAE solution, RICOS Lightning, for predicting wind loads on buildings.
📎 Sources (17)
Factual claims are grounded in the sources below. Forward-looking analysis is AI-generated interpretation.
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Original source: ITmedia AI+ (日本) ↗