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ml-for-aec

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Computer vision for buildings, image-to-floorplan, generative ML models, performance prediction, structural analysis ML, energy prediction, natural language to design, and point cloud ML for AEC computational design

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# Machine Learning for AEC

Machine learning is reshaping specific domains within Architecture, Engineering, and Construction, though the transformation is uneven. This skill provides a thorough, practitioner-oriented guide to where ML delivers real value in AEC today, the architectures and methods that work, the data challenges that constrain adoption, and practical pipelines for training, deploying, and maintaining ML models in production AEC workflows.

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## 1. ML in AEC: Current State

### 1.1 Where ML Actually Works in AEC Today

ML in AEC is most effective where three conditions converge: (a) sufficient training data exists or can be generated, (b) the task is well-defined with measurable performance metrics, and (c) the cost of errors is manageable or human review is in the loop.

**Proven, deployed applications**:
- Construction progress monitoring (photo comparison to BIM schedule)
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