README.md

metadata

license: mit
dataset_info:
  features:
    - name: L
      dtype: float32
    - name: Fx
      dtype: float32
    - name: Fy
      dtype: float32
    - name: xP
      dtype: float32
    - name: Fy2
      dtype: float32
    - name: xQ
      dtype: float32
    - name: Ax
      dtype: float32
    - name: Ay
      dtype: float32
    - name: By
      dtype: float32
    - name: score_minmax
      dtype: float32
    - name: score_euclid
      dtype: float32
    - name: score_balance
      dtype: float32
    - name: winner
      dtype:
        class_label:
          names:
            '0': minmax
            '1': euclid
            '2': balance
  splits:
    - name: original
      num_bytes: 1680
      num_examples: 30
    - name: augmented
      num_bytes: 168000
      num_examples: 3000
  download_size: 201706
  dataset_size: 169680
configs:
  - config_name: default
    data_files:
      - split: original
        path: data/original-*
      - split: augmented
        path: data/augmented-*

Beam Tabular HW1

This dataset was created for CMU 24-679 (AI/ML for Engineers) HW1.
It extends Project 0 (beam statics simulator) into a tabular dataset suitable for ML practice.

Splits

• original: 30 manually specified load cases
• augmented: 3000 synthetic cases generated by physics-based random sampling

Features

• L: beam length [m]
• Fx, Fy: applied force components [N]
• xP: location of primary load [m]
• Fy2, xQ: optional surprise vertical load and its location [m]
• Ax, Ay, By: reaction forces at supports [N]
• score_minmax: max reaction criterion
• score_euclid: Euclidean norm of reactions
• score_balance: balance score between supports
• winner: best objective (0=minmax, 1=euclid, 2=balance)

Augmentation Method

We used Monte Carlo random sampling of beam parameters within realistic ranges, then recomputed support reactions and optimizer scores. This approach ensures every augmented sample obeys statics, while covering a wider design space than the 30 original cases.

License

MIT — released for educational use.

AI Tool Disclosure

ChatGPT (OpenAI, GPT-5) was used for:

• Code support
• Documentation writing

All diagrams, dataset generation, and labeling logic were created and validated by the author.