Cement float64 102 540 | Blast Furnace Slag float64 0 359 | Fly Ash float64 0 200 | Water float64 122 247 | Superplasticizer float64 0 32.2 | Coarse Aggregate float64 801 1.15k | Fine Aggregate float64 594 993 | Age int64 1 365 | Concrete compressive strength float64 2.33 82.6 |
|---|---|---|---|---|---|---|---|---|
540 | 0 | 0 | 162 | 2.5 | 1,040 | 676 | 28 | 79.986111 |
540 | 0 | 0 | 162 | 2.5 | 1,055 | 676 | 28 | 61.887366 |
332.5 | 142.5 | 0 | 228 | 0 | 932 | 594 | 270 | 40.269535 |
332.5 | 142.5 | 0 | 228 | 0 | 932 | 594 | 365 | 41.05278 |
198.6 | 132.4 | 0 | 192 | 0 | 978.4 | 825.5 | 360 | 44.296075 |
266 | 114 | 0 | 228 | 0 | 932 | 670 | 90 | 47.029847 |
380 | 95 | 0 | 228 | 0 | 932 | 594 | 365 | 43.698299 |
380 | 95 | 0 | 228 | 0 | 932 | 594 | 28 | 36.44777 |
266 | 114 | 0 | 228 | 0 | 932 | 670 | 28 | 45.854291 |
475 | 0 | 0 | 228 | 0 | 932 | 594 | 28 | 39.28979 |
198.6 | 132.4 | 0 | 192 | 0 | 978.4 | 825.5 | 90 | 38.074244 |
198.6 | 132.4 | 0 | 192 | 0 | 978.4 | 825.5 | 28 | 28.021684 |
427.5 | 47.5 | 0 | 228 | 0 | 932 | 594 | 270 | 43.01296 |
190 | 190 | 0 | 228 | 0 | 932 | 670 | 90 | 42.326932 |
304 | 76 | 0 | 228 | 0 | 932 | 670 | 28 | 47.813782 |
380 | 0 | 0 | 228 | 0 | 932 | 670 | 90 | 52.90832 |
139.6 | 209.4 | 0 | 192 | 0 | 1,047 | 806.9 | 90 | 39.358048 |
342 | 38 | 0 | 228 | 0 | 932 | 670 | 365 | 56.141962 |
380 | 95 | 0 | 228 | 0 | 932 | 594 | 90 | 40.563252 |
475 | 0 | 0 | 228 | 0 | 932 | 594 | 180 | 42.620648 |
427.5 | 47.5 | 0 | 228 | 0 | 932 | 594 | 180 | 41.836714 |
139.6 | 209.4 | 0 | 192 | 0 | 1,047 | 806.9 | 28 | 28.23749 |
139.6 | 209.4 | 0 | 192 | 0 | 1,047 | 806.9 | 3 | 8.063422 |
139.6 | 209.4 | 0 | 192 | 0 | 1,047 | 806.9 | 180 | 44.207822 |
380 | 0 | 0 | 228 | 0 | 932 | 670 | 365 | 52.516697 |
380 | 0 | 0 | 228 | 0 | 932 | 670 | 270 | 53.300632 |
380 | 95 | 0 | 228 | 0 | 932 | 594 | 270 | 41.151375 |
342 | 38 | 0 | 228 | 0 | 932 | 670 | 180 | 52.124386 |
427.5 | 47.5 | 0 | 228 | 0 | 932 | 594 | 28 | 37.427515 |
475 | 0 | 0 | 228 | 0 | 932 | 594 | 7 | 38.603761 |
304 | 76 | 0 | 228 | 0 | 932 | 670 | 365 | 55.260122 |
266 | 114 | 0 | 228 | 0 | 932 | 670 | 365 | 52.90832 |
198.6 | 132.4 | 0 | 192 | 0 | 978.4 | 825.5 | 180 | 41.719503 |
475 | 0 | 0 | 228 | 0 | 932 | 594 | 270 | 42.13112 |
190 | 190 | 0 | 228 | 0 | 932 | 670 | 365 | 53.692254 |
237.5 | 237.5 | 0 | 228 | 0 | 932 | 594 | 270 | 38.40795 |
237.5 | 237.5 | 0 | 228 | 0 | 932 | 594 | 28 | 30.079769 |
332.5 | 142.5 | 0 | 228 | 0 | 932 | 594 | 90 | 37.721921 |
475 | 0 | 0 | 228 | 0 | 932 | 594 | 90 | 42.229026 |
237.5 | 237.5 | 0 | 228 | 0 | 932 | 594 | 180 | 36.251959 |
342 | 38 | 0 | 228 | 0 | 932 | 670 | 90 | 50.459301 |
427.5 | 47.5 | 0 | 228 | 0 | 932 | 594 | 365 | 43.698299 |
237.5 | 237.5 | 0 | 228 | 0 | 932 | 594 | 365 | 38.995384 |
380 | 0 | 0 | 228 | 0 | 932 | 670 | 180 | 53.104131 |
427.5 | 47.5 | 0 | 228 | 0 | 932 | 594 | 90 | 41.542997 |
427.5 | 47.5 | 0 | 228 | 0 | 932 | 594 | 7 | 35.076402 |
349 | 0 | 0 | 192 | 0 | 1,047 | 806.9 | 3 | 15.049193 |
380 | 95 | 0 | 228 | 0 | 932 | 594 | 180 | 40.759063 |
237.5 | 237.5 | 0 | 228 | 0 | 932 | 594 | 7 | 26.258004 |
380 | 95 | 0 | 228 | 0 | 932 | 594 | 7 | 32.823194 |
332.5 | 142.5 | 0 | 228 | 0 | 932 | 594 | 180 | 39.779318 |
190 | 190 | 0 | 228 | 0 | 932 | 670 | 180 | 46.931942 |
237.5 | 237.5 | 0 | 228 | 0 | 932 | 594 | 90 | 33.116911 |
304 | 76 | 0 | 228 | 0 | 932 | 670 | 90 | 49.185149 |
139.6 | 209.4 | 0 | 192 | 0 | 1,047 | 806.9 | 7 | 14.589312 |
198.6 | 132.4 | 0 | 192 | 0 | 978.4 | 825.5 | 7 | 14.638265 |
475 | 0 | 0 | 228 | 0 | 932 | 594 | 365 | 41.93462 |
198.6 | 132.4 | 0 | 192 | 0 | 978.4 | 825.5 | 3 | 9.13142 |
304 | 76 | 0 | 228 | 0 | 932 | 670 | 180 | 50.948829 |
332.5 | 142.5 | 0 | 228 | 0 | 932 | 594 | 28 | 33.019006 |
304 | 76 | 0 | 228 | 0 | 932 | 670 | 270 | 54.378283 |
266 | 114 | 0 | 228 | 0 | 932 | 670 | 270 | 51.732763 |
310 | 0 | 0 | 192 | 0 | 971 | 850.6 | 3 | 9.866402 |
190 | 190 | 0 | 228 | 0 | 932 | 670 | 270 | 50.655112 |
266 | 114 | 0 | 228 | 0 | 932 | 670 | 180 | 48.695621 |
342 | 38 | 0 | 228 | 0 | 932 | 670 | 270 | 55.064311 |
139.6 | 209.4 | 0 | 192 | 0 | 1,047 | 806.9 | 360 | 44.69804 |
332.5 | 142.5 | 0 | 228 | 0 | 932 | 594 | 7 | 30.275581 |
190 | 190 | 0 | 228 | 0 | 932 | 670 | 28 | 40.856969 |
485 | 0 | 0 | 146 | 0 | 1,120 | 800 | 28 | 71.988189 |
374 | 189.2 | 0 | 170.1 | 10.1 | 926.1 | 756.7 | 3 | 34.397958 |
313.3 | 262.2 | 0 | 175.5 | 8.6 | 1,046.9 | 611.8 | 3 | 28.799413 |
425 | 106.3 | 0 | 153.5 | 16.5 | 852.1 | 887.1 | 3 | 33.398217 |
425 | 106.3 | 0 | 151.4 | 18.6 | 936 | 803.7 | 3 | 36.300911 |
375 | 93.8 | 0 | 126.6 | 23.4 | 852.1 | 992.6 | 3 | 28.999361 |
475 | 118.8 | 0 | 181.1 | 8.9 | 852.1 | 781.5 | 3 | 37.797074 |
469 | 117.2 | 0 | 137.8 | 32.2 | 852.1 | 840.5 | 3 | 40.196451 |
425 | 106.3 | 0 | 153.5 | 16.5 | 852.1 | 887.1 | 3 | 33.398217 |
388.6 | 97.1 | 0 | 157.9 | 12.1 | 852.1 | 925.7 | 3 | 28.096147 |
531.3 | 0 | 0 | 141.8 | 28.2 | 852.1 | 893.7 | 3 | 41.299612 |
425 | 106.3 | 0 | 153.5 | 16.5 | 852.1 | 887.1 | 3 | 33.398217 |
318.8 | 212.5 | 0 | 155.7 | 14.3 | 852.1 | 880.4 | 3 | 25.200348 |
401.8 | 94.7 | 0 | 147.4 | 11.4 | 946.8 | 852.1 | 3 | 41.099664 |
362.6 | 189 | 0 | 164.9 | 11.6 | 944.7 | 755.8 | 3 | 35.301171 |
323.7 | 282.8 | 0 | 183.8 | 10.3 | 942.7 | 659.9 | 3 | 28.296095 |
379.5 | 151.2 | 0 | 153.9 | 15.9 | 1,134.3 | 605 | 3 | 28.599464 |
362.6 | 189 | 0 | 164.9 | 11.6 | 944.7 | 755.8 | 3 | 35.301171 |
286.3 | 200.9 | 0 | 144.7 | 11.2 | 1,004.6 | 803.7 | 3 | 24.400556 |
362.6 | 189 | 0 | 164.9 | 11.6 | 944.7 | 755.8 | 3 | 35.301171 |
439 | 177 | 0 | 186 | 11.1 | 884.9 | 707.9 | 3 | 39.300132 |
389.9 | 189 | 0 | 145.9 | 22 | 944.7 | 755.8 | 3 | 40.596347 |
362.6 | 189 | 0 | 164.9 | 11.6 | 944.7 | 755.8 | 3 | 35.301171 |
337.9 | 189 | 0 | 174.9 | 9.5 | 944.7 | 755.8 | 3 | 24.097186 |
374 | 189.2 | 0 | 170.1 | 10.1 | 926.1 | 756.7 | 7 | 46.201787 |
313.3 | 262.2 | 0 | 175.5 | 8.6 | 1,046.9 | 611.8 | 7 | 42.795775 |
425 | 106.3 | 0 | 153.5 | 16.5 | 852.1 | 887.1 | 7 | 49.201007 |
425 | 106.3 | 0 | 151.4 | 18.6 | 936 | 803.7 | 7 | 46.801631 |
375 | 93.8 | 0 | 126.6 | 23.4 | 852.1 | 992.6 | 7 | 45.698469 |
475 | 118.8 | 0 | 181.1 | 8.9 | 852.1 | 781.5 | 7 | 55.599345 |
469 | 117.2 | 0 | 137.8 | 32.2 | 852.1 | 840.5 | 7 | 54.896079 |
AI for Civil Engineers — Teaching Datasets
Small tabular datasets used in the AI for Civil Engineers course, delivered with Qudelta. They are hosted here so course notebooks can load them directly in Google Colab:
import pandas as pd
DATA_URL = "https://huggingface.co/datasets/qudelta/ai4ce-fall2026/resolve/main"
concrete = pd.read_csv(f"{DATA_URL}/concrete_compressive_strength.csv")
All four datasets come from the UCI Machine Learning Repository and are redistributed under their original CC BY 4.0 licence.
Files
| File | Rows | Columns | Task | Target |
|---|---|---|---|---|
concrete_compressive_strength.csv |
1,030 | 9 | Regression | Concrete compressive strength (MPa) |
energy_efficiency.csv |
768 | 10 | Regression | Heating Load, Cooling Load (kWh/m²) |
steel_plates_faults.csv |
1,941 | 28 | Classification (7 classes) | fault_class |
water-treatment.data |
527 | 39 | Data-quality exploration | — (no label column) |
Concrete Compressive Strength
Mix proportions (kg/m³) and age (days) of 1,030 concrete samples, with measured compressive strength in MPa.
Energy Efficiency
768 simulated residential building shapes described by 8 geometric and glazing parameters, with heating and cooling loads.
Steel Plates Faults
Geometric and luminosity features of 1,941 steel plate surface defects, labelled with one of seven fault types:
Pastry, Z_Scratch, K_Scatch, Stains, Dirtiness, Bumps, Other_Faults.
Water Treatment Plant
Daily sensor and laboratory measurements from an urban wastewater treatment plant: a date column followed by 38 attributes, with ? marking missing values. Provided in the original UCI format (no header row); water-treatment.names documents every column.
Changes from the originals
- Converted to CSV with descriptive column headers.
- Concrete: column names shortened (units removed from the headers).
- Energy Efficiency: columns
X1–X8,Y1,Y2renamed to their descriptive names. - Steel Plates Faults: the seven one-hot fault columns were merged into a single
fault_classcolumn. - Water Treatment Plant: unchanged original files.
Sources and citations
- Concrete Compressive Strength — Yeh, I-C. (1998). UCI Machine Learning Repository. https://doi.org/10.24432/C5PK67
- Energy Efficiency — Tsanas, A. & Xifara, A. (2012). UCI Machine Learning Repository. https://doi.org/10.24432/C51307
- Steel Plates Faults — Buscema, M., Terzi, S. & Tastle, W. (2010). UCI Machine Learning Repository. https://doi.org/10.24432/C5J88N
- Water Treatment Plant — Bejar, J., Cortes, U. & Poch, M. (1993). UCI Machine Learning Repository. https://doi.org/10.24432/C5FS4C
Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).
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