Datasets:

thanglexuan
/

Z24-dataset-processed

license: mit
task_categories:
  - time-series-forecasting
  - tabular-classification
  - other
task_ids:
  - multi-class-classification
language:
  - en
tags:
  - structural-health-monitoring
  - vibration-data
  - bridge-monitoring
  - time-series
  - accelerometer
  - civil-engineering
  - damage-detection
  - kuleuven
  - z24-bridge
pretty_name: Z24 Bridge Structural Health Monitoring Dataset
size_categories:
  - 1K<n<10K
source_datasets:
  - original
paperswithcode_id: z24-bridge
configs:
  - config_name: default
    data_files:
      - split: n/a
        path: '*.npy'
dataset_info:
  features:
    - name: inputs
      dtype:
        shape:
          - 27
          - 6000
        dtype: float64
      description: Vibration time-series data from 27 accelerometer sensors
    - name: labels
      dtype: int64
      description: Damage state labels
  splits:
    - name: full-data
      num_bytes: 2500000000
      num_examples: 1530
  download_size: 2500000000
  dataset_size: 2500000000

Z24 Bridge Dataset - KULEUVEN

Dataset Overview

The Z24 Bridge dataset is a crucial benchmark in Structural Health Monitoring (SHM) research, collected from the Z24 bridge in Switzerland by KULEUVEN (Katholieke Universiteit Leuven). This dataset is widely used in structural health monitoring and damage detection research.

Z24 Bridge Information

The Z24 Bridge was a two-span prestressed concrete bridge built in 1963 in Switzerland with the following specifications:

Total Length: 60m (2 spans × 30m each)
Width: 8.3m
Structure: Prestressed concrete box girder
Bridge Type: Two-span continuous
Construction Year: 1963
Data Collection Period: 1998 (before controlled demolition)

Dataset Structure

Original Raw Data

Shape: (17, 9, 27, 65530)

Dimension meanings:

17: Number of scenarios/test conditions
9: Number of measurement setups/configurations
27: Number of sensors/measurement points
65530: Length of time points in each measurement

After Data Cleaning

Shape: (17, 9, 27, 60000)

Processing steps:

Removed 5530 corrupted or missing data points
Normalized time series length to 60000 samples
Maintained original sensor and setup layout

Final Processed Data (Reshaped)

Shape: (1530, 27, 6000)

Reshaping process:

1530 = 17 × 9 × 10: Total measurement segments
- 17 scenarios
- 9 setups
- 10 segments divided from each long time series
27: Number of sensors (unchanged)
6000 = 60000 ÷ 10: Each segment contains 6000 time samples

Technical Specifications

Measurement Parameters

Sensor type: Accelerometers
Measurement: Vibration response data
Units: m/s² (acceleration)

Data Collection Conditions

Environmental conditions: Controlled and natural conditions
Excitation: Ambient vibration, controlled loading
Damage scenarios: Progressive damage artificially introduced
Reference state: Undamaged baseline measurements

File Structure

Data_Z24_processed/
├── README.md                 # This documentation file (English)
├── README_vi.md             # Vietnamese version
├── inputs.npy               # Input data (1530, 27, 6000)
├── labels.npy              # Labels for damage states
├── data_visualization.ipynb # Jupyter notebook for data exploration
├── pixi.toml               # Environment configuration
└── pixi.lock              # Locked dependencies

File Descriptions

`inputs.npy`

Shape: (1530, 27, 6000)
Type: numpy.float64
Content: Preprocessed vibration time-series data
Sensors (27): Accelerometers placed at various locations on the bridge

`labels.npy`

Shape: Corresponding to sample count (1530,)
Type: Depends on classification task
Content:
- Damage state labels
- Environmental condition indicators
- Or regression targets

Research Applications

This dataset is commonly used for:

1. Damage Detection

Binary classification: damaged vs undamaged
Multi-class: different damage severities
Anomaly detection algorithms

2. Damage Localization

Spatial localization of damage
Sensor fusion techniques
Modal analysis approaches

3. Machine Learning Applications

Deep learning for SHM
Feature extraction methods
Time series classification
Transfer learning studies

4. Signal Processing Research

Modal parameter identification
Frequency domain analysis
Time-frequency analysis
Noise robustness studies

Dataset Characteristics

Advantages

✅ Realistic data: Collected from real-world structure
✅ Controlled damage: Progressive damage scenarios
✅ Multiple sensors: Rich spatial information
✅ Benchmark status: Widely used for comparison
✅ Well-documented: Extensive literature available

Challenges

⚠️ Environmental effects: Temperature, humidity variations
⚠️ Operational variability: Traffic, loading conditions
⚠️ Class imbalance: Limited damage data vs normal conditions
⚠️ Noise levels: Real-world measurement noise
⚠️ Sensor placement: Fixed sensor configuration

Data Processing Pipeline

# Data processing flow from raw to final
Raw Data (17, 9, 27, 65530)
    ↓ [scenarios, setups, sensors, timepoints]
[Data Cleaning & Quality Control]
    ↓
Cleaned Data (17, 9, 27, 60000)
    ↓
[Segmentation & Reshaping]
    ↓
Final Data (1530, 27, 6000)
    ↓ [segments, sensors, timepoints_per_segment]

Preprocessing Steps Performed

Quality Control: Removed corrupted measurements
Length Normalization: Trimmed/padded to 60000 samples
Segmentation: Divided into 6000-sample segments
Reshaping: Flattened scenario × setup × segment dimensions

Usage Instructions

Loading Data

import numpy as np

# Load input data and labels
inputs = np.load('inputs.npy')  # Shape: (1530, 27, 6000)
labels = np.load('labels.npy')  # Shape: (1530,)

print(f"Input shape: {inputs.shape}")
print(f"Labels shape: {labels.shape}")

Basic Analysis

# Statistical analysis
print(f"Input range: [{inputs.min():.4f}, {inputs.max():.4f}]")
print(f"Input mean: {inputs.mean():.4f}")
print(f"Input std: {inputs.std():.4f}")

# Label distribution
unique_labels, counts = np.unique(labels, return_counts=True)
print(f"Unique labels: {unique_labels}")
print(f"Label counts: {counts}")

Visualization

Use the provided data_visualization.ipynb notebook to visualize and explore the dataset.

References

Key Papers

Worden, K., & Manson, G. (2007). The application of machine learning to structural health monitoring. Philosophical Transactions of the Royal Society A, 365(1851), 515-537.
Reynders, E., et al. (2008). Output-only structural health monitoring in changing environmental conditions by means of nonlinear system identification. Structural Health Monitoring, 7(4), 243-268.
Maeck, J., & De Roeck, G. (2003). Dynamic bending and torsion stiffness derivation from modal curvatures and torsion rates. Journal of Sound and Vibration, 265(1), 153-170.

Dataset References

KULEUVEN SHM Database: Structural Health Monitoring research group
Z24 Bridge Benchmark: Community standard in SHM research
Original Studies: Search literature with keywords "Z24 bridge" + "KULEUVEN"

Contact & Support

Institution: KULEUVEN (Katholieke Universiteit Leuven)
Department: Civil Engineering
Research Group: Structural Mechanics
Website: KULEUVEN Civil Engineering

License & Citation

When using this dataset in research, please cite the original papers and acknowledge KULEUVEN university.

@misc{z24_bridge_dataset,
  title={Z24 Bridge Structural Health Monitoring Dataset},
  author={KULEUVEN},
  institution={Katholieke Universiteit Leuven},
  year={1998},
  note={Processed version: 17 scenarios, 9 setups, 27 sensors, reshaped to (1530, 27, 6000)}
}

Data Structure Summary

Dimension	Original	Cleaned	Final	Description
Scenarios	17	17	17×10=170*	Test conditions
Setups	9	9	9×10=90*	Measurement configurations
Sensors	27	27	27	Accelerometer locations
Timepoints	65530	60000	6000	Samples per segment
Total Shape	(17,9,27,65530)	(17,9,27,60000)	(1530,27,6000)	Final format

*Each original time series was divided into 10 segments, hence 1530 = 17×9×10

Dataset processed and prepared for Structural Health Monitoring and Machine Learning research.