Bridge Monitoring Data Compression Error Statistics

5.5 KB of simulation results evaluating a novel domain-specific data compression algorithm for bridge structural health monitoring. The dataset, authored by Ming Chen and last updated in April 2026, contains error statistics for sparse data after a supplementation process. The described method achieved a 75% compression ratio, exceeding 92% with synergistic processing, while retaining 95% data fidelity.

Use Cases

• Benchmarking data compression algorithms based on the reported 75-92% compression ratios and 95% fidelity rate.
• Analyzing error patterns in time-series data after sparse data supplementation, as mentioned in the description.
• Training or validating models for bridge deterioration prediction using extracted dynamic structural characteristics.
• Assessing trade-offs between storage efficiency and engineering precision in infrastructure monitoring systems.

Strengths

• Specific performance metrics are provided, including a 75% base compression ratio and a 95% feature retention accuracy.
• The dataset is small (5.5 KB), ensuring fast download and easy initial inspection.
• It is shared under a permissive CC-BY-4.0 license, allowing for broad reuse.

Limitations

• The dataset scale is tiny (5.5 KB), suggesting it contains summary statistics rather than raw monitoring data.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for certain analyses.

Provenance

Source

Ming Chen, via figshare.

Collection Method

Likely generated from simulation analyses described in the associated research.

Freshness

Last updated 2026-04-15 17:59:39; freshness should be verified.