Sensor Data from a Large-Scale Indoor Rainfall-Triggered Landslide Model Test

A laboratory study instrumented a physical model of a loess-mudstone interface landslide with pore-water pressure and earth pressure sensors under artificial rainfall. The dataset likely contains sensor readings and TLS-derived displacement data from the test, which quantified mudstone strength degradation from 4.90 MPa to 0.82 MPa within 3 hours. The data was authored by Chi Li and last updated on April 30, 2026.

Use Cases

• Modeling pore-water pressure response to rainfall based on the described three-stage evolution pattern.
• Analyzing the correlation between sensor anomalies and TLS displacement precursors for early-warning lead time estimation.
• Studying the water-film-controlled softening mechanism of mudstone based on the quantified strength degradation data.
• Cross-validating pressure-based indicators with spatially continuous displacement fields to characterize progressive destabilization.

Strengths

• Data is derived from a controlled, large-scale indoor physical model test, allowing for precise measurement of cause and effect.
• Includes cross-validation between point sensor data (pressure) and spatially continuous displacement mapping from terrestrial laser scanning.
• Quantifies specific material property changes, such as unconfined compressive strength decreasing from 4.90 MPa to 0.82 MPa within 3 hours.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is very small at 5.5 KB, indicating limited scope or a summary of results rather than raw time-series streams.

Provenance

Source

figshare

Collection Method

Data collected from a large-scale indoor physical model test under artificial rainfall conditions, instrumented with pressure sensors and terrestrial laser scanning.

Time Range

Time range of the physical experiment is not specified.

Freshness

Last updated 2026-04-30 17:34:30; freshness should be verified.

Geography

The study's geographic context is not specified; data is from a laboratory model.