UPV-Compressive Strength Dataset for Regional Logarithmic Calibration of Volcanic-Aggregate Concrete

Description

This dataset supports the manuscript “Regionally Calibrated Logarithmic UPV-Strength Model for Non-Destructive Assessment of Volcanic-Aggregate Concrete in Existing Structures”. It contains paired ultrasonic pulse velocity (UPV) and compressive-strength records obtained from real inspection campaigns on existing reinforced-concrete structures manufactured with volcanic aggregates. The database includes 210 paired UPV-fc observations, together with the variables required to reproduce the logarithmic calibration procedure, including testing year, construction-period interval, structural element type, aggregate-size interval, ultrasonic P-wave velocity, compressive strength, model-subset identifiers, and validation/control fields where applicable. The data are provided to support reproduction of the statistical analysis, logarithmic UPV-strength fitting, stratification by aggregate size and construction history, graphical outputs, and field-validation checks reported in the associated manuscript. Raw field reports, client-related documentation, exact building-identifying information, laboratory certificates, precise locations, and data associated with protected heritage assets are not included due to confidentiality, professional, and heritage-protection constraints.

Steps to reproduce

1. Download the anonymized dataset and the associated scripts. 2. Open the dataset file and use the records with valid paired UPV and compressive-strength values. 3. Apply the logarithmic model fc = a ln(UPV) + b to the complete dataset and to the refined calibration subsets described in the manuscript. 4. Reproduce the stratified analyses by construction-period interval and aggregate-size interval using the corresponding variables included in the dataset. 5. Use the supplied scripts to regenerate the logarithmic fits, coefficients of determination, graphical outputs, and field-validation error checks reported in the manuscript. 6. Compare the resulting R² values, subset sizes, and mean absolute validation error with those reported in the article.

Institutions

• Universidad Europea de Canarias

  Canary Islands, La Orotava
• Universidad de Granada

  Andalusia, Granada

Categories

Licence