Non-destructive Estimation of Concrete Compressive Strength: Databases

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Dataset information

Country of origin
Updated
2025.10.29 00:00
Created
2025.04.07
Available languages
English
Keywords
concrete, non-destructive test, rebound hammer, upv, sonreb, in-situ
Quality scoring

Dataset description

Repository Description This repository presents an extensive collection of published studies correlating non-destructive test (NDT) results with concrete compressive strength. The included NDT methods are: Ultrasonic Pulse Velocity (UPV) Rebound Hammer (RH) SonReb (combined UPV and RH) The dataset comprises 16,531 test results from 115 published studies, distributed as follows: UPV: 6,103 tests from 90 studies RH: 10,428 tests from 87 studies SonReb: 3,299 tests from 53 studies In each database, the final response variable is the concrete compressive strength, normalized to a 150×300 mm reference cylinder. Database Structure Each database contains approximately equal proportions of numerical and categorical variables: UPV: 20 input variables RH: 18 input variables SonReb: 25 input variables These variables cover material age, location, composition, NDT parameters, and testing procedures. Of the total dataset, 1,328 UPV tests, 3,042 RH tests, and 544 SonReb tests were conducted on in-situ structures, while the remainder were performed on laboratory-cast specimens. The datasets span material ages from the 1930s to freshly cast specimens, excluding tests on concrete less than one day old. Numerical data from visualized results in publications were extracted using an open-source computer vision-assisted software for high precision. Project and Documentation These databases were compiled as part of the EU-funded ReCreate project. The ReCreate project researches the process of reusing precast concrete elements through four real-life deconstruction and reuse pilots in Europe. The project aims to assess and improve the technical feasibility of a circular life cycle for structural precast elements across the entire value chain from disassembly, quality control, design for reuse and reconstruction. The derivation and application of these datasets in an empirical and machine learning modelling framework is described in detail in the open-access journal article written by the authors at: https://doi.org/10.1016/j.ndteint.2025.103549 For detailed descriptions of nomenclature, abbreviations, and assumptions, refer to the Database Guide PDF included in this repository.
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2019
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