Maria Grohmann, Jens Wöstmann, Stefan Maack, Ernst Niederleithinger
{"title":"Ground-penetrating radar datasets for non-destructive geometry evaluation of concrete step specimens with embedded elements","authors":"Maria Grohmann, Jens Wöstmann, Stefan Maack, Ernst Niederleithinger","doi":"10.1016/j.dib.2026.113103","DOIUrl":null,"url":null,"abstract":"<div><div>The datasets comprise raw data collected using ground-penetrating radar (GPR) in pulse-echo mode on three distinct concrete specimens. The specimens represent fundamental non-destructive testing (NDT) challenges in civil engineering, such as locating built-in elements and evaluating their dimensions to characterize both the internal and external geometry of concrete structures. Data acquisition was performed using two bistatic antennas with centre frequencies of 1.5 GHz and 2.6 GHz. Both antennas were mounted on a scanner system, enabling automated measurements with high positional accuracy and high spatial sampling density. During each measurement, only one antenna was active. This article provides a comprehensive description of the uploaded datasets, the design of the three concrete specimens, the technical specifications of the GPR equipment, and the data acquisition process. The raw GPR data are provided in a universally readable format and consist of the recorded time signals stored at each individual measurement point across the scanned area. The datasets are intended to support comparative studies in signal processing, imaging, and data interpretation, serving as a reference for various real-world NDT scenarios in civil engineering.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"68 ","pages":"Article 113103"},"PeriodicalIF":1.9000,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data in Brief","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352340926006517","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/7/24 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
The datasets comprise raw data collected using ground-penetrating radar (GPR) in pulse-echo mode on three distinct concrete specimens. The specimens represent fundamental non-destructive testing (NDT) challenges in civil engineering, such as locating built-in elements and evaluating their dimensions to characterize both the internal and external geometry of concrete structures. Data acquisition was performed using two bistatic antennas with centre frequencies of 1.5 GHz and 2.6 GHz. Both antennas were mounted on a scanner system, enabling automated measurements with high positional accuracy and high spatial sampling density. During each measurement, only one antenna was active. This article provides a comprehensive description of the uploaded datasets, the design of the three concrete specimens, the technical specifications of the GPR equipment, and the data acquisition process. The raw GPR data are provided in a universally readable format and consist of the recorded time signals stored at each individual measurement point across the scanned area. The datasets are intended to support comparative studies in signal processing, imaging, and data interpretation, serving as a reference for various real-world NDT scenarios in civil engineering.
期刊介绍:
Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.