Find research datasets worth reusing

opencc-by-4.0Dec 2023View details →

VoroCrack3d: An annotated data set of 3d CT concrete images with synthetic crack structures

VoroCrack3d is an annotated data set of 3d CT images of concrete with synthetic crack structures. Its main purpose is the training and testing of machine learning models for 3d crack segmentation. The data set comprises 1344 images together with their corresponding ground truths. The concrete backgrounds are cropped out sections of size 400x400x400 voxels of CT images of concrete. To this end, several different concrete samples were scanned (normal concrete (NC), high-performance concrete (HPC), ultra-high-performance concrete (UHPC), air pore concrete; without and with reinforcements (straight steel fibers, crimped steel fibers, hooked-end steel fibers, polypropylene fibers, fibers made of glass fiber-reinforced polymer). The original concrete images have a resolution between 2.8 and 106 micrometers. The crack structures are modeled via minimum-weight surfaces in Voronoi diagrams according to the paper [1] C. Jung, C. Redenbach, Crack Modeling via Minimum-Weight Surfaces in 3d Voronoi Diagrams, Journal of Mathematics in Industry, 13, 10 (2023). https://doi.org/10.1186/s13362-023-00138-1. The surfaces are discretized, dilated and superimposed on the concrete backgrounds. The data set offers a high variety regarding concrete types, noise levels and crack widths, shapes, regularity and branching. This makes it suitable for studying the generalizability and robustness of 3d crack segmentation methods. ______________________________________________________________________________________________ The folder 'data' contains seven subfolders, each containing the data generated from a specific concrete type (NC, HPC, air pore concrete, polypropylene fiber-reinforced concrete, steel fiber-reinforced concrete (straight, crimped and hooked-end steel fibers)). Each subfolder again contains four subfolders according to the point process model that was used for generating the 3d Voronoi diagrams. The point processes and Voronoi diagrams are restricted to windows of size 400x150x400.  - 'hc': Hard core point process with 60% volume density and intensity 0.000025 obtained from force-biased sphere packing. - 'matclust': Matérn cluster process with parent intensity 0.0002/50, offspring intensity 50 and cluster radius 20. - 'ppp': Poisson point process with intensity 0.0002. - 'ppp-scaled': Poisson point process with intensity 0.0002 (but inside 200x150x200 window). The resulting Voronoi diagram is stretched in x- and z- direction by a factor of 2. Each of these contains five subfolders: one for the 3d input images, two for the corresponding labels (ground truths; one with and one without pores/fibers), one for the input and label previews (slice z=200 for each of the images) and a misc folder containing the concrete background without crack and, if applicable, the pore/fiber segmentation image. The data itself then contains 48 images: 1a-1d: crack with up to seven branches; fixed crack width (~1 voxel). 2a-2d: crack with up to four branches; fixed crack width (~1 voxel). 3a-3d: crack with up to one branch; fixed crack width (~1 voxel). 4a-4d: crack with no branches; fixed crack width (~1 voxel). 5a-5d: crack with no branches; fixed crack width (~3 voxels). 6a-6d: crack with no branches; fixed crack width (~5 voxels). 7a-7d: crack with no branches; fixed crack width (~7 voxels). 8a-8d: crack with up to seven branches; multiscale crack (bernoulli parameter 0.01); 9a-9d: crack with up to seven branches; multiscale crack (bernoulli parameter 0.02); 10a-10d: crack with up to seven branches; multiscale crack (bernoulli parameter 0.05); 11a-11d: crack with up to seven branches; multiscale crack (bernoulli parameter 0.1); 12a-12d: crack with up to seven branches; multiscale crack (bernoulli parameter 0.2); The names 'a'-'d' indicate level of added noise added to the image: a: None. b: Uniformly on [-sigma,sigma]  c: Uniformly on [-2*sigma,2*sigma]  d: Uniformly on [-4*sigma,4*sigma]  Negative values are mapped to 0.  For inputs of type int, noise values are rounded to the nearest integer. (sigma = standard deviation of voxel greyvalues in image) Note that the grey values in the ground truths correspond to the local crack width. They can be thresholded to obtain binary masks. For more details, we refer to [1].

opencc-by-4.0Mar 2024View details →

Case study of self-compacting, fiber reinforced, lightweight concrete, intended for production of precast elements

This a dataset set to paper entitled: "Case study of self-compacting, fiber reinforced, lightweight concrete, intended for production of precast elements". Dataset is one excel file divided in various sheets containing: <ol> <li>Properties of used aggregates</li> <li>Initial properties of concrete</li> <li>Composition of concrete</li> <li>Concrete with fibres</li> <li>Final concrete</li> </ol>

opencc-by-4.0Jun 2023View details →

Monotonic Flexural Testing of Corroded Reinforced Concrete Beams Database

The database presented here provides a collection of 804 corroded reinforced concrete beams from 54 experimental programs available in the literature. All beam specimens were tested under simply-supported monotonic three/four-point bending conditions and failed in flexure-dominated modes. The database includes 45 independent variables, 11 dependent variables, 649 corroded members, and 155 uncorroded control beams, tested across 14 countries. Of the corroded beams, 11 were naturally corroded, 30 were corroded via long-term environmental exposure (typically in the form of salt spray or fog), and 608 were corroded artificially through the impressed-current method. All observations (individual beam tests) are statistically independent, as each data entry represents one independent test. Highlighted cells indicate non-reported variables. This database was compiled as part of the author's Ph.D. research for the purpose of predictive machine learning. Published articles applying the database can be accessed at: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.dibe.2024.100527" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.dibe.2024.100527</a> Please see the accompanying User's Manual PDF for a complete description of all nomenclature, abbreviations, assumptions, and calculations used to derive the input and response parameters. Please feel free to reach out to the authors if you have any queries or concerns.

opencc-by-4.0Jul 2023View details →

FIB-SEM tomograms of the steel-concrete interface of mortar and concrete specimens

This datasets contains tomograms showing the steel-concrete interface of mortar (files starting with NCI) and concrete (files starting with CI) specimens. They were acquired by a FIB-SEM (focused ion beam-scanning electron microscope). There are five datasets: <ol> <li>NCI-1, with a voxel size of 30 nm</li> <li>NCI-2, with a voxel size of 50 nm</li> <li>NCI-3, with a voxel size of 50 nm, consisting of four microscopy sessions (A, B, C, D)</li> <li>NCI-4, with a voxel size of 30 nm, consisting of four microscopy sessions (A, B, C, D)</li> <li>CI, with a voxel size of 30 nm, consisting of six microscopy sessions (A, B, C, D, E, F)</li> </ol> More information can be found here: (TBD)

opencc-by-4.0Jul 2020View details →

ds-uct-007: Asphalt Concrete: X-Ray micro-CT of an asphalt concrete sample.

Summary: .X-Ray micro-computed tomography (micro-CT) of an asphalt concrete sample, including both raw projection data and the final reconstructions, for one single resolution (voxel size of 7 μm). .The 3D image was generated with an X-Ray micro-CT Scanner version Xradia Versa 510 from Zeiss performed by A Pereira at the UFF micro-CT Facility. .Image data segmented with four different segmentation techniques: DL (Deep Learning), ML (Machine Learning), TH (Thresholding) and WS (Watershed). .For use of these data, please remember to cite the DOI of the Zenodo repository and relevant papers. Details: .Tomo - Voxel size: 7 μm; Sample-source: 31 mm; Sample-detector: 274.25 mm; Optical magnification: 0.4X; Filter: LE#6; Beam energy: 100 kV; Power: 9 W; Exposure time: 4.0 sec; Projections: 1600. Contents: ._info_ds-uct-007.txt .ds-uct-007_asphalt_concrete_07um_16bits.zip .ds-uct-007_asphalt_concrete_07um_1600p.txrm .ds-uct-007_asphalt_concrete_07um_1600p_Drift.txrm .ds-uct-007_asphalt_concrete_07um_1600p_recon.txm .ds-uct-007_asphalt_concrete_07um_DL.zip .ds-uct-007_asphalt_concrete_07um_ML.zip .ds-uct-007_asphalt_concrete_07um_TH32.zip .ds-uct-007_asphalt_concrete_07um_WS.zip

opencc-by-4.0Jan 2024View details →

A Comprehensive Self-Consolidating Concrete Dataset for Advanced Construction Practices

<ul> <li>Size: over 2500 Self-consolidating concrete mixtures from 176 published papers.</li> <li>Material type: Self-consolidating concrete (SCC).</li> <li>Features: <ul> <li>Identification features (5 features): References, number of the mixture, the authors, year of publication, & the mixture code.</li> <li>Powders type, content, & density (76 features): Cement, various supplementary cementitious materials, & other mineral additions.</li> <li>Paste properties (8 features): The total amount of powder used, the water content, the calculated volume of the paste, the water-to-cement ratio, the water-to-binder ratio, the water-to-powder ratio, the volume of water to the volume of powder ratio, & the volume of water to the volume of cement ratio.</li> <li>Aggregate properties (7 features): Content and density of fine and coarse aggregates, the total aggregate, the maximum size of the aggregate, & the fine-to-total-aggregate ratio.</li> <li>Admixture properties (3 features): Quantity of admixture used, its proportion relative to the cement & the total binder content.</li> </ul> </li> <li>Properties: <ul> <li>Fresh properties (13 features): Including filling ability properties, i.e., slump flow spread, V-funnel flow time, & the T50 time; Passing ability properties, i.e., J-Ring flow spread, L-box H1/H2 ratio, & U-box flow; Segregation resistance i.e., sieve segregation index, column segregation index, dynamic segregation index, segregation factor, & sieve GTM stability test. Additionally, the percentage of air content is also documented.</li> <li>Rheological properties (3 features): yield stress & plastic viscosity values alongside with the used rheometer. The instruments employed in these measurements include the ICAR Rheometer, R/S Plus Rheometer, ConTec5 Viscometer, ConTec4SCC, Concrete Shear Box, & TR-CRI Concrete Rheometer.</li> </ul> </li> <li>Application: Essential in choosing Self-Compacting Concrete (SCC) mixtures for different uses, considering the importance of both fresh & rheological properties. Intended to support the creation of sustainable & eco-friendly building materials.</li> </ul>

opencc-by-4.0Jun 2024View details →

Deposition of data for developing deep learning models to assess crack width and self-healing progress in concrete (krkCMd)

This is a deposition of data for developing deep learning models to assess crack width and self-healing progress in concrete [1]. It relates to an experimental study on the autogenous self-healing of high-strength concrete [2]. Concrete specimens were prepared, matured, cracked, and exposed to self-healing. High-resolution scanning of the specimen surface and scale-invariant image processing were performed, multiple grid lines crossing cracks were established, and brightness degree profiles were extracted. Then, manual measurements of the crack widths were obtained by an operator. The dataset comprises 19,098 records of brightness profiles, reference crack width measurements, and benchmark measurements by deep learning and analytic models. The source images, which were stacked and marked with grid lines, are provided. The considerable number of brightness profiles coupled with manual reference measurements make the dataset well suited for developing an image-based deep learning models or analytic algorithms for assessing crack widths in concrete. The deposited data includes: <ul> <li>krkCMd_table.csv: delimited, comma-separated text file containing a dataset of 19,098 crack brightness degree profiles, reference crack width measurements by operator, and benchmark measurements by a deep CNN metasensor and by an analytic edge detector.</li> <li>krkCMd_images.zip: archive containing source image files in folders by test series:  -   stacked images of cracks in subsequent stages of self-healing (.tif files), -   zip archives assigned to image stacks and containing sets of ImageJ data files .roi, -   ImageJ .roi files specifying the locations of grid lines in the images.</li> <li>krkCMd_scripts.zip: archive containing custom scripts supporting image preprocessing and computing benchmark variables.</li> </ul> For details please see the <a href="https://doi.org/10.1038/s41597-025-04485-z">data descriptor [1]</a>. When referring to the data in publications please cite [1]. [1] Jakubowski, J., Tomczak, K. Dataset for developing deep learning models to assess crack width and self-healing progress in concrete. Sci Data 12, 165 (2025). https://doi.org/10.1038/s41597-025-04485-z [2] Jakubowski, J. & Tomczak, K. Deep learning metasensor for crack-width assessment and self-healing evaluation in concrete. Constr. Build. Mater. 422, 135768 (2024). https://doi.org/10.1016/j.conbuildmat.2024.135768

opencc-by-4.0Apr 2021View details →

Data to publication: Fibre optic measurements and model uncertainty quantification for Fe-SMA strengthened concrete structures

This dataset contains the results of an experimental campaign, presented in the publication "Fibre optic measurements and model uncertainty quantification for Fe-SMA strengthened concrete structures". The publication covers fibre optic measurements inside large-scale specimens subjected to external load. The specimens comprised reinforced concrete slabs, strengthened with reinforcement bars made from iron-based shape memory alloy.

opencc-by-4.0Oct 2022View details →

Earthquake Response of Reinforced Concrete Frames with Infill and Active External Confinement: Tests and Dataset

One option to retrofit reinforced concrete (RC) frames is the construction of infill walls. Many studies have shown that infill increases lateral strength and stiffness but tends to reduce drift capacity relative to bare frames. Fewer studies have quantified reductions in drift demand attributed to infills prior to failure. This report summarizes experiments designed to compare drift demands of frames with and without infill. Included data comes from two theses completed at Purdue University which focused on the dynamic response of one-third scale, non-ductile RC frames to uniaxial simulated earthquake ground motions. Tests were conducted on bare frames, frames with masonry infill walls, and frames with timber infill walls. In 11 of 14 test series, active confinement was applied to columns using external post-tensioned reinforcement   This dataset summarizes two experimental programs that studied the dynamic, in-plane response of one-third scale RC frames with full-height infills and active external column confinement. Theses summarized were written by Monical (2021) and Kerby (2022), and included data from 254 in-plane dynamic tests of non-ductile RC frames with various seismic retrofits.

opencc-by-4.0Apr 2024View details →

Supplementary data for the paper: "Resilient crystalline admixture in ultra-high performance self-healing concrete under cyclic freeze-thaw with de-icing salts"

Supplementary data for the paper: "Resilient crystalline admixture in ultra-high performance self-healing concrete under cyclic freeze-thaw with de-icing salts" Open data concerning experimental work. This study investigates the influence of a crystalline admixture (CA) in Ultra-high performance (fibre-reinforced) concrete under freeze-thaw (FT) cycles with de-icing salts with focus on single cracks with a width of around 120 µm, specifically focusing on the ability of the healing products of CA to survive and the ability to re-heal after a healing regime following FT exposure.

opencc-by-4.0Sep 2024View details →

Data for: Drying-Induced Deformation in Concrete: Insights from a 5-Year Study

The experimental dataset from Havlasek, Smilauer, and Nezerka's paper "Drying-Induced Deformation in Concrete: Insights from a 5-Year Study" is available in five archives. These archives contain the data organized according to the specified structure and accompanying Gnuplot source files for easier data visualization. Full experimental description is given in the paper and in the preceding publication entitled "Shrinkage-induced deformations and creep of structural concrete: 1-year measurements and numerical prediction accessible from https://www.sciencedirect.com/science/article/pii/S000888462100051X All Gnulot input files (*.gnu) generate a corresponding *.pdf file with the same base name. These PDFs are included, but they're not listed. Usage: $ gnuplot < plot_name_of_gnuplot_file.gnu The full experimental dataset is reduced to 100 time points using resampling. Initially, geometric progression is applied, but once the time step reaches 30 days, it remains constant. *** FILES STRUCTURE *** |--- ambient_conditions |    | # history of ambient humidity and temperature |    | |    |--- plot_large_beams_ambient.gnu |    | # time "0" corresponds to the onset of drying of the beams, i.e. concrete age 34.063 days |    | |    |--- data |          | # time "0" corresponds to concrete set |          |--- humidity_function.dat |          |--- temperature_function.dat       | |--- creep_shrinkage_prisms |    | # development of creep and shrinkage of beams 100x100x400 mm exposed to drying at the concrete age of 28 days; concrete specimens are drying from two lateral surfaces only |    | |    |--- axial_shrinkage.gnu |    | # total shrinkage |    |--- eccentric_creep.gnu |    | # total creep in axial direction and bending  |    | |    |--- data |          |--- Jtot_ecc_axial_toi.csv |          |--- Jtot_ecc_bending_toi.csv |          |--- shrinkage_toi.csv | |--- curvature_beams | # curvature of concrete beams with various sizes and sealing configurations, time "0" = installation time = onset of drying = concrete age of 34.063 days |    |  |    |--- beam_1.gnu |    | # 100 Top: breadth B = 0.10 m, height H = 0.10 m, span = 2.50 m, length = 2.70 m, drying from the top surface |    |--- beam_2.gnu |    | # 100 Sealed: breadth B = 0.10 m, height H = 0.10 m, span = 2.50 m, length = 2.70 m, sealed surface |    |--- beam_3.gnu |    | # 100 Bottom: breadth B = 0.10 m, height H = 0.10 m, span = 2.50 m, length = 2.70 m, drying from the bottom surface |    |--- beam_4.gnu |    | # 100 Both: breadth B = 0.10 m, height H = 0.10 m, span = 2.50 m, length = 2.70 m, drying from the top and bottom surfaces |    |--- beam_5.gnu |    | # 50 Top: breadth B = 0.10 m, height H = 0.05 m, span = 1.75 m, length = 1.95 m, drying from the top surface |    |--- beam_6.gnu |    | # 200 Top: breadth B = 0.10 m, height H = 0.20 m, span = 3.00 m, length = 3.20 m, drying from the top surface, loading with external weights 2 x 30 kg placed 0.6 m from the support |    |--- beam_7.gnu |    | # 150 Top: breadth B = 0.10 m, height H = 0.15 m, span = 3.00 m, length = 3.20 m, drying from the top surface |    |--- beams_100_ABC.gnu |    | # individual responses of the beams with height 100 and different sealing configurations |    |--- beams_100_mean.gnu |    | # mean responses of the beams with height 100 and different sealing configurations |    |--- beams_sizes_ABC.gnu |    | # individual responses of the beams with different height and drying from the top surface |    |--- beams_sizes_mean.gnu |    | # mean responses of the beams with different height and drying from the top surface |    | |    |--- data |          |--- automatic |          | # measurement with post-mounted linear potentiometer installed at midspan of each specimen |          |    |--- 1_mean_curvature_toi.csv |          |    | # columns: time, mean curvature and standard deviation of curvature; the values are calculated from specimens A, B and C |          |    |--- 1A_curvature_toi.csv |          |    | # columns: time and curvature |          |    |--- 1B_curvature_toi.csv |          |    |--- 1C_curvature_toi.csv |          |    |--- 2_mean_curvature_toi.csv |          |    |--- 2A_curvature_toi.csv |          |    |--- 2B_curvature_toi.csv |          |    |--- 2C_curvature_toi.csv |          |    |--- 3_mean_curvature_toi.csv |          |    |--- 3A_curvature_toi.csv |          |    |--- 3B_curvature_toi.csv |          |    |--- 3C_curvature_toi.csv |          |    |--- 4_mean_curvature_toi.csv |          |    |--- 4A_curvature_toi.csv |          |    |--- 4B_curvature_toi.csv |          |    |--- 4C_curvature_toi.csv |          |    |--- 5_mean_curvature_toi.csv |          |    |--- 5A_curvature_toi.csv |          |    |--- 5B_curvature_toi.csv |          |    |--- 5C_curvature_toi.csv |          |    |--- 6_mean_curvature_toi.csv |          |    |--- 6A_curvature_toi.csv |          |    |--- 6B_curvature_toi.csv |          |    |--- 6C_curvature_toi.csv |          |    |--- 7_mean_curvature_toi.csv |          |    |--- 7A_curvature_toi.csv |          |    |--- 7B_curvature_toi.csv |          |    |--- 7C_curvature_toi.csv |          |--- dial_gauge |          | # measurement using a set of 5 digital indicators with a fixed position from the left support, only the front specimens (A) is measured using this technique |          | # columns: time, curvature and standard devidation of curvature. Weights corresponding to theoretical normalized deflections are applied. |          |    |--- 1a_weighted_toi.csv |          |    |--- 2a_weighted_toi.csv |          |    |--- 3a_weighted_toi.csv |          |    |--- 4a_weighted_toi.csv |          |    |--- 5a_weighted_toi.csv |          |    |--- 6a_weighted_toi.csv |          |    |--- 7a_weighted_toi.csv |          |--- DIC |          | # measurement using digital image correlation, locations of the reference plates are above the support and at quarter-spans |               |--- 1_stat_toi.csv |               | # columns: time, mean curvature and standard deviation of curvature. The values are calculated from specimens A, B and C. The weights are given by the standard deviation of curvature of the individual specimens |               |--- 1a_stat_toi.csv |               | # columns: time, mean curvature and standard deviation of curvature. |               |--- 1b_stat_toi.csv |               |--- 1c_stat_toi.csv |               |--- 2_stat_toi.csv |               |--- 2a_stat_toi.csv |               |--- 2b_stat_toi.csv |               |--- 2c_stat_toi.csv |               |--- 3_stat_toi.csv |               |--- 3a_stat_toi.csv |               |--- 3b_stat_toi.csv |               |--- 3c_stat_toi.csv |               |--- 4_stat_toi.csv |               |--- 4a_stat_toi.csv |               |--- 4b_stat_toi.csv |               |--- 4c_stat_toi.csv |               |--- 5_stat_toi.csv |               |--- 5a_stat_toi.csv |               |--- 5b_stat_toi.csv |               |--- 5c_stat_toi.csv |               |--- 6_stat_toi.csv |               |--- 6a_stat_toi.csv |               |--- 6b_stat_toi.csv |               |--- 6c_stat_toi.csv |               |--- 7_stat_toi.csv |               |--- 7a_stat_toi.csv |               |--- 7b_stat_toi.csv |               |--- 7c_stat_toi.csv | |--- drying_cylinders | # data of moisture loss measured on cylinders drying from the top and bottom surface and with a sealed circumference, onset of drying at concrete age 28 days |    | |    |--- drying_cylinders_average.gnu |    | |    |--- data |    | # columns: duration of drying, average moisture loss [kg/m^3], standard deviation of moisture loss |          |--- dwdV_25_toi.csv |          |--- dwdV_50_toi.csv |          |--- dwdV_100_toi.csv |          |--- dwdV_150_toi.csv |          |--- dwdV_200_toi.csv | |---isotherm | # data for sorption isotherm expressed as a dependence of moisture content [kg/m^3] on relative humidity [-] |    | |    |--- isotherm.gnu |    | experimental data and a least-squares fit with a VanGenuchten expression |    | |    |--- data |    | # columns: relative humidity, average moisture content and standard deviation of moisture content |          |--- isotherm_beam.dat |          | # data measured on specimens cut from a spare concrete beam |          |--- isotherm_cube.dat |          | # data measured on specimens cut from a standard concrete cube

opencc-by-4.0Jun 2007View details →

Souls of concrete in silence

Está pintura feita em uma folha A4 (21cm X 29.7cm) e é coberta com tinta das cores verde, pretas, vermelha, branca na base que se mesclam e geram tonalidades diferentes de verde e do preto. Eu pintei em mês de meu aniversário em 2007, na cidade de Feira de Santana na Bahia, Brasil.

opencc-by-4.0May 2022View details →

FRIBAS database: collection of the main characteristics of 237 reinforced concrete and 71 masonry buildings

FRIBAS-DB is a database that comprises information about 312 buildings (237 reinforced concrete moment resisting frame, 71 unreinforced masonry, 4 mixed type). For each building 37 parameters related to the main building characteristics (age, height, structural typology and main vibrational period), and foundation soil characteristics (e.g. resonance frequency, outcropping geology, seismic soil class, topographic class) are reported. The 312 buildings are located in Basilicata and Friuli Venezia Giulia regions (Southern and North-eastern Italy, respectively) and in different geological and built-environment settings. The FRIBAS-DB allows studying the influence of these parameters for the building dynamic response. In the following the details of each field of the FRIBAS-DB are given. ID_GIS: unique identifier for each building; ID: building identifier containing a number and the province acronym (MT=Matera; PZ=Potenza; VdA=Villa d’Agri; FVG=Friuli Venezia Giulia); Municipality: name of the municipality; COD_COM: municipality code according to the national institute of statistics (ISTAT); LAT, LONG: coordinates of the building in the WGS84-UTM - zone 33N (EPSG:32633); Construction material: RC for Reinforced Concrete Moment Resisting Frame; Masonry for unreinforced masonry buildings; Mixed refers to buildings with both reinforced concrete and load-bearing masonry elements; Soft storey: presence of a soft storey, i.e. a floor that can activate a weak-floor failure mechanism; Building use: Residential, Public, Industrial, Turistic; Age of Construction: < 1919; <1988*; 1919-1945; 1946-1961; 1962-1971; 1972-1975; 1976-1981; 1982-1991; 1992-1996; 1992-2001; 1997-2001; 2002-2008; >2008; (*a more accurate class attribution has not been possible); # Floors: the last floor was included when its estimated volume was comparable with the those of other floors in the building; Presence of basement: all the floors that are partially or totally below ground are considered as basement; Building height from the ground to the top of the roof (m): if the building is located on a slope, the ground floor is considered to be the one at the higher side of the slope; Building height from the basement to the top of the roof (m): total height, including also the basement and the structures present at the top of the building;  Building width B (m): the shorter dimension of a circumscribing polygon; Building length L (m): the longer dimension of a circumscribing polygon; B/L: ratio between building width and building length as a measure for regularity in plan; B/H: ratio between building width and building height (from the ground level to the top of the building); Floor area (m2): the building area calculated based on building footprints (e.g. from openstreetmaps or available national/regional digital maps); Polygon area (m2): the area of the circumscribing polygon; Area ratio: ratio between floor and polygon area Building shape: geometric shape of the building, R (rectangle), S (square), T (T-shape), L (L-shape), C (C-shape), H (H-shape), Tr (trapezoid); Seismic provisions (masonry): Description of any seismic provisions (e.g. additional pillars, ring beam, walls reinforcement, tie-rods) if present; Masonry openings (%): percentage of openings with respect to the building lateral surface; Masonry type: type of load-bearing masonry, including material (e.g. stone, bricks, concrete blocks), layout (regular, irregular) and quality; Slab: rigid or flexible, rigid floors often consist of reinforced concrete and hollow tiles; Roof type: wood (with or without hollow tiles), reinforced concrete (with or without hollow tiles); Additional floors: added afterwards to the building, but not included in the original project; Foundation type: shallow or deep; Position of the building: single block is for buildings that consist of a single unit; in case of multiple blocks (e.g. in the case of attached buildings), we distinguish between internal buildings (attached to two or more buildings) and buildings located at the edge (far end blocks attached only to one building). The presence of seismic joints or staircases is specified in the text field; F1_building (Hz): the experimental fundamental frequencies in two directions of the buildings (longitudinal and transversal) were considered, defined as F1_building (lower value) and as F2_buildings (higher value). The fundamental vibrational frequencies for all buildings have been estimated from single station ambient noise measurements analysed through the Horizontal-to-Vertical Spectral Ratio technique. The noise was recorded at the top of each building, aligning the horizontal axes of the sensor parallel to the two main building axes. Measurements were carried out using two different instruments (Tromino and Lunitek Sentinel GEO). The recording time varies between 10 and 30 minutes. The HVSRs have been estimated by the following procedure: each component was divided into non-overlapping windows of 20 s; each window was detrended, tapered (set to 0.5), padded, Fast Fourier Transformed and smoothed with triangular windows with a width equal to 5% of the central frequency. For each of the 20 s windows, the arithmetic mean of the two horizontal component’s spectra was used to combine E-W and N-S components in the single horizontal (H) spectrum; then the HVSR is computed. Finally, the average HVSR spectrum is obtained, providing also the relative ± 2 standard deviations.  F2_building (Hz): see above F0_ Foundation Soil (Hz): the main resonance frequency obtained from HVSR analysis of single station ambient noise measurements. Measurements were carried out using two different instruments, Tromino or Reftek datalogger equipped with Lennartz 3D-Lite. The recording time varies between 10 and 30 minutes. For data analysis see “F1_building (Hz)” field. For some cases, the HVSR from microzonation studies were used; Geology: the geological classification was inferred from field surveys or the detailed geological maps of microzonation studies at the scale of 1:5000 or 1:10.000, if available. Otherwise the geological map at the scale 1:50.000 was considered. The outcropping geology classes present are: Gravina Calcarenite (coarse-grained carbonate sandstone); Calcari M.te Viggiano (Limestones and carbonate sandstones); Marsicovetere Breccia (massive calcareous breccias); Subappennine clay; Conglomeratic deposits; Sands and sandstones; Clean Gravels; Silts and Clays; Sand; Silty Gravels; Gravels and Sands, with silt and clay; Alluvial deposits; Colluvial deposits; Eluvial and colluvial deposits; Anthropic deposits; Soft soil/rigid soil: soils with Vs > 360 m/s have been considered as rigid soil. This class is composed mainly by outcropping bedrock (limestones, sandstones and breccias of the South-Appennine Units) and by clean coarse gravels of the Upper Friulian Plain. Soils with Vs < 360 m/s have been considered as soft soils. These are loose sediments (silts, clays, sands, gravels and their mixture) of different origin (alluvial, colluvial, eluvial or antropic). Seismic soil class: this soil classification refers to the national building code (NTC2018, § 3.2.2) based on Vs30. Vs profiles have been measured nearby the studied buildings. If deduced by microzonation studies, they are marked by star (*). Topographic class: The topographic class refers to the national building code classification (NTC2018, § 3.2.2).

opencc-by-sa-4.0Sep 2014View details →

Dynamic testing of a four-storey building with reinforced concrete and unreinforced masonry wall: Data set

This paper presents a publically available data set recorded during the shake-table test of a structure with reinforced concrete (RC) and unreinforced masonry (URM) walls. The shake-table test, performed at the TREES laboratory of EUCENTRE (Pavia, Italy), was part of a larger research initiative at EPFL (Lausanne, Switzerland) that addresses the seismic behaviour of mixed RC-URM structures. The half-scale test unit was subjected to several shakings of different intensity levels. The paper presents the geometry of the test unit, the properties of the construction materials, the instrumentation, and outlines the organization of the recorded data. Two sets of data are available: the unprocessed data and a second set of processed data where conventional and optical measurements are synchronised. This second set contains also some derived data, which allows to quickly plot key quantities such as base shear and top displacement. The aim of the paper is to provide all information required by the reader for analysing the test data and using it for validation purposes of numerical and mechanical models. The performance of the test unit is described in a companion paper.

opencc-by-4.0Dec 2016View details →

Supporting materials for manuscript entitled "Investigation of guided wave propagation in pipes fully- and partially-embedded in concrete"

This set contains data in support of some of the figures appearing in an open access manuscript entitled 'Investigation of guided wave propagation in pipes fully- and partially-embedded in concrete,' published at the Journal of the Acoustical Society of America (DOI:10.1121/1.4972118) by the authors. The data set contains the numerical output from finite element (FE) modelling, Semi-analytical FE (SAFE) modelling, simulations using the Disperse software, and experimental measurements of guided wave transmission loss in full-scale laboratory tests. The set contains three separate data files. Details on the specific data is provided within an extra file ('readme' file).

opencc-by-4.0Sep 2010View details →

Fig. 11. Fossiliferous concretion BHI 4788 in Scaphites Of The ''Nodosus Group'' From The Upper Cretaceous (Campanian) Of The Western Interior Of North America

Fig. 11. Fossiliferous concretion BHI 4788 from the Baculites cuneatus Zone of the Pierre Shale, Meade County, South Dakota. The concretion contains an adult specimen of B. cuneatus Cobban, 1962, an adult macroconch of Hoploscaphites brevis (Meek, 1876), and ''Inoceramus'' sagensis Owen, 1852.

opencc-by-4.0Mar 2024View details →

The influence of corrosion processes on the degradation of concrete cover

It is a dataset for the paper that analyzes the impact of the accelerated corrosion process of reinforcing steel on the type of destruction of the concrete cover. In this work, two methods were used to accelerate corrosion. In the first method, in order to initiate corrosion processes, chloride ions were injected into the concrete using the migration method. The moment of initiation of the corrosion process was monitored using an electrochemical method of measuring polarization resistance. In the next step, the corrosion process was accelerated in the electrolysis process. Changes on the sample surface were also monitored by using a camera with which photos were taken at certain intervals. In the second method, the corrosion process of the reinforcing bar was accelerated thanks to the use of the electrolysis process but the source of chloride ions was the electrolyte in the form of a 3% NaCl solution.