Automated Author ProfileAiram Sausen
Airam Sausen
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 0.3 (sum of 1 dataset Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
"With the objective of investigating methods to estimate the compressive strength of in-service concrete structures, the following section describes the method developed in this research. The approach consists of the combined application of destructive and non-destructive tests on structures built with different strength ranges. This strategy aims to support the construction and validation of mathematical and computational models capable of representing the mechanical behavior of concrete, based on different ages and testing conditions.The first stage of the methodology consisted of defining the project variables for the construction of the structures, including the desired concrete strength ranges, the type of materials, and their specific characteristics. To reflect typical conditions of buildings of this type, commonly used materials were prioritized. The binder was acquired in two batches, with specific masses of 3.03 g/cm\u00b3 and 3.015 g/cm\u00b3, respectively. The fine aggregate used presented a specific mass of 2.608 g/cm\u00b3 and a fineness modulus of 1.76. The coarse aggregate presented a specific mass of 2.921 g/cm\u00b3, a bulk density of 1.661 g/cm\u00b3, a maximum diameter of 19 mm, and 1.30% absorption.Next, concrete mixes were proportioned with distinct strength levels of 10 MPa, 25 MPa, 40 MPa, and 55 MPa for the construction of frame-type structures, named Est.1, Est.2, Est.3, and Est.4, respectively. All mixes achieved a slump of 12 cm with a tolerance of \u00b12 cm, and their water-to-cement ratios were adjusted accordingly.In the second stage of the methodology, the four structures\u2014each consisting of four concrete columns\u2014were subjected to NDTs and DTs at predefined ages of 28, 56, 91, and 182 days, with the objective of creating a comprehensive database for the development and validation of mathematical and computational models aimed at evaluating the compressive strength of concrete. Initially, non-destructive tests (NDTs) \u2014 Rebound Number, Pin Penetration, and Pull-Off \u2014 were performed. Then, destructive tests (DTs) were conducted through core extraction.For each testing age, one column from each of the four structures was used: column 1 for tests at 28 days, column 2 for tests at 56 days, column 3 for tests at 91 days, and column 4 for tests at 182 days.For each test, a minimum of six samples per column were taken in each structure, totaling 24 samples per NDT and DT type at each testing age.The dataset is organized into two processing levels within a single file in .xls format (DATASET KRUG 2025):(1) Experimental Results \u2013 records of non-destructive and destructive tests for each test type and testing age.(2) Import to Matlab \u2013 data organized in columns, formatted for direct import into the Matlab workspace.This resource supports research on modeling and predicting the compressive strength of concrete and serves as an experimental basis for future studies."
Authors
- Lucas Fernando Krug ;
- Paulo Sausen ;
- Airam Sausen ;
- Flávia Izabel Bandeira Krug