five

Supporting dataset for the manuscript “Macroscopic mechanical properties based on concrete microstructure”

收藏
科学数据银行2025-12-03 更新2026-04-23 收录
下载链接:
https://www.scidb.cn/detail?dataSetId=5a29d44515c7441cb9adeaf5477c498a
下载链接
链接失效反馈
官方服务:
资源简介:
This dataset supports the findings of the manuscript titled “Macroscopic mechanical properties based on concrete microstructure”. The study establishes a quantitative relationship between the microstructural parameters and the macroscopic compressive strength of concrete through experimental testing and a theoretical model.The dataset comprises:1. Pore structure parameters: The volume fractions of micropores (V_c) and meso-macropores (V_m) for various concrete mixes (e.g., ERPC, SRPC, LHPC, PC series), obtained via liquid nitrogen adsorption and mercury intrusion porosimetry tests.2. Gassing fractions: The amounts of evolved water (W_c), total gas (B_c), CO₂, and SO₂ across characteristic temperature stages (100°C to 1100°C), as well as the amount of unrecognized oxygen and hydrogen gas (G) within the 600°C-1100°C range, measured using thermogravimetric analysis coupled with infrared spectroscopy.3. Mechanical property data: The experimentally measured compressive strength values of concrete cube specimens and the corresponding strength values calculated based on the hexagonal close-packed pore theoretical model.4. Source data for key figures: The underlying data used to generate the stress-strain curves, pore size distribution curves, and relationship curves between compressive strength and micro-parameters (BcG/Wc, Vc/(1-Vm)) presented in the paper.This data forms the foundation for establishing and validating the “microstructure-based theoretical model for concrete compressive strength”. It directly supports the paper's core conclusions regarding the positive correlation with gassing fraction (internal gel property) and the negative correlation with pore volume fraction. The dataset is relevant for research in fields such as micromechanics of concrete materials, durability assessment, and multi-scale modeling.
提供机构:
Xiaokun Sun; Gaowei Yue; Minmin Li; Mengfei Yu
创建时间:
2025-12-03
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作