碳纤维增强复合材料耐湿热性能评价方法数据集

该数据集主要面向碳纤维复合材料水吸收、碳纤维复合材料长期力学性能退化、碳纤维复合材料热机械性能劣化与川藏高原环境下碳纤维拉索服役性能数据库研究，基于万能试验机、动态热机械分析仪、恒温水槽、天平测试采集获得碳纤维复合材料水吸收数据主要收集了复合材料在不同老化环境下的扩散系数与饱和吸水率数据；碳纤维复合材料长期力学性能退化数据主要收集了复合材料在不同老化环境下的拉伸性能、剪切性能与弯曲性能的退化数据；碳纤维复合材料热机械性能劣化数据主要收集了复合材料在不同老化环境下的玻璃化转变温度的退化数据；川藏高原环境下碳纤维拉索服役性能数据库主要收集了拉索用碳纤维复合材料在高原典型环境（如湿热）与应力耦合环境下的长期服役性能数据，主要包括碳纤维复合材料的拉伸性能、剪切性能、弯曲性能及玻璃化转变温度保留率等关键性能指标的退化数据。数据量为27.1MB。

This dataset is dedicated to research on water absorption of carbon fiber reinforced polymer (CFRP) composites, long-term mechanical property degradation of CFRP composites, thermomechanical performance deterioration of CFRP composites, and the service performance database of carbon fiber stay cables under the Sichuan-Tibet Plateau environment. The data were collected via universal testing machines, dynamic mechanical analyzers (DMA), constant-temperature water tanks, and precision balances. The water absorption data of CFRP composites mainly document the diffusion coefficient and saturated water absorption of composites under different aging environments. The long-term mechanical property degradation data mainly collect the degradation data of tensile, shear and flexural properties of CFRP composites under various aging environments. The thermomechanical performance deterioration data mainly gather the degradation data of glass transition temperature (Tg) of CFRP composites under different aging environments. The service performance database of carbon fiber stay cables under the Sichuan-Tibet Plateau environment mainly collects long-term service performance data of CFRP composites for stay cables under the coupled environment of typical plateau environments (e.g., humid-heat conditions) and stress, including degradation data of key performance indicators such as tensile, shear, flexural properties and glass transition temperature retention rate. The total size of the dataset is 27.1 MB.

搜集汇总

背景与挑战

背景概述

该数据集聚焦于碳纤维增强复合材料的耐湿热性能评价，包含水吸收、长期力学性能退化、热机械性能劣化及川藏高原服役性能四类数据，涉及扩散系数、拉伸、剪切、弯曲性能和玻璃化转变温度等关键指标。数据源自'川藏铁路桥梁用大吨位碳纤维复合材料拉索'研究项目，总数据量约为27.1MB。