【目的】SH+遗址土+生石灰[SH-(C+CaO)]改性夯筑土对遗址夯土具有较好的兼容性,故其耐久性研究在该改性土的应用与推广中是必不可少的。【方法】对SH-(C+CaO)改性夯筑土进行干湿循环、冻融循环、安定性、耐碱性试验,且不同次数均设定为1...【目的】SH+遗址土+生石灰[SH-(C+CaO)]改性夯筑土对遗址夯土具有较好的兼容性,故其耐久性研究在该改性土的应用与推广中是必不可少的。【方法】对SH-(C+CaO)改性夯筑土进行干湿循环、冻融循环、安定性、耐碱性试验,且不同次数均设定为1、3、5、9、15、25和40次,以系统揭示该改性土的物理、力学、水热性能的变化规律,并利用扫描电镜(scanning electron microscopy,SEM)和X射线衍射(X-ray diffraction,XRD)进行定性分析,以孔隙面积占比建立损伤变量,并分析损伤变量与各性能指标的定量关系。【结果】试样外观在40次循环结束后仍然完整。试样的各性能指标随循环次数的增加逐渐衰减,均在第5次和第25次循环处存在明显拐点;各项性能指标损失率较小,损伤变量与各性能指标之间高度相关,且试样在耐碱性试验条件下的损伤程度最小,在安定性试验下的损伤程度最大。循环结束后的SEM和XRD试验结果表明:干湿循环试验下的试样因颗粒团聚产生破坏,长石类和方解石的相对含量降低;耐碱性试验下的试样破坏缓慢,长石类的相对含量减少最多,石英的相对含量增加最多;冻融循环试验和安定性试验下的试样均产生较多裂隙,其中安定性试验下的试样中的长石类相对含量变化较为明显,但冻融循环试验下的试样的矿物成分变化并不明显。【结论】SH-(C+CaO)改性夯筑土在干旱地区常见的碱性环境下表现较优。本文为夯土类遗址文物掏蚀区的保护与修复提供了理论支撑。展开更多
This study utilized a computer application developed in Visual StudioTM using C# to extract pixel samples (RGB) from multiple images (26 images obtained from August 20, 2024, to September 22, 2024), of a purslane pot ...This study utilized a computer application developed in Visual StudioTM using C# to extract pixel samples (RGB) from multiple images (26 images obtained from August 20, 2024, to September 22, 2024), of a purslane pot taken from a top-down perspective at a distance of 30 cm. These samples were projected into the CIELAB color space, and the extracted pixels were plotted on the a*b* plane, excluding the luminance value. A polygon was then drawn around all the plotted pixels, defining the color to be identified. Subsequently, the application analyzed another image to determine the number of pixels within the polygon. These identified pixels were transformed to white, and the percentage of these pixels relative to the total number of pixels in the image was calculated. This process yielded percentages for brown (soil), green (leaf cover), and pink (stem color). A single polygon was sufficient to accurately identify the green and brown colors in the images. However, due to varying lighting conditions, customized polygons were necessary for each image to accurately identify the stem color. To validate the green polygon’s accuracy in identifying purslane leaves, all leaves in the image were digitized in AutoCADTM, and the green area was compared to the total image area to obtain the observed green percentage. The green percentage obtained with the polygon was then compared to the observed green percentage, resulting in an R2 value of 0.8431. Similarly, for the brown color, an R2 value of 0.9305 was found. The stem color was not subjected to this validation due to the necessity of multiple polygons. The R2 values were derived from percentage data obtained by analyzing the total pixels in the images. When sampling to estimate the proportion and analyzing only the suggested sample size of pixels, R2 values of 0.93049 for brown and 0.8088 for green were obtained. The average analysis time to determine the brown soil percentage using the polygon (BP) for 26 images with an average size of 1070 × 1210 pixels was 44 seconds. In contrast, sampling to estimate the proportion reduced the analysis time to 0.9 seconds for the same number of images. This indicates that significant time savings can be achieved while obtaining similar results.展开更多
文摘【目的】SH+遗址土+生石灰[SH-(C+CaO)]改性夯筑土对遗址夯土具有较好的兼容性,故其耐久性研究在该改性土的应用与推广中是必不可少的。【方法】对SH-(C+CaO)改性夯筑土进行干湿循环、冻融循环、安定性、耐碱性试验,且不同次数均设定为1、3、5、9、15、25和40次,以系统揭示该改性土的物理、力学、水热性能的变化规律,并利用扫描电镜(scanning electron microscopy,SEM)和X射线衍射(X-ray diffraction,XRD)进行定性分析,以孔隙面积占比建立损伤变量,并分析损伤变量与各性能指标的定量关系。【结果】试样外观在40次循环结束后仍然完整。试样的各性能指标随循环次数的增加逐渐衰减,均在第5次和第25次循环处存在明显拐点;各项性能指标损失率较小,损伤变量与各性能指标之间高度相关,且试样在耐碱性试验条件下的损伤程度最小,在安定性试验下的损伤程度最大。循环结束后的SEM和XRD试验结果表明:干湿循环试验下的试样因颗粒团聚产生破坏,长石类和方解石的相对含量降低;耐碱性试验下的试样破坏缓慢,长石类的相对含量减少最多,石英的相对含量增加最多;冻融循环试验和安定性试验下的试样均产生较多裂隙,其中安定性试验下的试样中的长石类相对含量变化较为明显,但冻融循环试验下的试样的矿物成分变化并不明显。【结论】SH-(C+CaO)改性夯筑土在干旱地区常见的碱性环境下表现较优。本文为夯土类遗址文物掏蚀区的保护与修复提供了理论支撑。
文摘This study utilized a computer application developed in Visual StudioTM using C# to extract pixel samples (RGB) from multiple images (26 images obtained from August 20, 2024, to September 22, 2024), of a purslane pot taken from a top-down perspective at a distance of 30 cm. These samples were projected into the CIELAB color space, and the extracted pixels were plotted on the a*b* plane, excluding the luminance value. A polygon was then drawn around all the plotted pixels, defining the color to be identified. Subsequently, the application analyzed another image to determine the number of pixels within the polygon. These identified pixels were transformed to white, and the percentage of these pixels relative to the total number of pixels in the image was calculated. This process yielded percentages for brown (soil), green (leaf cover), and pink (stem color). A single polygon was sufficient to accurately identify the green and brown colors in the images. However, due to varying lighting conditions, customized polygons were necessary for each image to accurately identify the stem color. To validate the green polygon’s accuracy in identifying purslane leaves, all leaves in the image were digitized in AutoCADTM, and the green area was compared to the total image area to obtain the observed green percentage. The green percentage obtained with the polygon was then compared to the observed green percentage, resulting in an R2 value of 0.8431. Similarly, for the brown color, an R2 value of 0.9305 was found. The stem color was not subjected to this validation due to the necessity of multiple polygons. The R2 values were derived from percentage data obtained by analyzing the total pixels in the images. When sampling to estimate the proportion and analyzing only the suggested sample size of pixels, R2 values of 0.93049 for brown and 0.8088 for green were obtained. The average analysis time to determine the brown soil percentage using the polygon (BP) for 26 images with an average size of 1070 × 1210 pixels was 44 seconds. In contrast, sampling to estimate the proportion reduced the analysis time to 0.9 seconds for the same number of images. This indicates that significant time savings can be achieved while obtaining similar results.
