永久冻土
地温梯度
路基
岩土工程
堆
冻胀
地质学
钻孔
环境科学
人类住区
地理
考古
海洋学
地球物理学
作者
Kaichi Qiu,Wenbing Yu,Yan Lü,Da Hu,Mingyi Zhang
标识
DOI:10.1016/j.jobe.2023.107919
摘要
The geothermal environment of urban buildings is critical to the structural stability of urban buildings in permafrost regions, which is directly related to the safety of human settlements. However, existing studies have not been able to obtain the 3D geothermal field of building subgrades and its evolution during operation, due to a lack of continuous geothermal data. In this study, thermistor sensors were installed inside pile foundations during the construction, providing valuable data for geothermal field analysis. Based on long-term in-situ monitoring data, this paper studied the evolution of the geothermal field of urban building in permafrost regions of Northeast China. The results indicate that excavation and cast-in-place foundation construction cause irreversible degradation of the warm permafrost. When the building is vacant, the subgrade is frozen in the winter, with a maximum frost penetration depth of 6 m. When heated in winter, the frost penetration depth decreases year by year and the ground becomes warmer. The ground temperature within the monitoring depth (i.e., the bearing layer of the pile foundation) increases to above 0 °C. The mean annual ground temperature within the 0–4 m depth of the subgrade increases significantly, while that at depths below 7 m exhibits slight fluctuations. The 3D geothermal field and its evolution of the building subgrade are obtained for the first time. It was also found that the sunny-shady effect induces asymmetry of the geothermal field of the building, which increases the risk of uneven settlement of the building. In addition, the seasonal frost penetration on the northern side reaches 3 m, which can cause frost-heaving damage to buildings. These findings provide scientific evidence for risk assessment, disaster prevention, and control of building in permafrost regions under the scenario of climate warming.
科研通智能强力驱动
Strongly Powered by AbleSci AI