聚丙烯纤维加筋黄土边坡防护原位测试及改进策略
In Situ Test and Improvement Measures of Polypropylene Fiber-Reinforced Loess Slope Protection
徐志平 1贾卓龙 2晏长根 2王逸凡2
作者信息
- 1. 中铁第一勘察设计院集团有限公司,陕西 西安 710043
- 2. 长安大学 公路学院,陕西 西安 710064
- 折叠
摘要
为了给加筋黄土边坡防护技术的大面积推广提供理论依据和参考,对聚丙烯纤维加筋黄土防护边坡的土壤含水率及边坡形态进行了持续 6 个月的原位测试,结果表明:聚丙烯纤维加筋黄土具有良好的边坡防护效果,加筋黄土防护边坡土壤含水率总体趋于稳定,不同土层土壤含水率平均变异系数比原状边坡降低 10.89%,这得益于加筋黄土防护层形态总体维持完整,能够长期抵抗降雨、干湿循环、冻融循环等不利外部环境的持续影响;然而,聚丙烯纤维加筋黄土防护边坡上植被生长缓慢,且防护层与原状土体易分异脱离.提出了对加筋黄土进行保水改良、采用快速植生的多功能层设计和可操作性强的材料物理防脱技术等加筋黄土防护边坡的改进策略.
Abstract
In order to provide a theoretical basis and reference for the large-scale promotion of the technology of reinforced loess slope protec-tion,the soil moisture content and slope morphology of the polypropylene fiber-reinforced loess protective slope were tested in situ for 6 months.The results show that the PP fiber-reinforced loess has a good slope protection effect,and the soil moisture content of the reinforced loess protective slope is generally stable.The average variability of soil moisture content in different soil layers is 10.89%lower than that of the untreated slope.This is because the morphology of the reinforced loess protective layer is generally complete,and it can resist the contin-uous impact of adverse external environments such as rainfall,dry-wet cycle,and freeze-thaw cycle for a long time.However,the vegetation growth on the PP fiber-reinforced loess protective slope is slow,and the protective layer is easy to differentiate and detach from the undis-turbed soil.The improvement strategies of the reinforced loess protective slope are put forward,such as water retention improvement of the re-inforced loess,the design of the multi-functional layer with rapid vegetation,and the physical anti-detachable technology of the material with strong operability.
关键词
黄土边坡防护/聚丙烯纤维/加筋黄土/原位测试/土壤含水率/改进策略Key words
loess slope protection/polypropylene fiber/reinforced loess/in situ testing/soil moisture content/improvement strategy引用本文复制引用
基金项目
国家自然科学基金(42077265)
中铁第一勘察设计院集团有限公司技术开发项目(院科17-26)
出版年
2024
国家科技期刊平台
NSTL
万方数据