方解石
降水
腐蚀
土壤水分
水槽
环境科学
土工试验
扫描电子显微镜
水蚀
土壤科学
地质学
水文学(农业)
岩土工程
矿物学
材料科学
流量(数学)
地貌学
复合材料
物理
气象学
数学
几何学
作者
Xinyi Jiang,Cassandra J. Rutherford,Bora Çetin,Kaoru Ikuma
出处
期刊:Geo-Congress 2020
日期:2020-02-21
卷期号:: 104-110
被引量:5
标识
DOI:10.1061/9780784482834.012
摘要
Soil erosion is one of the main challenges of today's geotechnical engineering discipline along rivers, coastlines, and after major storm and hurricane events. The use of vegetation cover is one of the most cost-effective methods for soil erosion control, however, it does not provide long-term ground improvement since the method does not improve soil properties. The study presented in this paper explored the use of the bacterial enzyme induced calcite precipitation (BEICP) technique to reduce the erosion susceptibility of silty sandy soil. The BEICP technique was applied to the surface of sand specimens via a spraying method. The specimens were treated at four different enzyme concentrations (0 mg/mL-control, 0.3 mg/mL, 0.7 mg/mL, and 1.5 m/mL) and tested for erosion susceptibility. The specimens were subjected to water erosion in a recirculating flume with mean flow velocity of 23.2 cm/s. The amount of soil erosion was measured via weight loss of the soil specimen. In addition, scanning electron microscope (SEM) analyses were conducted on specimens. Results of the erosion tests demonstrated that the soils treated with high enzyme concentrations experienced almost no soil weight loss while the weight loss increased significantly (from 2.4% to 15.5%) with a decrease in enzyme concentrations. SEM analyses showed that top 2.54 cm (1-inch) layer of the soil specimen had the highest calcite precipitation formation than the lower level which could help to reduce the erosion susceptibility of the silty sand specimens. Overall, results of this study indicate that the BEICP technique has a potential act as an effective solution to reduce the erosion susceptibility sandy soils when applied through a spray application to the surface of the material.
科研通智能强力驱动
Strongly Powered by AbleSci AI