Evaluation of the Water Shielding Performance of a Capillary Barrier System through a Small-Scale Model Test

• Main Author: Byeong-Su Kim
• Format: Article
• Language: English
• Published: MDPI AG 2021-06-01
• Series: Applied Sciences
• Subjects: small-scale capillary barrier; lateral no-flow condition; diversion length; water-shielding; water retention characteristics
• Online Access: https://www.mdpi.com/2076-3417/11/11/5231

Capillary barrier (CB) systems consisting of a fine-grained soil layer placed over a coarse-grained soil layer can generally provide a water-shielding effect, increasing the slope stability of soil structures during rainfall. In order to improve the water-shielding performance of CB systems, laboratory model tests have been previously conducted under various conditions; notably, large-scale model tests are especially required. The inefficiency in increasing the production time of CB models until now explains their high cost. In this paper, we propose a laboratory small-scale CB (SSCB) model test for a quick and efficient evaluation of the function of a CB system. In this model test, differently from previous studies, a side drainage flow in the direction of the inclined sand layer was set as the no-flow condition; moreover, the laboratory SSCB model tests were performed by considering three rainfall intensities (i.e., 20, 50, and 100 mm/h) under the lateral no-flow condition. The results showed that the larger the rainfall intensity, the shorter the diversion length was of the CB system. To evaluate the effectiveness of the SSCB model test proposed in this study, the diversion length was estimated by an empirical equation under the lateral flow condition based on hydraulic conductivity functions and the soil water characteristic curves of sand and gravel and then compared to the results of the SSCB model tests. It was hence demonstrated that the water-shielding performance of the CB system can be efficiently evaluated through SSCB model tests under the lateral no-flow condition, rather than through large-scale model tests.