Abstract:

To address the issue of channel-type loose deposits undergoing simultaneous vertical infiltration and lateral runoff during rainfall, which significantly affects their stability and failure modes, a prediction model for the infiltration-wetting front depth of loose deposits under multi-directional runoff influence (multi-directional catchment Green-Ampt, abbreviated as MCGA) was established based on the theoretical framework of the classic Green-Ampt (referred to as GA) model, incorporating the influence coefficient of lateral catchment and the slope runoff coefficient. To validate the reliability and applicability of the proposed model, we designed and conducted physical model experiments to simulate multi-directional infiltration into loose deposits under coupled multi-factor interactions. Systematically simulating infiltration processes under varying conditions of infiltration duration, vertical rainfall intensity, lateral inflow angle, and flow rate, the migration path and depth dynamics of the wetting front were monitored in real-time via data acquisition and observation systems. Computational results from the proposed model and the classical GA model were then compared with experimental measurements. The results indicate that, compared to the GA model, which disregards multi-directional runoff effects, the MCGA model shows superior agreement between predicted wetting front depths and experimental measurements across multiple scenarios. The mean absolute percentage error (MAPE) of the MCGA model consistently falls below 10%, demonstrating significantly enhanced predictive accuracy. Further parameter analysis indicates that the model is applicable to infiltration scenarios where lateral inflow angles range from 15° to 60°, and lateral inflow intensity does not exceed vertical rainfall intensity. These findings provide a theoretical reference for deepening the understanding of unsaturated infiltration patterns, failure mechanisms, and disaster risk assessment in loose deposits subjected to multi-directional inflow.

Key words: 1. State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, Sichuan 610059, China, 2. Sichuan Southwest Dadi Group Co., Ltd., Chengdu, Sichuan 610032, China