پیش‌بینی پتانسیل رمبندگی خاک‌های رمبنده از طریق شبکه‌های عصبی مصنوعی

Collapsible soils are soils that compact and collapse after they get wet. The soil particles are originally loosely packed and barely touch each other before moisture soaks into the ground. As water is added to the soil in quantity and moves downward, the water wets the contacts between soil particles and allows them to slip past each other to become more tightly packed. Water also affects clay between other soil particles so that it first expands, and then collapses like a house of cards. Another term for collapsible soils is "hydrocompactive soils" because they compact after water is added. The amount of collapse depends on how loosely the particles are packed originally and the thickness of the soil that becomes wetted. Collapsible soils consist of loose, dry, low-density materials that collapse and compact under the addition of water or excessive loading. These soils are distributed throughout the southwestern United States, specifically in areas of young alluvial fans, debris flow sediments, and loess (wind-blown sediment) deposits. Soil collapse occurs when the land surface is saturated at depths greater than those reached by typical rain events. This saturation eliminates the clay bonds holding the soil grains together. Similar to expansive soils, collapsible soils result in structural damage such as cracking of the foundation, floors, and walls in response to settlement. Collapsible soils may be suspected in undeveloped areas that have young, accumulating sandy and silty soils in dry areas. The soils may be confirmed to be collapsible through engineering testing. These tests include study of seismic waves through the soils, rates of drilling through the soils (blow counts), and testing undisturbed soil samples obtained by careful drilling for compaction after wetting. In this study, the ability of Artificial Neural Networks (ANN) has been investigated to determine the collapse potential of soils. Therefore, different samples of collapsible soil have been collected from an area (Zahedan plain). General tests were performed on the samples in the laboratory and 130 samples of collapsible soil from different depths and locations were recorded in the database. The collapse potential tests (One-dimensional collapse test) was carried out on the samples and with the aim of further investigations, the grain size distribution, specific gravity, atterberg limits and strength properties of the samples were performed. In the later stages, the collapsible samples data were prepared for the artificial neural networks input. After the network training process and the subsequent learning, some network models have been selected under experiments, which include six inputs and one output. According to the predicted results, it was indicated that the correlation between experimental and predicted data by the ANN is 95%. Furthermore, the results show that artificial neural networks can predict collapse potential of soils, also the calculations and required tests will be reduced due to their simple use and inexpensive tests.