عنوان مقاله [English]
An accurate prediction of bearing capacity is important for the safe and reliable design of shallow foundations. The bearing capacity of shallow foundations has been widely investigated by different researchers, who have proposed different methods to determine this critical topic. However, traditional deterministic methods of estimating the bearing capacity of shallow foundations do not explicitly consider the uncertainty associated with factors affecting bearing capacity. Therefore, due to the large uncertainty associated with geotechnical engineering, such s atural eterogeneity, measurement and transformation uncertainty, it may not always represent a realistic situation. Reliability methods consider all sources of uncertainty in the analysis and incorporate them in the geotechnical design. In this paper, a rational approach, based on probabilistic analysis, using Monte Carlo simulation, is presented. This approach accounts for uncertainty associated with two shear parameters, i.e; soil friction angle, soil cohesion and unit weight of soil (C, $\varphi$ and $\gamma$). In this study, the bearing capacity proposed by Meyerhof is selected in the deterministic and probabilistic ultimate bearing capacity of strip footings. Soil cohesion, C, soil friction angle, $\varphi$, and soil unit weight, $\gamma$, are assumed to be random variables, and the footing breadth, B, and depth of foundation, D, are assumed to be deterministic. For each of the bearing capacity input variables, a random value is generated in relation to the parameter uncertainty of the input mean value; coefficient of variation (COV), known or assumed probability distribution function (PDF) and any correlation that exists between that input variable and other available input variables. Then, from Meyerhofs bearing equation, the deterministic value of the bearing capacity is obtained and repeated hundreds or thousands of times, as part of the Monte Carlo simulation, until certain acceptable convergence is met. Finally, all the bearing capacities obtained are collated and used to plot the cumulative probability distribution curve, from which, predictions associated with target reliability levels of 90\% and 95\% can be estimated. The results indicate that the reduced safety factor in the probabilistic, compared to the deterministic, approach is common. It also reduces the impact of increased uncertainty parameters on the reliability level of the bearing capacity of the foundation.