Abstract
Numerical and computational analyses surrounding the behavior of the bearing capacity of soils near or adjacent to slopes have been of great importance in earthwork constructions around the globe due to its unique nature. This phenomenon is encountered on pavement vertical curves, drainages, and vertical infrastructure foundations. In this work, multiple data were collected on the soil and footing interface parameters, which included width of footing, depth of foundation, distance of slope from the footing edge, soil bulk density, slope and frictional angles, and bearing capacity factors of cohesion and overburden pressure determined for the case of a foundation on or adjacent to a slope. The genetic programming (GP), evolutionary polynomial regression (EPR), and artificial neural network (ANN) intelligent techniques were employed to predict the ultimate bearing capacity of footing on or adjacent to a slope. The performance of the models was evaluated as well as compared their accuracy and robustness with the findings of Prandtl. The results were observed to show the superiority of GP, EPR, and ANN techniques over the computational works of Prandtl. In addition, the ANN outclassed the other artificial intelligence methods in the exercise.
Highlights
Building substructures are often constructed on or adjacent to slopes owing to the nonavailability of level ground, especially in hilly dominant topography encountered in highway vertical curves, embankments, erosion watersheds, etc. e study of the bearing capacity of loaded slopes is vital because they are more prone to fail than other types of earth structures [1,2,3,4,5]
Methods proposed by the researchers available to find the bearing capacity of shallow foundations on or near slopes include limit equilibrium analysis [10, 11], slip line analysis [12], variational calculus [13], the method of rigorous characteristics [14], improved movement optimization [15], finite element analysis [16, 17], and multiblock analysis [9]
Prandtl [18] is generally credited with some pioneer work in bearing capacity theory, having tried to establish the punching failure mechanism for thick metals based on the theory of plasticity
Summary
Building substructures are often constructed on or adjacent to slopes owing to the nonavailability of level ground, especially in hilly dominant topography encountered in highway vertical curves, embankments, erosion watersheds, etc. e study of the bearing capacity of loaded slopes is vital because they are more prone to fail than other types of earth structures [1,2,3,4,5]. Despite studies into the effects of foundation shape and depth [19], Meyerhof (1957 and 1974) [20,21,22,23], there has been few research works on the bearing capacity of Journal of Engineering footings on and/or adjacent to slopes made of c′ − v′ soils. Meyerhof studied general failure mechanisms for bearing capacity on purely cohesionless or cohesive soils adjacent to slopes using an assumed failure pattern based on the empirical observation from model footing tests in the laboratory. Griffiths [25] used the finite element analysis (FEA) to determine the bearing capacity of c′ − v′ soils on slopes attaining considerable results His Nc factor recorded pitfall due to a convergence issue. There are numerous AI modelling algorithms, namely, genetic algorithm (GA), ant colony (AC), differential evolution (DE), particle swarm (PS), artificial neural network (ANN), genetic programming (GP) [35], and gene expression programming [32], artificial neural network (ANN) [36], genetic programming (GP), and gene expression programming have been widely used [29, 35]
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