The use of drilled shafts to stabilize an unstable slope has gained popularity in highway applications, mainly because it is a structural fix that does not require additional right-of-way. An analysis method for determining the factor of safety of a drilled shaft or slope system and for determining the earth thrust on the drilled shafts for structural design is introduced. The concept of the analysis is cast in the limiting equilibrium approach via the method of slices, while incorporating the drilled shaft–induced arching effects as the soil mass moved downslope and around the drilled shafts. The mathematical equations based on the limiting equilibrium calculation, together with the load transfer factor for accounting for the drilled shaft–induced arching effects, are presented. The three-dimensional finite element model parametric study using ABAQUS program was used to derive the semiempirical equations for quantifying the arching effect. A UASLOPE computer program was written to incorporate these algorithms for applications to real cases. A case study of a fully instrumented and monitored slope stabilization project, ATH-124, in Ohio, is presented. The analysis of the slope at the project site using finite element modeling and the computer code UASLOPE is presented, together with field-monitored data. On the basis of field monitoring data and the comparison between the finite element analysis results with the computer code UASLOPE results, the suggested analysis and design approach appears to be reasonable.

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Introduction: Although it is a regular duty of geotechnical engineers to evaluate how much shallow foundation settles in the granular soil, there is no well-approved formula for this task. The intent of this research is to develop a formula that is adequately simple to be used in routine geotechnical engineering work but complete enough to address the behavior of granular soil associated with the settlement issue. Methods: Cone penetration test and foundation load test data were used to generate a formula that can predict the settlement. Genetic Programming (GP) based Symbolic Regression (GP-SR) and artificial neural networks were used to develop an optimized formula. Settlements were also calculated using the finite method and compared to the results of the developed formula. Results and Conclusion: Two formulas were developed using SR, and several models were developed using ANN. ANN model 1 has the highest R2 value (0.93) and the lowest MSE (0.16) among all developed ANN and GP-SR models. FEM settlements were almost double the measured ones in some instances.