LAUSR

During the design and construction of a project, determination of geomechanical parameters is a key factor for its success. Back analysis is an appropriate method to reduce in situ and field measurements during site investigation and design phase of the projects. Over the past decades, displacement measurements were used as input data for back analysis of geomechanical parameters. In back analysis, an important factor is to choose an appropriate algorithm to minimize the magnitude of the error function in order to reduce the difference between measured and calculated displacements. In this paper, two methods, i.e. continuous ant colony algorithm (CACA) and multivariable regression (MR), were applied to calculate optimized values for four geomechanical parameters in “Lavarak” underground hydroelectric power plant cavern in Iran. These four parameters are maximum principal stress, minimum principal stress, the angle between maximum principal stress and the horizontal axis, and the deformation modulus of the rock mass. Initially, back analysis was conducted with CACA. For this purpose, CACA was programmed in Phase2 software used for numerical modelling to calculate the displacement of the measuring points. Then, back analysis of four parameters was done with both linear and nonlinear MR using Phase2, and the optimized values were calculated. According to the Lar Consulting Engineers’ data for Lavarak HEPP, the results show lower general error function values of CACA than MR. In other words, CACA algorithm shows better performance for obtaining optimal values and for having more accurate calculations. CACA shows better performance for back-calculation of the maximum principal stress, the angel of maximum principal stress, and the deformation modulus of the rock mass.