Abstract
In this paper, a neuro particle-based optimization of the artificial neural network (ANN) is investigated for slope stability calculation. The results are also compared to another artificial intelligence technique of a conventional ANN and adaptive neuro-fuzzy inference system (ANFIS) training solutions. The database used with 504 training datasets (e.g., a range of 80%) and testing dataset consists of 126 items (e.g., 20% of the whole dataset). Moreover, variables of the ANN method (for example, nodes number for each hidden layer) and the algorithm of PSO-like swarm size and inertia weight are improved by utilizing a total of 28 (i.e., for the PSO-ANN) trial and error approaches. The key properties were fed as input, which were utilized via the analysis of OptumG2 finite element modelling (FEM), containing undrained cohesion stability of the baseline soil (Cu), angle of the original slope (β), and setback distance ratio (b/B) where the target is selected factor of safety. The estimated data for datasets of ANN, ANFIS, and PSO-ANN models were examined based on three determined statistical indexes. Namely, root mean square error (RMSE) and the coefficient of determination (R2). After accomplishing the analysis of sensitivity, considering 72 trials and errors of the neurons number, the optimized architecture of 4 × 6 × 1 was determined to the structure of the ANN model. As an outcome, the employed methods presented excellent efficiency, but based on the ranking method, the PSO-ANN approach might have slightly better efficiency in comparison to the algorithms of ANN and ANFIS. According to statistics, for the proper structure of PSO-ANN, the indexes of R2 and RMSE values of 0.9996, and 0.0123, as well as 0.9994 and 0.0157, were calculated for the training and testing networks. Nevertheless, having the ANN model with six neurons for each hidden layer was formulized for further practical use. This study demonstrates the efficiency of the proposed neuro model of PSO-ANN in estimating the factor of safety compared to other conventional techniques.
Highlights
In most engineering problems such as stability of slopes, many parameters need to be taken into account [1,2,3]
Tslhoepesibgyniufiscianngt tfharcetoerinintepllrigedenicttimngetthhoedfsacotfoAr NofNsa, fAetNyFwISa,sacnodnshiydberrieddnaesutrhoePoSuOtp-AutNoNf tmheetnheotwdso.rkTsh.eInsigthniisfiwcaanyt, four effective parameters were recognized as the input dataset: (i) The setback distance (b/B), (ii) the slope angle (β), (iii) the undrained cohesion strength, and (iv) the vertical load on the slope (Fy)
The basic aim of the present study was to propose a proper predictive approach in order to estimate the factor of safety of a single layer pure cohesive slope subjected to vertical stresses
Summary
In most engineering problems such as stability of slopes, many parameters need to be taken into account (e.g., soil stiffness, soil-interface interaction parameters, etc.) [1,2,3]. Most conventional approaches are very complicated, an in most cases, not considered reliable solutions (e.g., consisting nonlinear finite element model or finite difference method analysis consideration). The developed solutions explained how a particular slope (for example height, slope angle, soil characteristics, and so on) affects the above infrastructures but did not present a general solution in the case of other slope conditions. In this regard, the applied stresses on the slope, along with its distance from the crest of slope, are known as an essential factor that affects the stability of the slope [4,5,6]. The slope stability (e.g., subjected to vertical stresses or seismic loading) is demonstrated as a function of different key parameters that are called initial ground properties (e.g., soil elastic modulus (E), internal friction angle (φ) which for the cohesive slope is equal to zero, unit weight (γ), undrained cohesion strength (Cu) which for the sandy slope is considered to zero, Poisson’s ratio (v))
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