# Novel Nature-Inspired Hybrids of Neural Computing for Estimating Soil Shear Strength
## 1 Introduction  
Soil shear strength (SSS) is defined as the resistance of soil against shear stresses [1]. It is one of the most determinant parameters in the designing process of geotechnical engineering projects [2]. For example, for designing high and massive structures, proper analysis of the SSS is very important, as the load is directly applied to the soil underneath. More clearly, this parameter enables the engineers to decide about the foundation type, and also, whether terrain improvement measures are required or not [3]. As a matter of fact, other than providing appropriate prerequisites (such as sampling and specimen maintenance) for SSS laboratory tests, these methods required a considerable time as well ashigh costs [4]. High technical skills are also necessary due to utilizing complicated equipment such as a triaxial test apparatus [5; 6]. This is why achieving inexpensive and non-destructive SSS evaluative methods is a crucial task in related projects. Recently, powerful artificial intelligence techniques such as adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) have almost antiquated traditional models for analyzing geotechnical phenomena. [PERSON] and [PERSON] [7] investigated the feasibility of five well-known predictive models of ANFIS, genetic programming (GP), classical support vector machine (SVM), and two of its variants, namely regularized generalized proximal SVM and twin SVM in modeling the friction capacity of driven piles set in clay. In addition to presenting a GP-based predictive formula, they demonstrated the superiority of the ANFIS. For SSS, [PERSON] et al. [8] employed the ANN and ANFIS for simulating the SSS from measured particle size distribution, normalized difference vegetation index (NDVI), soil organic matter (SOM), and calcium carbonate equivalent (CCE). Based on the obtained values of error and correlation of 0.05 and 0.86 for the ANN, and 0.08 and 0.60 for ANFIS, they concluded that the ANN performs more efficiently. [PERSON] et al. [9] predicted the SSS parameters using a probabilistic neural network. [PERSON] and [PERSON] [10] showed the efficiency of two clustering method of ANFIS, namely fuzzy c-mean clustering and subtractive clustering for estimating the shear strength of unsaturated soils. The advent of metaheuristic algorithms enabled scholars to optimize the conditions of various engineering problems. As well as this, overcoming the computational drawbacks of the intelligent model is another notable application of these techniques. Many researchers have successfully used these algorithms for optimizing the ANN [11; 12] and ANFIS [11; 13] in geotechnical engineering issues. In the case of shear strength, [PERSON] et al. [14] developed the combination of cuckoo search optimization and least squares support vector machine (LSSVM) for predicting the SS of soil. In that work, they studied a national expressway project. The findings revealed that the proposed ensemble outperforms ANN, regression tree, and typical LSSVM. Likewise, [PERSON] et al. [3] synthesized the support vector regression (SVR) with particle swarm optimization (PSO) to create a hybrid model for the approximation of soil SS. Referring to the correlation of 0.888, they introduced the proposed SVR-PSO model as a promising alternative for the mentioned aim. [PERSON] et al. [15] tested the capability of two ensembles of ANFIS based on PSO and genetic algorithm (GA) for SS prediction of plastic clay soil. It was shown that both proposed ensembles achieved higher accuracy than ANN and SVR. However, the PSO (correlation = 0.601 and error = 0.038) surpassed GA (correlation = 0.569 and error = 0.040) in optimizing the ANFIS. As stated, although the potential of well-known hybrid algorithms, such as PSO and GA, has been accepted in the field of geotechnical engineering, a lack of new exploration for employing other optimization algorithms could be considered as a knowledge gap in this field.