Use of adaptive neuro-fuzzy inference system and gene expression programming methods for estimation of the bearing capacity of rock foundations

Abstract

Purpose This study aims to examine the potential of two artificial intelligence (AI)-based algorithms, namely, adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP), for indirect estimation of the ultimate bearing capacity (qult) of rock foundations, which is a considerable civil and geotechnical engineering problem. Design/methodology/approach The input-processing-output procedures taking place in ANFIS and GEP are represented for developing predictive models. The great importance of simultaneously considering both qualitative and quantitative parameters for indirect estimation of qult is taken into account and explained. This issue can be considered as a remarkable merit of using AI-based approaches. Furthermore, the evaluation procedure of various models from both engineering and accuracy viewpoints is also demonstrated in this study. Findings A new and explicit formula generated by GEP is proposed for the estimation of the qult of rock foundations, which can be used for further engineering aims. It is also presented that although the ANFIS approach can predict the output with a high degree of accuracy, the obtained model might be a black-box. The results of model performance analyses confirm that ANFIS and GEP can be used as alternative and useful approaches over previous methods for modeling and prediction problems. Originality/value The superiorities and weaknesses of GEP and ANFIS techniques for the numerical analysis of engineering problems are expressed and the performance of their obtained models is compared to those provided by other approaches in the literature. The findings of this research provide the researchers with a better insight to using AI techniques for resolving complicated problems.

Combined ANFIS–Wavelet Technique to Improve the Estimation Accuracy of the Power Output of Neighboring PV Systems during Cloud Events

“…Results in Figure 6 and Table 3 also show a comparison of the model proposed in this paper and our recent VRI-GEP technique [29]. It can be observed that both the GEP and ANFIS techniques provide more accurate results than other techniques in the literature [39,40]. Reference [29] employed the GEP technique to develop a VRI model whereas in this paper, the ANFIS-DWT model is used to estimate the power output directly without calculating the VRI as per the strategy of all existing models in the literature.…”

“…Results in Figure 6 and Table 3 also show a 83 comparison of the model proposed in this paper and our recent VRI-GEP technique [29]. It can be 84 observed that both the GEP and ANFIS techniques provide more accurate results than other 85 techniques in the literature [39,40]. Reference [29]…”

A hybrid computational method for optimization design of bistable compliant mechanism

“…Some metrics are used to verify the ANFIS prediction (Dao and Huang, 2015a; Sadrossadat et al , 2018) as follows:…”

Using genetic programming to model the bond strength of GFRP bars in concrete under the effects of design guidelines

“…Artificial intelligence (AI) predictions such as those made by artificial neural networks (ANN) commonly offer results that are significantly more accurate than predictions generated by either analytical models or design guidelines. However, ANN has been characterized as “black-box” models due to the extremely large numbers of nodes and connections within their layered structures (Tsai, 2009; Tsai, 2010; Öztekin, 2016; Vardhan et al , 2017; Yan and Lin, 2017; Sadrossadat et al , 2018). Since it was first proposed by Koza (1992), genetic programming (GP) has garnered considerable research attention because of its ability to model nonlinear relationships for input–output mappings without assuming the prior form of these relationships.…”