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Development of neuro-fuzzy and neuro-bee predictive models for prediction of the safety factor of eco-protection slopes

Safa M. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam|
Khorami M. Universidad UTE, Facultad de Arquitectura y Urbanismo, Calle Rumipamba s/n y Bourgeois, Quito, Ecuador| Wakil K. Information Technology Department, National Institute of Technology, Sulaimani, Kurdistan Region 46001, Iraq| Trung N.T. Research Center, Sulaimani Polytechnic University, Kurdistan Region, Sulaimani, 46001, Iraq| Suhatril M. Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, 758307, Viet Nam| Shariati M. Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, 758307, Viet Nam| Sari P.A. Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, 50603, Malaysia|

Physica A: Statistical Mechanics and its Applications Số , năm 2020 (Tập 550, trang -)

DOI: 10.1016/j.physa.2019.124046

Tài liệu thuộc danh mục: ISI, Scopus

English

English

Từ khóa: Cost effectiveness; Forecasting; Fuzzy neural networks; Mean square error; Safety factor; Slope protection; Slope stability; Soil testing; Determination coefficients; Eco-engineering; Fuzzy inference systems; Guthrie corridor expressway (GCE); Mechanical structures; Neuro-bee; Neuro-Fuzzy; Root mean squared errors; Fuzzy inference
Tóm tắt tiếng anh
This study is aimed to investigate the surface eco-protection techniques for cohesive soil slopes along the selected Guthrie Corridor Expressway (GCE) stretch by way of analyzing a new set of intelligence techniques namely neuro-bee, artificial neural network (ANN) and neuro-fuzzy. Soil erosion and mass movement which induce landslides have become one of the disasters faced in Selangor, Malaysia causing enormous loss affecting human lives, destruction of property and the environment. Establishing and maintaining slope stability using mechanical structures are costly. Hence, biotechnical slope protection offers an alternative which is not only cost effective but also aesthetically pleasing. To reach the aim of the current study, a field investigations and numerical studies were conducted and a suitable database was prepared and established. By preparing factor of safety (FOS) as a single output parameter and a combination of the most important parameters on that, the desired models have been designed based on training and test patterns. In order to evaluate and compare the prediction precision of the developed models, a series of statistical indices, such as root mean squared error (RMSE), determination coefficient (R-square) and variance account for (VAF) are calculated. Many intelligence models with the most effective parameters on the mentioned models were developed to predict FOS. Based on the simulation results and the measured indices, it was found that the proposed neuro-fuzzy model with the lowest system error and highest R-square performs better as compared to other proposed ANN and neuro-bee models. Therefore, the neuro-fuzzy can provide a new applicable model to effectively predict the FOS of the slopes due to the fact that it is able to combine the advantages of the ANN and fuzzy inference system to indicate a high prediction capacity in solving problem of slope stability. � 2020 Elsevier B.V.

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