Application of Developed New Artificial Intelligence Approaches in Civil Engineering for Ultimate Pile Bearing Capacity
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RESEARCH PAPER
Application of Developed New Artificial Intelligence Approaches in Civil Engineering for Ultimate Pile Bearing Capacity Prediction in Soil Based on Experimental Datasets Hooman Harandizadeh1 · Vahid Toufigh2 Received: 28 May 2019 / Accepted: 23 December 2019 © Shiraz University 2020
Abstract In this study, a neural-fuzzy (NF) system is combined with group method of data handling (GMDH) in order to estimate the axial bearing capacity of driven piles. To reach optimum design of this conjunction (NF-GMDH) network, the metaheuristic techniques including particle swarm optimization (PSO) and gravitational search algorithm (GSA) were utilized. The datasets used for estimating pile bearing capacity were collected from the literature review. The parameters influencing the modeling and pile capacity analysis were taken into account as Flap number, surrounding soil properties, the pile geometric characteristics, and internal friction angles of the pile–soil interface. The efficiency of hybrid NF-GMDH networks in train and test phases was examined. Applying the PSO algorithm to the hybrid NF-GMDH model structure improved the model performance and achieved a higher level of accuracy in predicting the ultimate pile bearing capacity (RMSE = 1375 and SI = 0.255) compared to NF-GMDH model developed by GSA (RMSE = 1740.7 and SI = 0.357). In addition, based on achieved results, the developed NF-GMDH networks showed relatively better performances in comparison with gene programming and linear regression model methods considered in this study. Keywords Driven piles · Flap number · Ultimate bearing capacity · Neural-fuzzy system · Group method of data handling · Evolutionary algorithms
1 Introduction The axial pile bearing capacity prediction has a significant role in fundamental principles of deep foundation analysis and design. Although analytical and semiempirical relationships have been presented to obtain ultimate axial bearing capacity for a wide range of driven piles based on constant geotechnical conditions, however some influence factors should be considered with a high degree of precision to derive these conventional relationships (Shahin 2010, 2014). In other words, since the interaction behavior of soil and pile is a complex phenomenon and not fully understood, most of * Hooman Harandizadeh [email protected]; [email protected] 1
Department of Civil Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, 22 Bahman Blvd., Kerman, P.O. Box 76175‑133, Iran
Faculty of Civil and Surveying Engineering, Graduate University of Advanced Technology, Kerman, Iran
2
the proposed empirical models have a relatively low accuracy in estimation of pile capacity (Shahin 2016). The governing analytical and empirical equations are based on the simplifications and assumptions applied in the physical pile and soil behavior modeling in practice; also, using static pile analysis methods for evaluating the unit shaft and pile tip resistance may have achieved the same bearing c
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