دانلود رایگان مقاله انگلیسی فرمول بندی مبتنی بر ANFIS عددی برای پیش بینی ظرفیت باربری بار محوری شمع ها - اشپرینگر 2017

Abstract

This study explores the potential of adaptive neuro-fuzzy inference systems (ANFIS) for prediction of the ultimate axial load bearing capacity of piles (Pu) using cone penetration test (CPT) data. In this regard, a reliable previously published database composed of 108 datasets was selected to develop ANFIS models. The collected database contains information regarding pile geometry, material, installation, full-scale static pile load test and CPT results for each sample. Reviewing the literature, several common and uncommon variables have been considered for direct or indirect estimation of Pu based on static pile load test, cone penetration test data or other in situ or laboratory testing methods. In present study, the pile shaft and tip area, the average cone tip resistance along the embedded length of the pile, the average cone tip resistance over influence zone and the average sleeve friction along the embedded length of the pile which are obtained from CPT data are considered as independent input variables where the output variable is Pu for the ANFIS model development. Besides, a notable criticism about ANFIS as a prediction tool is that it does not provide practical prediction equations. To tackle this issue, the obtained optimal ANFIS model is represented as a tractable equation which can be used via spread sheet software or hand calculations to provide precise predictions of Pu with the calculated correlation coefficient of 0.96 between predicted and experimental values for all of the data in this study. Considering several criteria, it is represented that the proposed model is able to estimate the output with a high degree of accuracy as compared to those results obtained by some direct CPT-based methods in the literature. Furthermore, in order to assess the capability of the proposed model from geotechnical engineering viewpoints, sensitivity and parametric analyses are done.

9 Summary and Conclusion

This paper aimed at investigating the robustness of ANFIS method for estimating the ultimate axial load bearing capacity of piles using CPT data which is a crucial problem in geotechnical engineering. In this regard, a collection of data was used for the development of the models. The selected database contained information about the pile installation method, pile material, full-scale pile load test and CPT data. However, the model was developed to simultaneously take into account At, As, qcs, f s, qct as input variables obtained from pile load test and CPT results due to the fact that these variables are more meaningful from geotechnical engineering viewpoints. It is well-known that there exist some practical equations to estimate the ultimate axial load bearing capacity of piles based on experimental results obtained by CPT, as was presented in the paper. It should be highlighted that the strength of ANFIS algorithm, as a predictive tool, absolutely lies in its high precision for prediction and approximation purposes; however, the main weakness of ANFIS modeling technique which has scholarly been visited in the existing literature is the fact that it has not been able to generate explicit models or equations which can be used for hand-calculation aims. In other words, ANFIS has been considered as a black-box predictive tool. In this paper, the obtained optimal ANFIS model was converted to an explicit tractable formula which can be used for pile design uses. Additionally, engineers are required to know about the degree of accuracy, the physical behavior of the model, the relative significance of each variable and the validation and verification of the models they use in their computations or calculations.