Volume 13, Issue 4 (10-2023)
2023, 13(4): 497-518 |
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Fattahi H, Ghaedi H. IMPROVING PREDICTIONS OF GEOGRID-REINFORCED STONE COLUMN BEARING CAPABILITY: A COMPARATIVE ANALYSIS OF RES AND REGRESSION METHODS. International Journal of Optimization in Civil Engineering 2023; 13 (4) :497-518
URL: http://ijoce.iust.ac.ir/article-1-568-en.html
URL: http://ijoce.iust.ac.ir/article-1-568-en.html
Abstract: (6095 Views)
Predicting the bearing capability (qrs) of geogrid-reinforced stone columns poses a significant challenge due to variations in soil and rock parameters across different locations. The behavior of soil and rock in one region cannot be generalized to other regions. Therefore, accurately predicting qrs requires a complex and stable nonlinear equation that accounts for the complexity of rock engineering problems. This paper utilizes the Rock Engineering System (RES) method to address this issue and construct a predictive model.To develop the model, experimental data consisting of 219 data points from various locations were utilized. The input parameters considered in the model included the ratio between geogrid reinforced layers diameter and footing diameter (d/D), the ratio of stone column length to diameter (L/dsc), the qrs of unreinforced soft clay (qu), the thickness ratio of Geosynthetic Reinforced Stone Column (GRSB) and USB to base diameter (t/D), and the settlement ratio to footing diameter (s/D). Following the implementation of the RES-based method, a comparison was made with other models, namely linear, power, exponential, polynomial, and multiple logarithmic regression methods. Statistical indicators such as root mean square error (RMSE), mean square error (MSE), and coefficient of determination (R2) were employed to assess the accuracy of the models. The results of this study demonstrated that the RES-based method outperforms other regression methods in terms of accuracy and efficiency.
Keywords: Rock engineering system, regression methods, geogrid-reinforced stone columns, bearing capability, accuracy and efficiency.
Type of Study: Research |
Subject:
Optimal design
Received: 2023/08/18 | Accepted: 2023/10/18 | Published: 2023/10/18
Received: 2023/08/18 | Accepted: 2023/10/18 | Published: 2023/10/18
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