دوره 13، شماره 4 - ( 8-1402 )                   جلد 13 شماره 4 صفحات 518-497 | برگشت به فهرست نسخه ها


XML English Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Fattahi H, Ghaedi H. IMPROVING PREDICTIONS OF GEOGRID-REINFORCED STONE COLUMN BEARING CAPABILITY: A COMPARATIVE ANALYSIS OF RES AND REGRESSION METHODS. IJOCE 2023; 13 (4) :497-518
URL: http://ijoce.iust.ac.ir/article-1-568-fa.html
IMPROVING PREDICTIONS OF GEOGRID-REINFORCED STONE COLUMN BEARING CAPABILITY: A COMPARATIVE ANALYSIS OF RES AND REGRESSION METHODS. عنوان نشریه. 1402; 13 (4) :497-518

URL: http://ijoce.iust.ac.ir/article-1-568-fa.html


چکیده:   (9115 مشاهده)
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.
 
متن کامل [PDF 1193 kb]   (2932 دریافت)    
نوع مطالعه: پژوهشي | موضوع مقاله: Optimal design
دریافت: 1402/5/27 | پذیرش: 1402/7/26 | انتشار: 1402/7/26

ارسال نظر درباره این مقاله : نام کاربری یا پست الکترونیک شما:
CAPTCHA

بازنشر اطلاعات
Creative Commons License این مقاله تحت شرایط Creative Commons Attribution-NonCommercial 4.0 International License قابل بازنشر است.

کلیه حقوق این وب سایت متعلق به دانشگاه علم و صنعت ایران می باشد.

طراحی و برنامه نویسی : یکتاوب افزار شرق

© 2024 CC BY-NC 4.0 | Iran University of Science & Technology

Designed & Developed by : Yektaweb