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توضیحات
ABSTRACT
The uniaxial compressive strength of intact rocks is extensively used in many rock engineering projects. High-quality core samples are required for the uniaxial compressive strength determinations. However, such core samples cannot always be obtained from weak rocks. For this reason, the predictive models are often employed to estimate indirectly. In present study, various models have been developed in order to predict uniaxial compressive strength. For this purpose different tests were accomplished. The root mean square error index was calculated as 6.1 from the neuro-fuzzy model and 13.63 from the multiple regression model. As a result, performance index reveled that the neuro-fuzzy exhibited a very high prediction capacity
INTRODUCTION
Rock engineers widely use the uniaxial compressive strength (UCS) of rocks in designing tunnels, foundations and slopes Measuring UCS has been standardized by both the ASTM and ISRM and it requires specimens prepared accurately. But it is often extremely difficult and time consuming to obtain such samples from weak, highly fractured and thinly bedded rocks. Therefore, some predictive models considering simple index parameters such as schmidt hammer, point load index, sound velocity, and physical properties were investigated by many researchers because these index tests require less or no sample preparation when compared with the UCS test. Also, they can be used easily in the field. Soft computing methods like ANN (Artificial Neural Network) or ANFIS (Adaptive Nero-Fuzzy Inference System) can be used so as to model a system that lack of complete or computationally feasible analytic description. The fuzzy-rule based approach introduced by Zadeh , Fuzzy-rule based modeling is a qualitative modeling scheme where the system behavior is described using a natural language . During the last decades there were so many applications of fuzzy systems in geotechnical engineering. The main objective of this study is to compare linear and multiple regression models compared with ANFIS (Adaptive Nero-Fuzzy Inference System) predictive model. For this purpose, a total of 16 different rock types have been chosen from different mining and civil engineering projects of Iran and were subjected to testes based on ISRM suggested methods.
Year: 2011
Publisher : Sixth National Congress on Civil Engineering
By : R. Noorani , H. kordi
File Information: English Language/ 8 Page / size: 175 KB
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سال : 1390
ناشر :ششمین کنـگره ملی مهنـدسی عمـران
کاری از : رضا نورانی , حسین کردی
اطلاعات فایل : زبان انگلیسی / 8 صفحه / حجم : KB 175
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