Assessment of Rock Strength, Porosity and Los Angeles Abrasion Value through Artificial Neural Networks and Ordinary Least Square Methods

Publish Year: 1394
نوع سند: مقاله کنفرانسی
زبان: English
View: 564

This Paper With 9 Page And PDF Format Ready To Download

  • Certificate
  • من نویسنده این مقاله هستم

استخراج به نرم افزارهای پژوهشی:

لینک ثابت به این Paper:

شناسه ملی سند علمی:

ICESCON01_0382

تاریخ نمایه سازی: 25 بهمن 1394

Abstract:

Unconfined Compressive Strength (UCS) is the most widely used design parameter in rock and geotechnical engineering and its role in analysis of geotechnical problems is crucial. Laboratory test is the most reliable and direct method for determining UCS, but this method is time-consuming and expensive. But, UCS could be estimated using correlations between UCS and other parameters of rock that are determined by simple and not expensive test methods. In this research attempt is made to study the correlation of the UCS with Los Angeles Abrasion Value and Porosity and estimate the UCS through Ordinary Least Square (OLS) and Multi-layer Perception (MLP) of artificial neural network (ANN) methods. Authors are presented eleven models of these correlations to estimate UCS. Results obtained from these models indicate that the ANN not only performs better than OLS but also provides acceptable and reliable outcomes with respect to the predicted objectives' materialization. The average correlation (R) of results are presented 6.0 by OLS and 6.6 by ANNS

Keywords:

Unconfined compressive strength , Los Angeles abrasion value , porosity , ANN , MLP

Authors

Mojtaba Kiani

M.Sc. Student

Morteza Hashemi

Ph.D.Engineering Geology group, faculty of sciences, University of Isfahan, Isfahan, Iran

Rassou Ajalloeian

Prof.Engineering Geology group, faculty of sciences, University of Isfahan, Isfahan, Iran

مراجع و منابع این Paper:

لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :