A neuro-fuzzy model for modulus of deformation of jointed rock masses

✍ Scribed by Candan Gokceoglu; Ertan Yesilnacar; Harun Sonmez; Ali Kayabasi

• Publisher: Elsevier Science
• Year: 2004
• Tongue: English
• Weight: 242 KB
• Volume: 31
• Category: Article
• ISSN: 0266-352X
• DOI: 10.1016/j.compgeo.2004.05.001

✦ Synopsis

Use of indirect estimation methods for some rock mass parameter is considered traditionally in the rock mechanics applications. Generally, the regression based-statistical methods are used to develop an empirical equation. However, new techniques such as artificial neural networks, fuzzy inference systems or neuro-fuzzy systems were employed in recent years. In this study, construction of a neuro-fuzzy system to estimate the deformation modulus of rock masses is aimed, because this modulus has a crucial importance for many design approaches in rock engineering. For the purpose, a database including 115 data sets was employed and a neuro-fuzzy system consisting of two inputs, one output and three layers was constructed. After learning process, total 18 if-then fuzzy rules were obtained. The performance values such as RMSE, VAF, absolute error and coefficient of cross-correlation were calculated and, the constructed neuro-fuzzy model exhibited a high performance according to the performance indices.