Ripping production prediction in different weathering zones according to field data

• Main Authors: Tonnizam Mohamad, E., Jahed Armaghani, D., Ghoroqi, M., Yazdani Bejarbaneh, B., Ghahremanians, T., Abd. Majid, M. Z., Tabrizi, O.
• Format: Article
• Published: Springer International Publishing 2017
• Subjects: TA Engineering (General). Civil engineering (General)
• Online Access: http://eprints.utm.my/id/eprint/76202/; https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019140055&doi=10.1007%2fs10706-017-0254-4&partnerID=40&md5=476ef1ac1ebf90fc42da90579e0f5fb9

In response to the environmental restrictions and the blasting problems, ripping method as a surface excavation method is the most commonly-used in construction of many civil engineering systems. So, it is essential to provide a more applicable rippability model that can effectively predict ripping production (Q) in the field. This paper presents several new models/equations for prediction of Q in diverse weathering zones (grade from II to V) based on field observations and in situ tests. To do this, four sites in Johor state, Malaysia were selected and a total of 123 direct ripping tests were carried out on two types of sedimentary rocks, namely, sandstone and shale. Based on literature’s suggestions and possible conducted field works, point load strength index, sonic velocity, Schmidt hammer rebound number and joint spacing were chosen to estimate Q in different weathering zones. Then, simple and multiple regression analyses, namely linear multiple regression (LMR) and non-linear multiple regression (NLMR) were performed to predict Q. The simple regression analysis generally showed an acceptable and meaningful correlation between the Q and input variables. Additionally, a range of 0.582–0.966 was obtained for coefficient of determination (R2) values of developed LMR models while this range was observed from 0.586 to 0.949 for proposed NLMR models. As a result, both the LMR and NLMR models deliver almost the same predictive performance in estimating the Q for various weathering zones. Nevertheless, in most of the cases, NLMR models can provide higher performance prediction in estimating Q compared to LMR models.