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Jamal Rostami

Researcher at Colorado School of Mines

Publications -  61
Citations -  2400

Jamal Rostami is an academic researcher from Colorado School of Mines. The author has contributed to research in topics: Rock mass classification & Disc cutter. The author has an hindex of 19, co-authored 48 publications receiving 1730 citations. Previous affiliations of Jamal Rostami include Mechanics' Institute & Pennsylvania State University.

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Disc cutting tests in Colorado Red Granite: Implications for TBM performance prediction

TL;DR: In this paper, a series of full-scale laboratory disc cutting tests was conducted with a single disc cutter (432 mm diameter and a constant cross-section profile) and a single rock type (a coarse-grained red granite).
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A new hard rock TBM performance prediction model for project planning

TL;DR: In this article, the authors proposed a new TBM performance prediction model based on a database of actual machine performance from different hard rock TBM tunneling projects, and analyzed the available data and offer new equations using statistical methods, relationships between different geological and TBM operational parameters were investigated.
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Performance prediction of hard rock TBM using Rock Mass Rating (RMR) system

TL;DR: In this paper, the authors used multivariate linear, non-linear and polynomial regression analyses of RMR input parameters to predict the TBM field penetration index (FPI).
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TBM Performance Analysis in Pyroclastic Rocks: A Case History of Karaj Water Conveyance Tunnel

TL;DR: In this paper, the authors present an overview of the Karaj Water Conveyance Tunnel (KWCT) operation and review the results of field performance of the machine, including analysis of available data including geological and geotechnical information and machine operational parameters, actual penetration and advance rates compared to the estimated machine performance using prediction models, such as CSM, NTNU and QTBM.
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Study of various models for estimation of penetration rate of hard rock TBMs

TL;DR: In this paper, the authors present a review of the capabilities of some of the more commonly used TBM performance prediction models and evaluate the accuracy of these models to support an improved level of predictive accuracy in penetration rate estimating.