Rassoul Ajalloeian, Ph.D. ,

Porosity is among the basic physical parameters widely used in evaluating the strength characteristics of building stones. Based on the geometric shape of the stone specimen, there are two procedures for determining the porosity: 1) measurements on specimens with regular shapes and 2) measurements on specimens with irregular shapes. The present study correlates the strength characteristics of stones and porosities measured from specimens with regular and irregular shapes. To this end, samples of carbonate stones were collected from different locations in Iran. The porosity of the samples was measured for specimens with regular (nr) and irregular (nir) shapes. Next, the strength characteristics of the samples, including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), and point load index (PLI), were determined. Results revealed that for all samples, nr has lower values than nir. Based on the scanning electron microscope (SEM) micrographs, this difference is due to the filling of some pore spaces of the specimens with regular shapes during their preparation by the coring machine. UCS, BTS, and PLI(50) were correlated with nr and nir using simple regression analyses. The accuracy of the correlation equations was compared based on their determination coefficient (R2) and diagonal line (1:1) measures. The findings indicated that nir provides higher accuracy than nr in evaluating the UCS, BTS, and PLI(50) of the samples. The effect of dry density (ρd) on the correlations between UCS, BTS, and PLI(50) with nir was investigated using multiple regression analyses. According to the results, ρd has a positive role in the prediction accuracy of the UCS, BTS, and PLI(50). The validity and accuracy of multiple regression equations were verified according to the statistics criteria and the published data by various researchers. Using these equations obviates the need to perform the UCS, BTS, and PLI(50) tests as time-consuming and laborious efforts. © 2024 Elsevier Ltd

Development of a hard rock TBM performance prediction model using RMR input parameters

( Article . )

Ajalloeian, R., Dardashti a., A.F., Rostami j., , Hassanpour j., J., Salimi a., , Dardashti a., A.F., Rostami j., , Ajalloeian, R., Hassanpour j., J., Salimi a.,

2025 29th International Computer Conference, Computer Society of Iran, CSICC 2025pp. 2995-3004

Evaluating the Effects of Mineral Grain Size and Mineralogical Composition on the Correlated Equations Between Strength and Schmidt Hardness of Granitic Rocks

( Article . )

Ajalloeian, R., Jamshidi, A., Khorasani, R.

Geotechnical and Geological Engineering (09603182)42(1)pp. 675-685

Performance Predictions of Hard Rock TBM in Subcritical Cutter Load Conditions

( Article . )

Dardashti a., A.F., Ajalloeian, R., Rostami j., , Hassanpour j., J., Salimi a.,

Rock Mechanics and Rock Engineering (07232632)57(1)pp. 739-755

In mechanized tunneling, the ability to accurately predict the performance of the tunnel boring machine (TBM) is critical, because of its impacts on the project schedule and cost. A substantial amount of research has been conducted on predicting the penetration rate of TBMs in rocks but sometimes the actual cutting process of the disc cutter has been ignored, as most of the available models have been developed based on the assumption that the chipping process is the dominant mode of rock breakage. This might be true in many cases, but there are also situations where tensile fractures do not extend long enough to form chips, instead the tracks get deeper, and fines and rock powder are generated instead of chipping. This situation happens when the cutter load is insufficient to form chips, or so called sub-critical loading occurs. Two of the recent tunneling projects where subcritical cutter load has been the dominant mode are the southern extension of Tehran subway Line 6 (SEL6), and the southern lot of Kerman water conveyance tunnel (KrWCT). The issue in SEL6 was insufficient thrust of the machine and in KrWCT the high strength of the rock. Evaluation of the FPIs in SEL6 shows that the FPIs were less than 100 (kN/cutter/mm/rev) and the prediction errors of the existing models are low. However, in KrWCT project in rocks with higher strength and higher FPIs, the prediction errors are considerable. This indicates that the common models cannot offer an accurate estimate of the performance of TBM in subcritical loading in rocks with high strength. This study attempts to use statistical and machine learning methods to develop a new relation for TBM performance prediction in such conditions based on actual machine operating data and rock engineering geological features. New equations are introduced for the estimation of FPI in subcritical loading conditions to achieve more reliable and accurate prediction of TBM penetration rate. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.

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