An Assessment on Permeability and Grout Take of Limestone: A Case Study at Mut Dam, Karaman, Turkey

Kayabaşı, Ali; Gökçeoğlu, Candan

doi:10.3390/w11122649

Cited by 6 publications

(4 citation statements)

References 26 publications

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“…In geotechnical practice, multiple regression analyses have been widely used. For example, multiple regression analyses were used to estimate the UCS [19,58,60,71], predict rock mass permeability [30,32], predict the deformation modulus of rock masses [4,31] and predict the TBM performance [22,57]. In this study, a series of simple regression analyses are performed before multiple regression analyses to check the multicollinearity.…”

Section: Multiple Regression Analysesmentioning

confidence: 99%

Assessment of rate of penetration of a tunnel boring machine in the longest railway tunnel of Turkey

Gökçeoğlu

2021

SN Appl. Sci.

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One of the most important issues in tunnels to be constructed with tunnel boring machines (TBMs) is to predict the excavation time. Excavation time directly affects tunnel costs and feasibility. For this reason, studies on the prediction of TBM performance have always been interesting for tunnel engineers. Therefore, the purpose of the study is to develop models to predict the rate of penetration (ROP) of TBMs. In accordance with the purpose of the study, a new database including 5334 cases is obtained from the longest railway tunnel of Turkey. Each case includes uniaxial compressive strength, Cerchar Abrasivity Index, α angle, weathering degree and water conditions as input or independent variables. Two multiple regression models and two ANN models are developed in the study. The performances of the ANN models are considerably better than those of the multiple regression equations. Before deep tunnel construction in a metamorphic rock medium, the ANN models developed in the study are reliable and can be used. In contrast, the performances of the multiple regression equations are promising, but they predict lower ROP values than the measured ROP values. Consequently, the prediction models for ROP are open to development depending on the new data and new prediction algorithms.

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Section: Multiple Regression Analysesmentioning

confidence: 99%

Assessment of rate of penetration of a tunnel boring machine in the longest railway tunnel of Turkey

Gökçeoğlu

2021

SN Appl. Sci.

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“…Data collected from [1,2,17] including Rock Quality Designation (RQD), Q-system (Q), Geological Strength Index (GSI) [18], Joint Spacing (JS), Joint Aperture (a), Second Permeability Index (SPI) [19] were used as input parameters (Figure 1b). Packer tests were performed in the field to record Lugeon value for each borehole with variation in depth and location.…”

Section: Data Collectionmentioning

confidence: 99%

“…Hydraulic conductivity K (HC) is an important parameter when choosing grout material and grout intake volume [1,2]. Generally, HC of rock mass is measured from the most common and appropriate Packer test [3] (or called Lugeon permeability test).…”

Section: Introductionmentioning

confidence: 99%

Estimating the Hydraulic Conductivity of jointed rock mass using Genetic Programming

Doan

Karakus

Nguyen

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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Ground engineering through injection of cementitious grouts or polymer resins is an integral part of fractured rock mass stabilisation by improving its maximum load carrying capacity, stiffness, cohesiveness and reduction in permeability. To optimise product delivery and achieve a high penetrability of product into the rock mass, it is imperative to understand the permeability characteristics or hydraulic conductivity of the rock mass. The hydraulic conductivity of the rock mass also dictates the type of polyurethane (PU) or cement-based products to be injected. For this purpose, a Packer test has to be conducted to measure the hydraulic conductivity of a rock formation which is an expensive and time-consuming process. Alternatively, a number of empirical methods that use rock mass classifications and rock joint properties Rock Quality Designation (RQD), Q-system (Q), Geological Strength Index (GSI), Joint Spacing (JS), Joint Aperture (a) proposed by a number of researchers that are capable of predicting hydraulic conductivity (HC). In this research, a predictive model between rock mass properties and HC is proposed using new approach - genetic programming (GP). For this purpose, a database of rock mass parameters including RQD, Q, GSI, JS, Joint Aperture (a), Second Permeability Index (SPI) and Packer test results available in the literature is established. The database is split into randomly selected training and testing sets. To assess the fitting quality, the sum of the absolute difference is used, while maximum depth on trees is set to control the bloat of the model. The performance is assessed with four statistical criteria and three GP models using different input combinations are proposed. These models have been converted into simple mathematical equations to calculate HC based on collected input data. In summary, two out of three models have successfully predicted HC with high correlation to the actual HC (R2 of testing sets ≈ 0.92). Therefore, this study has shown the feasibility of applying GP models into future prediction of HC for the initial phase of rock grouting design.

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“…Another case used to compare the proposed fit for GSI is based on the data published by Kayabasi and Gokceoglu, C. (2019). In this sample, the RQD values are presented, which vary between 10% and 100% while the GSI sample varies between 26 and 56 points.…”

Section: Validation Processmentioning

confidence: 99%

Proposal of a new method for calculating GSI

Vivanco¹,

Avendaño²

2022

Mining Science

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Rock mass classification systems are simple but valuable tools for the qualitative and quantitative classification of rock masses and for the planning of the fortification of mining excavations. Unfortunately, in the prefeasibility phase, not all the information needed for a preliminary project evaluation is always available, and one of the few available information is the RQD, however, although it is very necessary to determine the GSI to analyze the failure criteria, it is difficult to obtain at this stage of the project. Although several correlations between the different classification systems have been identified, the most abundant ones are those relating GSI as a function of RMR and as a function of Barton's Q. As for GSI relationships as a function of RQD, only three recent relationships are available: Hoek et al. ( 2013), Santa et al. (2019), andXia et al. (2022). Therefore, this study presents a correlational analysis of the GSI and RQD classification systems, using robust nonparametric statistics, with the aim of determining an expression to estimate GSI in the field. Among the results, it is highlighted that better GSI prediction results are obtained when 25% < RQD ≤ 87%, with a maximum error of ±14 points, improving the estimation accuracy by 62% with respect to current proposals. Despite the above, the difficulty of interpreting more accurately the specific geological characteristics of each rock mass remains.

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An Assessment on Permeability and Grout Take of Limestone: A Case Study at Mut Dam, Karaman, Turkey

Cited by 6 publications

References 26 publications

Assessment of rate of penetration of a tunnel boring machine in the longest railway tunnel of Turkey

Assessment of rate of penetration of a tunnel boring machine in the longest railway tunnel of Turkey

Estimating the Hydraulic Conductivity of jointed rock mass using Genetic Programming

Proposal of a new method for calculating GSI

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