Investigating shear behaviour of fibreglass rock bolts reinforcing infilled discontinuities for various pretension loads

Gregor, Peter; Mirzaghorbanali, Ali; McDougall, Kevin; Aziz, Naj; Shokri, Behshad Jodeiri

doi:10.1139/cgj-2022-0619

Can. Geotech. J.

2024

DOI: 10.1139/cgj-2022-0619

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Investigating shear behaviour of fibreglass rock bolts reinforcing infilled discontinuities for various pretension loads

Peter Gregor

Ali Mirzaghorbanali

Kevin McDougall

et al.

Abstract: In this paper, eight shear tests were carried out utilising a double shear with infilled shear interfaces after determining an appropriate experimental design and modified testing scheme. For this, two rock bolts, 20-tonne and 30-tonne, were tested with modified double shear testing apparatus at different pretension loads. The infilled test scheme was conducted with 5mm thick sandy clay infilled shear interfaces. A 40MPa small aggregate concrete was used for all samples to simulate the host rock. Based on the … Show more

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Cited by 3 publications

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Predicting axial-bearing capacity of fully grouted rock bolting systems by applying an ensemble system

Hosseini,

Jodeiri Shokri,

Mirzaghorbanali

et al. 2024

Soft Comput

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In this paper, the potential of the five latest artificial intelligence (AI) predictive techniques, namely multiple linear regression (MLR), multi-layer perceptron neural network (MLPNN), Bayesian regularized neural network (BRNN), generalized feed-forward neural networks (GFFNN), extreme gradient boosting (XGBoost), and their ensemble soft computing models were evaluated to predict of the maximum peak load (PL) and displacement (DP) values resulting from pull-out tests. For this, 34 samples of the fully cementitious grouted rock bolts were prepared and cast. After conducting pull-out tests and building a dataset, twenty-four tests were randomly considered as a training dataset, and the remaining measurements were chosen to test the models’ performance. The input parameters were water-to-grout ratio (%) and curing time (day), while peak loads and displacement values were the outputs. The results revealed that the ensemble XGBoost model was superior to the other models. It was because having higher values of R2 (0.989, 0.979) and VAF (99.473, 98.658) and lower values of RMSE (0.0201, 0.0435) were achieved for testing the dataset of PL and DP’ values, respectively. Besides, sensitivity analysis proved that curing time was the most influential parameter in estimating values of peak loads and displacements. Also, the results confirmed that the ensemble XGBoost method was positioned to predict the axial-bearing capacity of the fully cementitious grouted rock bolting system with extreme performance and accuracy. Eventually, the results of the ensemble XGBoost modeling technique suggested that this novel model was more economical, less time-consuming, and less complicated than laboratory activities.

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Predicting axial-bearing capacity of fully grouted rock bolting systems by applying an ensemble system

Hosseini,

Jodeiri Shokri,

Mirzaghorbanali

et al. 2024

Soft Comput

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Predicting grout’s uniaxial compressive strength (UCS) for fully grouted rock bolting system by applying ensemble machine learning techniques

Hosseini,

Entezam,

Jodeiri Shokri

et al. 2024

Neural Comput & Applic

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This study proposes a novel system for accurately predicting grout’s uniaxial compressive strength (UCS) in fully grouted rock bolting systems. To achieve this, a database comprising 73 UCS values with varying water-to-grout (W/G) ratios ranging from 22 to 42%, curing times from 1 to 28 days, the admixture of fly ash contents ranging from 0 to 30%, and two Australian commercial grouts, Stratabinder HS, and BU-100, was built after conducting comprehensive series of experimental tests. After building the dataset, a metaheuristic technique, the jellyfish search (JS) algorithm was employed to determine the weight of base models in the ensemble system. This system combined various data and modelling techniques to enhance the accuracy of the UCS predictions. What sets this technique apart is the comprehensive database and the innovative use of the JS algorithm to create a weighted averaging ensemble model, going beyond traditional methods for predicting grout strength. The proposed ensemble model was called the weighted averaging ensemble model (WAE-JS), in which the obtained results of several soft computing models such as multi-layer perceptron (MLP), Bayesian regularized (BR) neural networks, generalized feed-forward (GFF) neural networks, classification and regression tree (CART), and random forest (RF) were weighted based on JS and the new results were then generated. Eventually, the result of WAE-JS was compared to other models, including MLP, BR, GFF, CART, and RF, based on some statistical parameters, such as R-squared coefficients, RMSE, and VAF as indices for evaluating the performance and capability of the proposed model. The results suggested the superiority of the ensemble WAE-JS system over the base models. In addition, the proposed WAE-JS model effectively improved the predicting accuracy achieved from the MLP, BR, GFF, CART, and RF. Furthermore, the sensitivity analysis revealed that the W/G had the most significant impact on the grout’s UCS values.

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Analysing Double Shearing Mechanism in Fiberglass Rock Bolting Systems: a Comprehensive Analytical Model and Numerical Simulation Approach

Gregor,

Mirzaghorbanali,

McDougall

et al. 2024

Geotech Geol Eng

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An analytical method and numerical simulation were developed to investigate the shear performance of fiberglass rock bolts (20-tonne and 30-tonne) by conducting sixteen double-shearing tests with both clean and infilled shear interfaces. Following the preparation of the required samples, each test set-up was subjected to different ranges of pretension values. The infilled scenario involved 5 mm thick sandy clay infilled shear interfaces. The results of the double shearing tests unveiled that as pretension increased, so did the confining pressures at the shear interfaces for both clean and infilled joints. Also, an analytical model was developed utilising the Fourier transform, energy balance theory, and linear elastic theory. The result was an empirical relationship that could determine the double shear performance of fibreglass rock bolts in close agreement with the experimental data. Coefficients were incorporated to facilitate model calibration and tuning. Eventually, fast Lagrangian analysis of continua (FLAC) three-dimensional (3D) modelling was utilised to conduct numerical simulations of fibreglass rock bolts subjected to double shearing scenarios. The numerical model was calibrated against experimental data and then extended to conduct a sensitivity analysis on fibreglass rock bolts subjected to double shear test setup variations. Scenarios included rock bolt installation angles, shearing rates, and various host rock strengths. The results revealed that increasing the shear speed from the experimental test baseline yielded substantial displacement increases in the post-failure residual performance of the rock bolts. Changing the installation angle resulted in greater peak shear forces and extended residual zones. The least significant impacts were observed when changing the host rock UCS, suggesting neither rock bolt was drastically impacted by weak or strong host rocks.

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Investigating shear behaviour of fibreglass rock bolts reinforcing infilled discontinuities for various pretension loads

Cited by 3 publications

References 16 publications

Predicting axial-bearing capacity of fully grouted rock bolting systems by applying an ensemble system

Predicting axial-bearing capacity of fully grouted rock bolting systems by applying an ensemble system

Predicting grout’s uniaxial compressive strength (UCS) for fully grouted rock bolting system by applying ensemble machine learning techniques

Analysing Double Shearing Mechanism in Fiberglass Rock Bolting Systems: a Comprehensive Analytical Model and Numerical Simulation Approach

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