Characterization of mechanical and bonding properties of anchoring resins under elevated temperature

Nourizadeh, Hadi; Mirzaghorbanali, Ali; McDougall, Kevin; Jeewantha, L. H. Janitha; Craig, Peter; Motallebiyan, Amin; Shokri, Behshad Jodeiri; Rastegarmanesh, Ashkan; Aziz, Naj

doi:10.1016/j.ijrmms.2023.105506

Cited by 9 publications

(2 citation statements)

References 43 publications

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“…Moreover, these behaviours are controlled by some significant parameters, such as W/G ratios, CT, additives, proper mixing of grout components, and the type of grouts. As a result, certain researchers focused on investigating the impact of the strength properties of grouts [10,38,43,58,66,[78][79][80][81][82][83][84][85][86][87][88][89][90]. For instance, Hyett, Bawden [58] found that the bearing capacity of the fully grouted cable bolting system increased by 50 to 70% in lower W/G ratios under 40%.…”

Section: Effects Of Grout Mechanical Characteristics On the Axial Per...mentioning

confidence: 99%

“…Rock bolting systems can be further categorised into three main types based on how the internal fixtures interact with the rock and the bolt [8]: continuously mechanically coupled (CMC), continuously frictionally coupled (CFC), and discretely mechanically or frictionally coupled (DMFC). Based on the interlocking mechanism between the bolt and the surrounding rock, three conventional types of rock bolts are commonly used [9], namely (a) mechanically anchored bolts: these bolts rely on a mechanical anchoring mechanism to achieve a strong connection with the rock; (b) frictional bolts: these bolts utilise friction between the rock and the bolt surface to create a stable bond; (c) fully grouted rock bolting systems (FGRBSs): in this type, a cementitious grout or resin fills the space between the rock and the reinforced element (bolt), creating a continuous medium and strong bond [10,11] (Figure 1). This article exclusively investigates FGRBSs, categorised as CMC system according to the classification system.…”

Section: Introductionmentioning

confidence: 99%

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Axial Load Transfer Mechanism in Fully Grouted Rock Bolting System: A Systematic Review

Jodeiri Shokri,

Mirzaghorbanali,

Nourizadeh

et al. 2024

Applied Sciences

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The main objective of implementing primary ground-controlling methods, such as applying rock bolting systems, is to increase the strength of surrounding rock mass. Among all rock bolting systems, fully grouted rock bolting systems are the most popular and reliable retaining systems due to their simplicity, availability of materials, ease of installation in the field, and cost-effectiveness. While these types of rock bolts experience both axial and shear forces, understanding their response to axial loads remains complex and dependent on several factors. Extensive research has addressed the overall behaviour of the fully grouted rock bolting system, but a systematic review of the axial load transfer mechanism and its impact on overall performance is lacking. This study addresses this gap by employing a bibliometric analysis of 77 peer-reviewed publications to explore the current state of knowledge regarding the axial load transfer mechanism in fully grouted rock bolting systems. The analysis identifies influential journals, publishers, researchers, highly cited articles, and emerging keywords within this field. Furthermore, it reveals three key parameters significantly impacting the axial behaviour: (a) rock mass and boundary conditions, (b) mechanical behaviours of the grouts, and (c) the geometry and surface profile of the rock bolt. These parameters are subsequently discussed in detail, highlighting their influence on the axial performance of the system. Finally, this article concludes by suggesting promising directions for future research.

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Section: Effects Of Grout Mechanical Characteristics On the Axial Per...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Axial Load Transfer Mechanism in Fully Grouted Rock Bolting System: A Systematic Review

Jodeiri Shokri,

Mirzaghorbanali,

Nourizadeh

et al. 2024

Applied Sciences

Self Cite

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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 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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Characterization of mechanical and bonding properties of anchoring resins under elevated temperature

Cited by 9 publications

References 43 publications

Axial Load Transfer Mechanism in Fully Grouted Rock Bolting System: A Systematic Review

Axial Load Transfer Mechanism in Fully Grouted Rock Bolting System: A Systematic Review

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

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