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Abstract The ground vibration caused by rock blasting is an extremely hazardous outcome of the blasting operation. Blasting activity has detrimental effects on both the ecology and the human population living in proximity to the area. Evaluating the magnitude of blasting vibrations requires careful evaluation of the peak particle velocity (PPV) as a fundamental and essential parameter for quantifying vibration velocity. Therefore, this study employs models using the relevance vector machine (RVM) approach for predicting the PPV resulting from quarry blasting. This investigation utilized the conventional and optimized RVM models for the first time in ground vibration prediction. This work compares thirty-three RVM models to choose the most efficient performance model. The following conclusions have been mapped from the outcomes of the several analyses. The performance evaluation of each RVM model demonstrates each model achieved a performance of more than 0.85 during the testing phase, there was a strong correlation observed between the actual ground vibrations and the predicted ones. The analysis of performance metrics (RMSE = 21.2999 mm/s, 16.2272 mm/s, R = 0.9175, PI = 1.59, IOA = 0.8239, IOS = 0.2541), score analysis (= 93), REC curve (= 6.85E−03, close to the actual, i.e., 0), curve fitting (= 1.05 close to best fit, i.e., 1), AD test (= 11.607 close to the actual, i.e., 9.790), Wilcoxon test (= 95%), Uncertainty analysis (WCB = 0.0134), and computational cost (= 0.0180) demonstrate that PSO_DRVM model MD29 outperformed better than other RVM models in the testing phase. This study will help mining and civil engineers and blasting experts to select the best kernel function and its hyperparameters in estimating ground vibration during rock blasting project. In the context of the mining and civil industry, the application of this study offers significant potential for enhancing safety protocols and optimizing operational efficiency.

Abstract Tunnel Boring Machines (TBMs) are pivotal in underground projects like subways, highways, and water supply tunnels. Predicting and monitoring jack speed and torque is crucial for optimizing TBM excavation efficiency. Conventionally, skilled operators manually adjust numerous tunnelling parameters to regulate the machine's progress. In contrast, machine learning (ML) algorithms offer a promising avenue where computers learn from operator actions to establish parameter relationships autonomously. This study introduces an innovative approach to enhancing operator monitoring and TBM data comprehension. A robust correlation between TBM operator behaviour and TBM logged data is established by leveraging an Optuna-assisted ML methodology—the research light on the intricate dynamics influencing TBM advance rate parameters. Operational data is collected from micro slurry tunnel boring machine (MSTBM) umbrella support excavations. The proposed framework harnesses Optuna, an advanced hyperparameter optimization platform, to dynamically refine jack speed and torque settings. Through meticulous analysis of the interplay between TBM operator decisions and real-time logged data, the AI model discerns patterns, empowering informed decision-making. Using Optuna, a range of models, including random forest (RF), K-nearest neighbours (kNN), decision tree (DT), XGBoost, Support Vector Machine (SVM), and Artificial Neural Network (ANN) were automatically compared and tuned. The best model's (RF) performance is evaluated through a correlation coefficient (R2) of 96%, mean squared error (MSE) of 119.7, and mean absolute error (MAE) of 4.42 for jack speed decision making while 83% of R2, MSE of 0.62, and MAE of 0.42 for the torque decision making. This intelligent model can assist the TBM operator in making decisions about TBM control.

Abstract Recent and past studies mainly focus on reducing the dead weight of structure; therefore, they considered lightweight aggregate concrete (LWAC) which reduces the dead weight but also affects the strength parameters. Therefore, the current study aims to use varied steel wire meshes to investigate the effects of LWAC on mechanical properties. Three types of steel wire mesh are used such as hexagonal (chicken), welded square, and expanded metal mesh, in various layers and orientations in LWAC. Numerous mechanical characteristics were examined, including energy absorption (EA), compressive strength (CS), and flexural strength (FS). A total of ninety prisms and thirty-three cubes were made. For the FS test, forty-five 100 × 100 × 500 mm prism samples were poured, thirty-three 150 × 150 × 150 mm cube samples were made, and forty-five 400 × 300 × 75 mm EA specimens were costed for fourteen days of curing. The experimental findings demonstrate that the FS was enhanced by adding additional forces that spread the forces over the section. One layer of chicken, welded, and expanded metal mesh enhances the FS by 52.96%, 23.76%, and 22.2%, respectively. In comparison to the remaining layers, the FS in a single-layer hexagonal wire mesh has the maximum strength, 29.49 MPa. The hexagonal wire mesh with a single layer had the greatest CS, measuring 36.56 MPa. When all three types of meshes are combined, the CS does not vary in this way and is estimated to be 29.79 MPa. In the combination of three layers, the chicken and expanded wire mesh had the most energy recorded prior to final failure, which was 1425.6 and 1108.7 J, whereas it was found the highest 752.3 J for welded square wire mesh. The energy absorption for the first layer with hexagonal wire mesh increased by 82.81% prior to the crack and by 88.34% prior to the ultimate failure. Overall, it was determined and suggested that hexagonal wire mesh works better than expanded and welded wire meshes.

Abstract This study is an attempt for comprehensive, combining experimental data with advanced analytical techniques and machine learning for a thorough understanding of the factors influencing the wear and cutting performance of multi-blade diamond disc cutters on granite blocks. A series of sawing experiments were performed to evaluate the wear and cutting performance of multi blade diamond disc cutters with varying diameters in the processing of large-sized granite blocks. The multi-layer diamond segments comprising the Iron (Fe) based metal matrix were brazed on the sawing blades. The segment’s wear was studied through micrographs and data obtained from the Field Emission Scanning Electron Microscopy (FESEM) and Energy Dispersive X-ray (EDS). Granite rock samples of nine varieties were tested in the laboratory to determine the quantitative rock parameters. The contribution of individual rock parameters and their combined effects on wear and cutting performance of multi blade saw were correlated using statistical machine learning methods. Moreover, predictive models were developed to estimate the wear and cutting rate based on the most significant rock properties. The point load strength index, uniaxial compressive strength, and deformability, Cerchar abrasivity index, and Cerchar hardness index were found to be the significant variables affecting the sawing performance.

Abstract In this study, efforts were made to incorporate the influence of discontinuities and failure modes of rock into the classification of rock masses. The past tectonic activities may create microfractures in the rock body therefore the failure moods have been determined carefully under uniaxial compression. The results of the discontinuity analysis, conducted through kinematic study, highlighted the significant impact of wedge failure on the failure of the rock mass. In correlating the geological strength index with rock mass rating, it was observed that joint volume played a negative role, whereas compressive strength played a positive role. These correlations are particularly applicable for a certain rock type, as the compressive strength is inherently dependent on the type of rock. The analysis of failure modes under uniaxial compression reveals that the dissipation energy coefficient initially undergoes rapid increase before reaching its minimum value at the failure stage. The microstructures of the rock effect significantly the elastic and dissipation energy characteristics. Specifically, the axial splitting failure mode emerges as predominant. Given the area's past tectonic activity, these results emphasize the impact of microfractures within the rock body. Relating the failure criteria with the chemical composition of rock types reveals that rocks abundant in SiO2, such as gabbronorite, tend to exhibit brittle failure. Additionally, a dominance of Al2O3 over Fe2O3 suggests a predisposition towards brittle failure, while an increased ratio of CaO to MgO implies increased susceptibility to compression.

The “Baganuur” lignite coal mine is one of the biggest open cast mines in Mongolia. However, there is a huge challenge in managing the stability of its internal dump, which prevents the proper operation of the mine and has an impact on the economy. To solve the internal dump slope stability problem, this study focused on incorporating the inherent mechanical properties of the rock material to build numerical models of the internal dump. By applying two software programs from Rocscience (Phase2 and Slide) and four different methods, the finite element method, the Bishop method, the Janbu simplified method, and the Spencer simplified method, the current and improved internal dump parameters were numerically simulated and analyzed. Based on the properties of the rock, the LEM and FEM were used to determine the parameters that could have an impact on the stability of the internal waste dump. The impacts of the internal dump height, dip angle, and safety berm on these parameters were studied. This study covers several analytical methods for calculating safety factors. Based on the results of the numerical simulation, it is determined that it is possible to increase the internal dump capacity by approximately 56% at a 50 m height and 28° dip angle and using a 15 m safety berm. Under similar conditions, this study presents an optimum SRF at 40 m height, 28° dip angle, and 5 m safety berm. Based on the numerical models, it is found that changes in the dip angle have a greater impact than changes in the dump height on the slope stability of an internal dump.

During tunnel boring machine (TBM) excavation, lithology identification is an important issue to understand tunnelling performance and avoid time-consuming excavation. However, site investigation generally lacks ground samples and the information is subjective, heterogeneous, and imbalanced due to mixed ground conditions. In this study, an unsupervised (K-means) and synthetic minority oversampling technique (SMOTE)-guided light-gradient boosting machine (LightGBM) classifier is proposed to identify the soft ground tunnel classification and determine the imbalanced issue of tunnelling data. During the tunnel excavation, an earth pressure balance (EPB) TBM recorded 18 different operational parameters along with the three main tunnel lithologies. The proposed model is applied using Python low-code PyCaret library. Next, four decision tree-based classifiers were obtained in a short time period with automatic hyperparameter tuning to determine the best model for clustering-guided SMOTE application. In addition, the Shapley additive explanation (SHAP) was implemented to avoid the model black box problem. The proposed model was evaluated using different metrics such as accuracy, F1 score, precision, recall, and receiver operating characteristics (ROC) curve to obtain a reasonable outcome for the minority class. It shows that the proposed model can provide significant tunnel lithology identification based on the operational parameters of EPB-TBM. The proposed method can be applied to heterogeneous tunnel formations with several TBM operational parameters to describe the tunnel lithologies for efficient tunnelling.

Abstract The investigation compares the conventional, advanced machine, deep, and hybrid learning models to introduce an optimum computational model to assess the ground vibrations during blasting in mining projects. The long short-term memory (LSTM), artificial neural network (ANN), least square support vector machine (LSSVM), ensemble tree (ET), decision tree (DT), Gaussian process regression (GPR), support vector machine (SVM), and multilinear regression (MLR) models are employed using 162 data points. For the first time, the blackhole-optimized LSTM model has been used to predict the ground vibrations during blasting. Fifteen performance metrics have been implemented to measure the prediction capabilities of computational models. The study concludes that the blackhole optimized-LSTM model PPV11 is highly capable of predicting ground vibration. Model PPV11 has assessed ground vibrations with RMSE = 0.0181 mm/s, MAE = 0.0067 mm/s, R = 0.9951, a20 = 96.88, IOA = 0.9719, IOS = 0.0356 in testing. Furthermore, this study reveals that the prediction accuracy of hybrid models is less affected by multicollinearity because of the optimization algorithm. The external cross-validation and literature validation confirm the prediction capabilities of model PPV11. The ANOVA and Z tests reject the null hypothesis for actual ground vibration, and the Anderson–Darling test rejects the null hypothesis for predicted ground vibration. This study also concludes that the GPR and LSSVM models overfit because of moderate to problematic multicollinearity in assessing ground vibration during blasting.

Ground vibration is one of the most hazardous outcomes of blasting. It has a negative impact both on the environment and the human population near to the blasting area. To evaluate the magnitude of blasting vibrations, it is important to consider PPV as a fundamental critical base parameter practice in terms of vibration velocity. This study aims to explore the application of different soft computing techniques, including a Gaussian process regression (GPR), decision tree (DT), and support vector regression (SVR), for the prediction of blast-induced ground vibration (PPV) in quarry mining. The three models were evaluated using classical mathematical evaluation metrics (R2, RMSE, MSE, MAE). The result shows that the GPR model achieves an excellent prediction result; with R2 = 0.94, RMSE = 0.0384, MSE = 0.0014, and MAE = 0.0265, it shows high accuracy in predicting PPV. The Shapley additive explanation (SHAP) results emphasize the importance of understanding the interactions between the various factors and their effects on the vibration assessment. The findings can inform the development of more sustainable and environmentally friendly models for predicting blasting vibrations. Using a GPR to simulate and predict blasting-induced ground vibrations is the study’s main contribution. The GPR can capture complicated, non-linear correlations in data, making it ideal for blast-induced ground vibrations, which are dynamic and nonlinear. By using a Gaussian process regression, we can help companies and researchers improve the safety and efficiency in blast-induced ground vibration environments.

Blast charge initiation procedures have a significant impact on both mining safety and production rates. In this study, the inventory benefit of an electric initiation system was investigated to assess its influence on both fragmentation and blast-induced damages. The WipFrag software was used to examine the size distribution and productivity of 12 small-scale blasts initiated by both nonelectric and electric detonators. All blast rounds were initiated with plain-type electric and NONEL detonators. The average burden, spacing, stemming length, and charge weight were, respectively, 0.85 m, 1.10 m, 0.66 m, and 1.1 kg. The results showed that the mesh through which 80% of the blast fragments passed for the electric blast was smaller than the mesh through which the material products from the NONEL blast passed. The results also demonstrated that the generated blast-induced ground vibration (PPV) from all blast rounds for electric blast varied from 0.4–1.2 mm/s and 80–105 dB, while that for nonelectric blast ranged from 0.05–0.2 mm/s and 72–95 dB. As a result, the electric blast initiation technique was found to produce good fragmentation, with a higher percentage of optimum fragment sizes on spec than nonelectrically initiated blasts.

This study introduces a novel method for analyzing vibration data related to drill bit failure. Our approach combines explainable artificial intelligence (XAI) with convolutional neural networks (CNNs). Conventional signal analysis methods, such as fast Fourier transform (FFT) and wavelet transform (WT), require extensive knowledge of drilling equipment specifications, which limits their adaptability to different conditions. In contrast, our method leverages XAI algorithms applied to CNNs to directly identify fault signatures from vibration signals. The signals are transformed into their frequency components and then employed as inputs to a CNN model, which is trained to detect patterns indicative of drill bit failure. XAI algorithms are then employed to generate attention maps, highlighting regions of interest in the CNN. By scrutinizing these maps, engineers can identify critical frequencies associated with drill bit failure, providing valuable insights for maintenance and optimization. This method offers a transparent and interpretable framework for analyzing vibration data, enabling informed decision-making and proactive maintenance strategies to enhance drilling efficiency and minimize downtime. The integration of XAI with CNNs facilitates a deeper understanding of the root causes of drill bit failure and improves overall drilling performance.

In mining operations that employ explosives and mineral processing, one of the important factors for efficient and low-cost operation is the fragmentation size distribution of rock after it has been blasted. Automatic scaling is a critical component of fragmentation size distribution measurement as it will directly determine the accuracy of the size estimation. In this study, we propose a method to create a system for creating a scaled 3D CG model, without the use of ground truth data such as GCPs (Ground Control Points), for the purpose of improving fragmentation size distribution measurement using positional data such as GNSS (Global Navigation Satellite System)-aided photogrammetry. We confirmed the validation of the method through an experimental evaluation of actual muckpiles. The results showed evidence of improving the scaling aspect of 3D fragmentation measurement systems without using GCPs or manual scales, specifically in surface mines where GNSS data are available.

Blasting is routinely carried out in urban quarry sites. Residents or houses around quarry sites are affected by the ground vibrations induced by blasting. Peak Particle Velocity (PPV) is used as a metric to measure ground vibration intensity. Therefore, many prediction models of PPV using experimental methods, statistical methods, and Artificial Neural Networks (ANNs) have been proposed to mitigate this effect. However, prediction models using experimental and statistical methods have a tendency of poor prediction accuracy. In addition, while prediction models using ANNs can produce a highly accurate prediction results, a large amount of measured data is necessarily collected. In an urban quarry site where the number of blastings is limited, it is difficult to collect a lot of measured data. In this study, a new PPV prediction method using Weighted Non-negative Matrix Factorization (WNMF) is proposed. WNMF is a method that approximates a non-negative matrix (including missing data) to the product of two low-dimensional matrices and predicts the missing data. In addition, WNMF is one of the unsupervised learning methods, so it can predict PPV regardless of the amount of data. In this study, PPV was predicted using measured data from 100 sites at the Mikurahana quarry site in Japan. As a result, the proposed method showed higher accuracy when using measured data at 60 sites rather than 100 sites, and the root mean square error for PPV prediction decreased from 0.1759 (100 points) to 0.1378 (60 points).

This research introduces an innovative technique for estimating the particle size distribution of muckpiles, a determinant significantly affecting the efficiency of mining operations. By employing deep learning and simulation methodologies, this study enhances the precision and efficiency of these vital estimations. Utilizing photogrammetry from multi-view images, the 3D point cloud of a muckpile is meticulously reconstructed. Following this, the particle size distribution is estimated through deep learning methods. The point cloud is partitioned into various segments, and each segment’s distinguishing features are carefully extracted. A shared multilayer perceptron processes these features, outputting scores that, when consolidated, provide a comprehensive estimation of the particle size distribution. Addressing the prevalent issue of limited training data, this study utilizes simulation to generate muckpiles and consequently fabricates an expansive dataset. This dataset comprises 3D point clouds and corresponding particle size distributions. The combination of simulation and deep learning not only improves the accuracy of particle size distribution estimation but also significantly enhances the efficiency, thereby contributing substantially to mining operations.

Purpose: To report the hemostatic effects of palliative radiation therapy (RT) for the prevention of blood transfusions (BT) in patients with advanced gastric cancer (AGC). Methods and Materials: Twenty-eight patients who received palliative three-dimensional conformal RT for hemostasis of gastric bleeding were retrospectively assessed in a study conducted in Japan. The median follow-up was 143.5 days. Changes in hemoglobin (Hb) levels were compared at the beginning of RT and four weeks later. Blood transfusion-free survival (BTFS) and overall survival (OS) were measured from the beginning of RT. Treatment toxicity was evaluated within 60 days of RT initiation. Results: No statistically significant decrease in Hb level was observed four weeks after RT. Twenty-eight patients did not receive BT within a month after RT, of whom three died within a month; 6/28 patients (21%) received BT at a median interval of 99.5 days following RT. The one-year BTFS and OS rates for all patients were 69% and 12%, respectively. The one-year BTFS was statistically significantly higher in 17 patients treated with a biologically effective dose (BED)10 of 39 Gy (30 Gy in 10 fractions) (78%) compared with six patients treated with a BED10 of 48 Gy (40 Gy in 20 fractions) (25%). Grade 1 and 2 nausea (n?=?11) and a Grade 2 increase in alanine aminotransferase (n?=?1) were observed. One patient died of Grade 5 hemorrhage. Conclusions: Palliative RT is an effective treatment to prevent BT for bleeding occurring within AGC. Specifically, a fractionation regimen of 30 Gy in 10 fractions (a BED10 of 39 Gy) has a more durable hemostatic effect and thus should be considered for better prognosis.

The technologies of the fourth industrial revolution have the potential to make zero harm possible for the first time in the history of mining. In the journey toward zero harm, rock stress monitoring systems are important for the risk management process. Although communication systems for underground mining have improved significantly over the past two decades, it remains difficult to achieve reliable-all-the-time wireless communication in ultra-deep level underground mines. The aim of this study is to explore and test a smart phone network for communicating sensor data from the underground production environment to the surface. In this paper, the evaluation and performance over distance of a wireless communication system is performed in underground mining environments. The wireless system transmits the data collected from a sensor installed in a narrow reef stope, horizontal tunnel, and vertical shaft area of a mock underground mine. The evaluation was performed using the received signal strength of a mobile receiver over distance. The path loss coefficients of the underground mining environment were then derived for the measurement areas. The results show that a communication speed of 80 Mbps was achieved in a 60 m range, thus, indicating the potential for the support of applications requiring higher data rates.

Rock blasting is one of the most common and cost-effective excavation techniques. However, rock blasting has various negative environmental effects, such as air overpressure, fly rock, and ground vibration. Ground vibration is the most hazardous of these inevitable impacts since it has a negative impact not only on the environment of the surrounding area but also on the human population and the rock itself. The PPV is the most critical base parameter practice for understanding, evaluating, and predicting ground vibration in terms of vibration velocity. This study aims to predict the blast-induced ground vibration of the Mikurahana quarry, using Bayesian neural network (BNN) and four machine learning techniques, namely, gradient boosting, k-neighbors, decision tree, and random forest. The proposed models were developed using eight input parameters, one output, and one hundred blasting datasets. The assessment of the suitability of one model in comparison to the others was conducted by using different performance evaluation metrics, such as R, RMSE, and MSE. Hence, this study compared the performances of the BNN model with four machine learning regression analyses, and found that the result from the BNN was superior, with a lower error: R = 0.94, RMSE = 0.17, and MSE = 0.03. Finally, after the evaluation of the models, SHAP was performed to describe the importance of the models’ features and to avoid the black box issue.

Purpose: As the prognosis of cancer patients deteriorates, secondary carcinogenesis after chemotherapy, especially secondary hematological malignancies, becomes a serious problem. However, information on the frequency and time of onset of secondary hematological malignancies and the risk of hematological malignancy with different drugs is scarce. This study aimed to evaluate the incidence of leukemia and myelodysplastic syndrome in patients with solid tumors, including breast, colon, gastric, pancreatic, small cell lung, non-small cell lung, esophageal, ovarian, cervical, and endometrial cancers. Methods: Using the United States Food and Drug Administration Adverse Event Reporting System, we analyzed the reporting rates, reporting odds ratios, and the reporting onset times of secondary leukemia and myelodysplastic syndrome for each drug used. Results: The leukemia reporting rates were higher in breast, small cell lung, ovarian, and endometrial cancers than in other cancers, and the myelodysplastic syndrome reporting rates were higher in ovarian and endometrial cancers than in other cancers. For each cancer type, the reporting odds ratios of cytocidal anticancer agents, such as taxanes, anthracyclines, alkylating agents, platinum, and topoisomerase inhibitors, were higher than those of other drugs. Alternatively, the reporting odds ratios of molecular targeted drugs and immune checkpoint inhibitors were not higher than those of other drugs. Approximately half of the cases of leukemia and myelodysplastic syndrome were reported within 1 to 4 years after chemotherapy. Conclusions: Our study clarified the risks of leukemia and myelodysplastic syndrome for several anticancer drugs in patients with solid tumors. Our data may aid in the assessment of the risks of secondary leukemia and myelodysplastic syndrome when medical oncologists, clinical pharmacists, and patients select chemotherapy regimens.

Abstract The genetic diversity and structure of Pulsatilla cernua, a continental‐grassland relict, were investigated using variations in chloroplast DNA (cpDNA) and microsatellites of nuclear DNA. In the analyses of three cpDNA regions, 17 haplotypes were found in 24 populations of P. cernua from Japan, Korea, and Russia. Although the route and time of migration between the continent of Asia and Japan could not be well resolved, the cpDNA haplotype network suggests the existence of several ancient lineages in Japan and a recent secondary migration from Japan to the continent. Microsatellite analyses did not indicate genetic structure among the Japanese populations, indicating the existence of gene flow across the distribution area until recently. These results indicate that the present fragmentation of P. cernua in Japan may reflect a rapid, recent reduction from a previously large, continuous distribution.

In this paper, the local correspondence between synthetic aperture radar (SAR) images and optical images is proposed using an image feature-based keypoint-matching algorithm. To achieve accurate matching, common image features were obtained at the corresponding locations. Since the appearance of SAR and optical images is different, it was difficult to find similar features to account for geometric corrections. In this work, an image translator, which was built with a DNN (deep neural network) and trained by conditional generative adversarial networks (cGANs) with edge enhancement, was employed to find the corresponding locations between SAR and optical images. When using conventional cGANs, many blurs appear in the translated images and they degrade keypoint-matching accuracy. Therefore, a novel method applying an edge enhancement filter in the cGANs structure was proposed to find the corresponding points between SAR and optical images to accurately register images from different sensors. The results suggested that the proposed method could accurately estimate the corresponding points between SAR and optical images.

Faced with a changing about global environmental issues, there are great expectations to optimize energy saving design in home appliance products. For effective energy saving design in Refrigeration System, integrated design method is highly needed to understand detailed physical behavior of a whole product based on careful study of the interactive influence of each device (e.g., Insulator layout design, Heat exchanger, Compressor and FAN flow rate balance, etc.). Although 1D and 3D-CAE tool become a part of engineer's daily routine in recently, effective integrity interface between the Digital tool and real humanistic collaboration process is the most important key to accomplish the innovative synthesis. This paper presents a practical lean development method using set-based design method which combines digital tool with human tacit knowledge for architecture type product having hierarchical interaction between system and subsystems.

This study focuses on assessing the effectiveness of an empirically recommended support design through numerical modelling. Numerical techniques are utilized to accurately evaluate tunnel stability under various geological conditions. Numerical analysis is conducted on two different rock types along the tunnel route, employing the recommended support design based on the existing rock mass rating (RMR) and Q-system. Furthermore, support recommendations are made using the modified RMR and Q-system, considering the impact of stress. The numerical analysis indicates that the support recommended by the existing RMR may not substantially impact the total displacement and vertical stresses around the tunnel crown in the “Fair” to “Poor” rock mass of the study area. However, extending the bolt length by modifying the RMR to incorporate stress effects results in a reduction of total displacement and vertical stresses, ultimately achieving a stable level. These results underline the importance of considering stress effects and utilizing modified support designs to enhance tunnel stability. [ABSTRACT FROM AUTHOR]