An evolutionary adaptive neuro-fuzzy inference system for estimating field penetration index of tunnel boring machine in rock mass

Field penetration index (FPI) is one of the representative key parameters to examine the tunnel boring machine (TBM) performance. Lack of accurate FPI prediction can be responsible for numerous disastrous incidents associated with rock mechanics and engineering. This study aims to predict TBM performance (i.e. FPI) by an efficient and improved adaptive neuro-fuzzy inference system (ANFIS) model. This was done using an evolutionary algorithm, i.e. artificial bee colony (ABC) algorithm mixed with the ANFIS model. The role of ABC algorithm in this system is to find the optimum membership functions (MFs) of ANFIS model to achieve a higher degree of accuracy. The procedure and modeling were conducted on a tunnelling database comprising of more than 150 data samples where brittleness index (BI), fracture spacing, α angle between the plane of weakness and the TBM driven direction, and field single cutter load were assigned as model inputs to approximate FPI values. According to the results obtained by performance indices, the proposed ANFIS_ABC model was able to receive the highest accuracy level in predicting FPI values compared with ANFIS model. In terms of coefficient of determination (R2), the values of 0.951 and 0.901 were obtained for training and testing stages of the proposed ANFIS_ABC model, respectively, which confirm its power and capability in solving TBM performance problem. The proposed model can be used in the other areas of rock mechanics and underground space technologies with similar conditions. © 2021 Institute of Rock and Soil Mechanics, Chinese Academy of Science

Rock-burst occurrence prediction based on optimized naïve bayes models

Rock-burst is a common failure in hard rock related projects in civil and mining construction and therefore, proper classification and prediction of this phenomenon is of interest. This research presents the development of optimized naïve Bayes models, in predicting rock-burst failures in underground projects. The naïve Bayes models were optimized using four weight optimization techniques including forward, backward, particle swarm optimization, and evolutionary. An evolutionary random forest model was developed to identify the most significant input parameters. The maximum tangential stress, elastic energy index, and uniaxial tensile stress were then selected by the feature selection technique (i.e., evolutionary random forest) to develop the optimized naïve Bayes models. The performance of the models was assessed using various criteria as well as a simple ranking system. The results of this research showed that particle swarm optimization was the most effective technique in improving the accuracy of the naïve Bayes model for rock-burst prediction (cumulative ranking = 21), while the backward technique was the worst weight optimization technique (cumulative ranking = 11). All the optimized naïve Bayes models identified the maximum tangential stress as the most significant parameter in predicting rock-burst failures. The results of this research demonstrate that particle swarm optimization technique may improve the accuracy of naïve Bayes algorithms in predicting rock-burst occurrence. © 2013 IEEE

Tunnel boring machine performance prediction in tropically weathered granite through empirical and computational methods

Many works highlight the use of effective parameters in Tunnel Boring Machine (TBM) performance predictive models. However, there is a lack of study considering the effects of tropically weathered rock mass in these models. This research aims to develop several models for predicting Penetration Rate (PR) and Advance Rate (AR) of TBMs in fresh, slightly weathered and moderately weathered zones in granite. To achieve these objectives, an extensive study on 12,649 m of the Pahang- Selangor Raw Water Transfer (PSRWT) tunnel in Malaysia was carried out. The most influential parameters on TBM performance in terms of rock (mass and material) properties and machine specifications were investigated. A database consisting the tunnel length of 5,443 m, 5,530 m and 1,676 m representing fresh, slightly weathered and moderately weathered zones, respectively was analysed. Based on field mapping and laboratory study, a considerable difference of rock mass and material characteristics has been observed. In order to demonstrate the need for developing new models for prediction of TBM performance, two empirical models namely QTBM and Rock Mass Excavatability (RME) were analysed. It was found that empirical models could not predict TBM performance of various weathering zones satisfactorily. Then, multiple regression (i.e. linear and non-linear) analyses were applied to develop new equations for estimating PR and AR. The performance capacity of the multiple regression models could be increased in the mentioned weathering states with overall coefficient of determination (R2) of 0.6. Furthermore, two hybrid intelligent systems (i.e. combination of artificial neural network with particle swarm optimisation and imperialism competitive algorithm) were developed as new techniques in field of TBM performance. By incorporating weathering state as input parameter in hybrid intelligent systems, performance capacity of these models can be significantly improved (R2 = 0.9). With a newly-proposed systems, the results demonstrate superiority of these models in predicting TBM performance in tropically weathered granite compared to other existing and proposed techniques

Long-term degradation, damage and fracture in deep rock tunnels: A review on the effect of excavation methods

Rocks are frequently host materials for underground structures, particularly for deep Tunnels. Their behavior plays a fundamental role in the overall stability of these structures. In fact, the erection of deep tunnels imposes rocks excavations around the defined routes. These excavations are generally carried out by various methods of which the most used are Drill-and-Blast (DB) and Tunnel Boring Machine (TBM).  However, regardless of the tunnelling method used, the impacts such as the perturbation of the initial stress field in rocks and the release of the stored energy are always significant. The impacts produce damage, fractures and deformations which are generally time-dependent and influence the long-term stability of deep tunnels built in rocks. Thus, by considering the aforementioned excavation methods, this paper identifies, reviews and describes the relevant factors generated during and after rock excavations. Interestingly, such factors directly or indirectly influence the long-term stability and therefore the structural integrity of deep rock tunnels. In addition, some recommendations and proposals for future works are presented. This paper can provide useful references in understanding the degradations, damage and fractures generated by tunnelling methods and facilitate suitable actions to ensure long-term stability of deep underground structures

Prediction of blasting mean fragment size using support vector regression combined with five optimization algorithms

The main purpose of blasting operation is to produce desired and optimum mean size rock fragments. Smaller or fine fragments cause the loss of ore during loading and transportation, whereas large or coarser fragments need to be further processed, which enhances production cost. Therefore, accurate prediction of rock fragmentation is crucial in blasting operations. Mean fragment size (MFS) is a crucial index that measures the goodness of blasting designs. Over the past decades, various models have been proposed to evaluate and predict blasting fragmentation. Among these models, artificial intelligence (AI)-based models are becoming more popular due to their outstanding prediction results for multi-influential factors. In this study, support vector regression (SVR) techniques are adopted as the basic prediction tools, and five types of optimization algorithms, i.e. grid search (GS), grey wolf optimization (GWO), particle swarm optimization (PSO), genetic algorithm (GA) and salp swarm algorithm (SSA), are implemented to improve the prediction performance and optimize the hyper-parameters. The prediction model involves 19 influential factors that constitute a comprehensive blasting MFS evaluation system based on AI techniques. Among all the models, the GWO-v-SVR-based model shows the best comprehensive performance in predicting MFS in blasting operation. Three types of mathematical indices, i.e. mean square error (MSE), coefficient of determination (R2) and variance accounted for (VAF), are utilized for evaluating the performance of different prediction models. The R2, MSE and VAF values for the training set are 0.8355, 0.00138 and 80.98, respectively, whereas 0.8353, 0.00348 and 82.41, respectively for the testing set. Finally, sensitivity analysis is performed to understand the influence of input parameters on MFS. It shows that the most sensitive factor in blasting MFS is the uniaxial compressive strength. © 2021 Institute of Rock and Soil Mechanics, Chinese Academy of Science

Application of several optimization techniques for estimating TBM advance rate in granitic rocks

https://www.sciencedirect.com/science/article/pii/S1674775518303056This study aims to develop several optimization techniques for predicting advance rate of tunnel boring machine (TBM) in different weathered zones of granite. For this purpose, extensive field and laboratory studies have been conducted along the 12,649 m of the Pahang – Selangor raw water transfer tunnel in Malaysia. Rock properties consisting of uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), rock mass rating (RMR), rock quality designation (RQD), quartz content (q) and weathered zone as well as machine specifications including thrust force and revolution per minute (RPM) were measured to establish comprehensive datasets for optimization. Accordingly, to estimate the advance rate of TBM, two new hybrid optimization techniques, i.e. an artificial neural network (ANN) combined with both imperialist competitive algorithm (ICA) and particle swarm optimization (PSO), were developed for mechanical tunneling in granitic rocks. Further, the new hybrid optimization techniques were compared and the best one was chosen among them to be used for practice. To evaluate the accuracy of the proposed models for both testing and training datasets, various statistical indices including coefficient of determination (R2), root mean square error (RMSE) and variance account for (VAF) were utilized herein. The values of R2, RMSE, and VAF ranged in 0.939–0.961, 0.022–0.036, and 93.899–96.145, respectively, with the PSO-ANN hybrid technique demonstrating the best performance. It is concluded that both the optimization techniques, i.e. PSO-ANN and ICA-ANN, could be utilized for predicting the advance rate of TBMs; however, the PSO-ANN technique is superior

Universal physics-based rate of penetration prediction model for rotary drilling

The drilling process is one of the most important and expensive aspects of the oil and gas industry. Drilling is required during mining for different ore production processes such as blasting and large drilling operations. Overall, it contributes significantly to the total cost of mining. As a result, an accurate prediction of the rate of penetration (ROP) is crucial for drilling performance optimization and contributes directly to reducing drilling costs. Knowledge of drilling performance is a powerful tool to aid in the development of a consistent drilling plan as well as to anticipate issues that may arise during drilling operations. Several approaches, with varying degrees of complexity and accuracy, have been tested to predict drilling performance, but all have shown several limitation to predict the complete drilling performance curve including locate the founder point. This limitation can be extended to their capacity of covering different drilling scenarios with high accuracy. In this thesis (manuscript style) a review of the history of drilling performance prediction is conducted with emphasis on the rotary drilling of small and large diameters. The approaches are grouped into two categories: physics-based models and data-driven models. Due to the low complexity of the physics-based models and the scarcity of drilling performance prediction research that reports the founder point location, a novel physics-based ROP prediction model for rotary drilling that includes the founder point location is presented. This model presents high accuracy to predict the drilling performance for fixed cutter drill bit, roller-cone drill bit, and large diameter drilling operations. The behaviors of the new model constants (drillability coefficient and drillability constant term) are discussed when analyzed in relation to the unconfined compressive strength (UCS), bit diameter, and rotary speed. Additionally, a new experimental setup approach was developed based on the circular movement of the full-scale disc cutter that are normally used in raise boring and tunnel boring machines. This setup will permit to simulate the large diameter drilling operations in laboratory scale aiming the understanding of the fragmentation process and application of optimization to this scenario

Hybridizing five neural-metaheuristic paradigms to predict the pillar stress in bord and pillar method

Pillar stability is an important condition for safe work in room-and-pillar mines. The instability of pillars will lead to large-scale collapse hazards, and the accurate estimation of induced stresses at different positions in the pillar is helpful for pillar design and guaranteeing pillar stability. There are many modeling methods to design pillars and evaluate their stability, including empirical and numerical method. However, empirical methods are difficult to be applied to places other than the original environmental characteristics, and numerical methods often simplify the boundary conditions and material properties, which cannot guarantee the stability of the design. Currently, machine learning (ML) algorithms have been successfully applied to pillar stability assessment with higher accuracy. Thus, the study adopted a back-propagation neural network (BPNN) and five elements including the sparrow search algorithm (SSA), gray wolf optimizer (GWO), butterfly optimization algorithm (BOA), tunicate swarm algorithm (TSA), and multi-verse optimizer (MVO). Combining metaheuristic algorithms, five hybrid models were developed to predict the induced stress within the pillar. The weight and threshold of the BPNN model are optimized by metaheuristic algorithms, in which the mean absolute error (MAE) is utilized as the fitness function. A database containing 149 data samples was established, where the input variables were the angle of goafline (A), depth of the working coal seam (H), specific gravity (G), distance of the point from the center of the pillar (C), and distance of the point from goafline (D), and the output variable was the induced stress. Furthermore, the predictive performance of the proposed model is evaluated by five metrics, namely coefficient of determination (R2), root mean squared error (RMSE), variance accounted for (VAF), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results showed that the five hybrid models developed have good prediction performance, especially the GWO-BPNN model performed the best (Training set: R2 = 0.9991, RMSE = 0.1535, VAF = 99.91, MAE = 0.0884, MAPE = 0.6107; Test set: R2 = 0.9983, RMSE = 0.1783, VAF = 99.83, MAE = 0.1230, MAPE = 0.9253). Copyright © 2023 Zhou, Chen, Chen, Khandelwal, Monjezi and Peng

Tunnel Engineering

This volume presents a selection of chapters covering a wide range of tunneling engineering topics. The scope was to present reviews of established methods and new approaches in construction practice and in digital technology tools like building information modeling. The book is divided in four sections dealing with geological aspects of tunneling, analysis and design, new challenges in tunnel construction, and tunneling in the digital era. Topics from site investigation and rock mass failure mechanisms, analysis and design approaches, and innovations in tunnel construction through digital tools are covered in 10 chapters. The references provided will be useful for further reading