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

Intelligence prediction of some selected environmental issues of blasting: A review

Background: Blasting is commonly used for loosening hard rock during excavation for generating the desired rock fragmentation required for optimizing the productivity of downstream operations. The environmental impacts resulting from such blasting operations include the generation of flyrock, ground vibrations, air over pressure (AOp) and rock fragmentation. Objective: The purpose of this research is to evaluate the suitability of different computational techniques for the prediction of these environmental effects and to determine the key factors which contribute to each of these effects. This paper also identifies future research needs for the prediction of the environmental effects of blasting operations in hard rock. Methods: The various computational techniques utilized by the researchers in predicting blasting environmental issues such as artificial neural network (ANN), fuzzy interface system (FIS), imperialist competitive algorithm (ICA), and particle swarm optimization (PSO), were reviewed. Results: The results indicated that ANN, FIS and ANN-ICA were the best models for prediction of flyrock distance. FIS model was the best technique for the prediction of AOp and ground vibration. On the other hand, ANN was found to be the best for the assessment of fragmentation. Conclusion and Recommendation: It can be concluded that FIS, ANN-PSO, ANN-ICA models perform better than ANN models for the prediction of environmental issues of blasting using the same database. This paper further discusses how some of these techniques can be implemented by mining engineers and blasting team members at operating mines for predicting blast performance

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

Strength characterisation of shale using Mohr-Coulomb and Hoek-Brown criteria

Parameters on rock material strengths like triaxial compressive strength are usually determined from laboratory test on intact rock samples. Uncertainties arise in predicting the behaviour of a rock mass under confinement due to its discontinuous nature. Discontinuity such as joint induces inhomogeneous and anisotropic behaviour in the rock mass, in contrast to the behaviour of intact rock samples used in the lab tests. Several empirical approaches such as Rock Mass Rating (RMR) are available to classify and to evaluate the mass strength of discontinuous rock. However, RMR suffers from several limitations for it is not suitable for very poor quality rock mass such as shale. This study investigates the suitability of the new empirical approach namely Hoek-Brown failure criterion (2002). Such that it together with RocLab software, are used to evaluate and to assess the strength of rock mass under confinement and field condition. In this study two failure criteria were served. Results obtained indicate that the failure envelope derived using the new Hoek-Brown criterion shows a better presentation of shale under field condition in comparison with the classic Mohr-Coulomb method

A Novel Approach for Blast-Induced Flyrock Prediction Based on Imperialist Competitive Algorithm and Artificial Neural Network

Flyrock is one of the major disturbances induced by blasting which may cause severe damage to nearby structures. This phenomenon has to be precisely predicted and subsequently controlled through the changing in the blast design to minimize potential risk of blasting. The scope of this study is to predict flyrock induced by blasting through a novel approach based on the combination of imperialist competitive algorithm (ICA) and artificial neural network (ANN). For this purpose, the parameters of 113 blasting operations were accurately recorded and flyrock distances were measured for each operation. By applying the sensitivity analysis, maximum charge per delay and powder factor were determined as the most influential parameters on flyrock. In the light of this analysis, two new empirical predictors were developed to predict flyrock distance. For a comparison purpose, a predeveloped backpropagation (BP) ANN was developed and the results were compared with those of the proposed ICA-ANN model and empirical predictors. The results clearly showed the superiority of the proposed ICA-ANN model in comparison with the proposed BP-ANN model and empirical approaches

Effect of geological structure and blasting practice in fly rock accident at Johor, Malaysia

Blasting operation is common method in hard rock excavation at civil engineering and mining sites. Rock blasting results in the fragmentation along with environmental hazards such as fly rock, ground vibration, air-blast, dust and fumes. Most of the common accidents associated with blasting are due to fly rock. A fly rock accident had occurred on 15 July 2015 at a construction site at Johor, Malaysia. Due to this accident, nearby factory worker was killed while two other workers were seriously injured after being hit by rock debris from an explosion at construction site, 200 m away from the factory. The main purpose of this study is to investigate the causes of fly rock accident based on geological structures and blasting practice such as blast design, pre inspection on geological structures, identifying danger zone due to blasting and communication and evacuation of personnel before blast. It can be concluded that fly rock could have been controlled in three stages; initial drilling of holes based on blast design, ensure limiting charge for holes having less burden or having geological discontinuity, and selecting proper sequence of initiation of holes

A gene expression programming model for predicting tunnel convergence

Underground spaces have become increasingly important in recent decades in metropolises. In this regard, the demand for the use of underground spaces and, consequently, the excavation of these spaces has increased significantly. Excavation of an underground space is accompanied by risks and many uncertainties. Tunnel convergence, as the tendency for reduction of the excavated area due to change in the initial stresses, is frequently observed, in order to monitor the safety of construction and to evaluate the design and performance of the tunnel. This paper presents a model/equation obtained by a gene expression programming (GEP) algorithm, aiming to predict convergence of tunnels excavated in accordance to the New Austrian Tunneling Method (NATM). To obtain this goal, a database was prepared based on experimental datasets, consisting of six input and one output parameter. Namely, tunnel depth, cohesion, frictional angle, unit weight, Poisson's ratio, and elasticity modulus were considered as model inputs, while the cumulative convergence was utilized as the model's output. Configurations of the GEP model were determined through the trial-error technique and finally an optimum model is developed and presented. In addition, an equation has been extracted from the proposed GEP model. The comparison of the GEP-derived results with the experimental findings, which are in very good agreement, demonstrates the ability of GEP modeling to estimate the tunnel convergence in a reliable, robust, and practical manner

Deformation model of deep soil mixing using finite element method

Soil improvement is required to decrease the construction impact on the adjacent underground structures, when a deep excavation is carried out. Deep soil mixing (DSM) is a common method to control deformation caused by deep excavation. This method is an in situ soil mixing technology that mixes existing soil with cementitious materials. This paper presents a numerical modeling of DSM columns, which was conducted to compare the affected zone achieved by installing two different partially penetrated soil-cement columns using a small scale physical modelling. Test procedure and the finite element analysis that verify ground displacement patterns were described. The finite element method (FEM) was focused on the plane strain numerical modeling in ABAQUS. It was found that higher numbers of piles increase the effect of soil deformation where it will extend the soil in much deeper depth before it fails

Evaluation of Applicability and Accuracy of Bus Travel Time Prediction in High and Low Frequency Bus Routes Using Tree-Based ML Techniques

Prediction of bus travel time is a key component of an intelligent transportation system and has many benefits for both service users and providers. Although there is a rich literature on bus travel prediction, some limitations can still be observed. First, high-frequency and low-frequency bus routes have different characterizations in both operational and passenger behavior aspects. Therefore, it is highly expected that bus travel time prediction methods for different frequencies must have different outputs. Second, in the era of big data, applications of machine learning (ML) techniques in travel time prediction have significantly increased. However, there is no single ML model introduced in the literature that is the most accurate in predicting bus travel, especially with regard to bus service frequency. Consequently, the main objective of this study is to determine the most applicable route construction approach and most accurate tree-based ML technique for predicting bus travel time on high- and low-frequency bus routes. The following tree-based ML techniques were adopted in this study: chi-square automatic interaction detection (CHAID), random forest (RF), and gradient-boosted tree (GBT). According to the results, CHAID was selected as the most accurate model for predicting travel time on high-frequency routes, while GBT showed the best performance for low-frequency service. CHIAD analysis identified distance between stops and terminal departure behavior as the most significant factors of travel time on high-frequency routes. Moreover, we introduced the "key stop-based" route construction method for the first time, which is an accurate, reliable, and applicable method

Applications of two neuro-based metaheuristic techniques in evaluating ground vibration resulting from tunnel blasting

Peak particle velocity (PPV) caused by blasting is an unfavorable environmental issue that can damage neighboring structures or equipment. Hence, a reliable prediction and minimization of PPV are essential for a blasting site. To estimate PPV caused by tunnel blasting, this paper proposes two neuro-based metaheuristic models: neuro-imperialism and neuro-swarm. The prediction was made based on extensive observation and data collecting from a tunnelling project that was concerned about the presence of a temple near the blasting operations and tunnel site. A detailed modeling procedure was conducted to estimate PPV values using both empirical methods and intelligence techniques. As a fair comparison, a base model considered a benchmark in intelligent modeling, artificial neural network (ANN), was also built to predict the same output. The developed models were evaluated using several calculated statistical indices, such as variance account for (VAF) and a-20 index. The empirical equation findings revealed that there is still room for improvement by implementing other techniques. This paper demonstrated this improvement by proposing the neuro-swarm, neuro-imperialism, and ANN models. The neuro-swarm model outperforms the others in terms of accuracy. VAF values of 90.318% and 90.606% and a-20 index values of 0.374 and 0.355 for training and testing sets, respectively, were obtained for the neuro-swarm model to predict PPV induced by blasting. The proposed neuro-based metaheuristic models in this investigation can be utilized to predict PPV values with an acceptable level of accuracy within the site conditions and input ranges used in this study