• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.5
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• pp.431-449
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• 2022

As many tunnels generally have been constructed, various experiences and techniques have been accumulated for tunnel design as well as tunnel construction. Hence, there are not a few cases that, for some usual tunnel design works, it is sufficient to perform the design by only modifying or supplementing previous similar design cases unless a tunnel has a unique structure or in geological conditions. In particular, for a tunnel blast design, it is reasonable to refer to previous similar design cases because the blast design in the stage of design is a preliminary design, considering that it is general to perform additional blast design through test blasts prior to the start of tunnel excavation. Meanwhile, entering the industry 4.0 era, artificial intelligence (AI) of which availability is surging across whole industry sector is broadly utilized to tunnel and blasting. For a drill and blast tunnel, AI is mainly applied for the estimation of blast vibration and rock mass classification, etc. however, there are few cases where it is applied to blast pattern design. Thus, this study attempts to automate tunnel blast design by means of machine learning, a branch of artificial intelligence. For this, the data related to a blast design was collected from 25 tunnel design reports for learning as well as 2 additional reports for the test, and from which 4 design parameters, i.e., rock mass class, road type and cross sectional area of upper section as well as bench section as input data as well as16 design elements, i.e., blast cut type, specific charge, the number of drill holes, and spacing and burden for each blast hole group, etc. as output. Based on this design data, three machine learning models, i.e., XGBoost, ANN, SVM, were tested and XGBoost was chosen as the best model and the results show a generally similar trend to an actual design when assumed design parameters were input. It is not enough yet to perform the whole blast design using the results from this study, however, it is planned that additional studies will be carried out to make it possible to put it to practical use after collecting more sufficient blast design data and supplementing detailed machine learning processes.

• Journal of Korean Tunnelling and Underground Space Association
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• v.10 no.2
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• pp.105-118
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• 2008

Shotcrete is one of the main support materials in tunnelling. Its bonding state on excavated rock surfaces controls the safety of the tunnel: De-bonding of shotcrete from an excavated surface decreases the safety of the tunnel. Meanwhile, the bonding state of shotcrete is affected by blasting during excavation at tunnel face as well as bench cut. Generally, the bonding state of shotcrete can be classified as void, de-bonded, or fully bonded. In this study, the state of the back-surface of shotcrete is investigated using impact-echo (IE) techniques. Numerical simulation of IE technique is performed with ABAQUS. Signals obtained from the IE simulations were analyzed at time, frequency, and time-frequency domains, respectively. Using an integrated active signal processing technique coupled with a Short-Time Fourier Transform (STFT) analysis, the bonding state of the shotcrete can be evaluated accurately. As the bonding state worsens, the amplitude of the first peak past the maximum amplitude in the time domain waveform and the maximum energy of the autospectral density are increasing. The resonance frequency becomes detectable and calculable and the contour in time-frequency domain has a long tail parallel to the time axis. Signal characteristics with respect to ground condition were obtained in case of fully bonded condition. As the ground condition worsens, the length of a long tail parallel to the time axis is lengthened and the contour is located in low frequency range under 10 kHz.

• Journal of Korean Tunnelling and Underground Space Association
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• v.22 no.4
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• pp.451-468
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• 2020

Aging tunnels with small cross-sections can cause chronic traffic jams. This problem can be solved by widening the tunnel. In general, when the tunnel is expanded, the outer portion of the existing tunnel is excavated through a mechanical or blasting method. Such excavation affects not only the surrounding ground but also the existing tunnel. The application of the pre-cutting method can be a solution to these problems effectively. Therefore, if the widening of tunnel is performed by applying pre-cutting method, analysis of the impact of this method must be performed. In this study, in order to analyze the effect of applying pre-cutting in tunnel widening, numerical analysis is performed at six ground grades, from grade I to weathered rock. The analysis is performed with the expanding lane and the excavation length of pre-cutting as variables. In addition, the analysis is focused on the displacement of crown of the existing tunnel and the enlarged tunnel. As a result, the crown displacement of the enlarged tunnel is confirmed to converge at the same value regardless of the excavation length of the pre-cutting when the tunnel widening is completed. In the case of existing tunnels, uplift of crown occurs within 5 m of the front of the tunnel surface, and the shorter the excavation length of pre-cutting is found to be effective in preventing the occurrence of uplift.

• Geomechanics and Engineering
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• v.35 no.2
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• pp.121-133
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• 2023

The demand for cement and limestone crushed materials has increased many folds due to the tremendous increase in construction activities in Pakistan during the past few decades. The number of cement production industries has increased correspondingly, and so the rock-blasting operations at the limestone quarry sites. However, the safety procedures warranted at these sites for the blast-induced ground vibrations (BIGV) have not been adequately developed and/or implemented. Proper prediction and monitoring of BIGV are necessary to ensure the safety of structures in the vicinity of these quarry sites. In this paper, an attempt has been made to predict BIGV using artificial neural network (ANN) at three selected limestone quarries of Pakistan. The ANN has been developed in Python using Keras with sequential model and dense layers. The hyper parameters and neurons in each of the activation layers has been optimized using randomized and grid search method. The input parameters for the model include distance, a maximum charge per delay (MCPD), depth of hole, burden, spacing, and number of blast holes, whereas, peak particle velocity (PPV) is taken as the only output parameter. A total of 110 blast vibrations datasets were recorded from three different limestone quarries. The dataset has been divided into 85% for neural network training, and 15% for testing of the network. A five-layer ANN is trained with Rectified Linear Unit (ReLU) activation function, Adam optimization algorithm with a learning rate of 0.001, and batch size of 32 with the topology of 6-32-32-256-1. The blast datasets were utilized to compare the performance of ANN, multivariate regression analysis (MVRA), and empirical predictors. The performance was evaluated using the coefficient of determination (R²), mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and root mean squared error (RMSE)for predicted and measured PPV. To determine the relative influence of each parameter on the PPV, sensitivity analyses were performed for all input parameters. The analyses reveal that ANN performs superior than MVRA and other empirical predictors, andthat83% PPV is affected by distance and MCPD while hole depth, number of blast holes, burden and spacing contribute for the remaining 17%. This research provides valuable insights into improving safety measures and ensuring the structural integrity of buildings near limestone quarry sites.

• Journal of the Korean Society for Railway
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• v.20 no.5
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• pp.668-682
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• 2017

Alignments of railways recently constructed in Korea have been straightened due to the advent of high-speed rail, which means increasing the numbers of tunnels and bridges. Overbreak during tunnel construction may be unavoidable, and is very influential on overall stability. Over-excavation in tunneling is also one of the most important factors in construction costs. Overbreak problems around crown areas have decreased with improvements of excavation methods, but overbreak problems around bottom areas have not decreased because those areas are not very influential on tunnel stability compared with crown areas. The filling costs of 10 cm thickness of overbreak at the bottom of a tunnel are covered under construction costs by Korea Railway Authority regulations, but filling costs for more than the covered thickness are considered losses of construction cost. The filling material for overbreak bottoms of tunnels should be concrete, but concrete and mixed granular materials with fractured rock are also used for some sites. Tunnels in which granular materials with fractured rock are used may have a discontinuous section under the concrete slab track. The discontinuous section influences the propagation of waves generated from train operation. When the bottom of a tunnel is filled with only concrete material, the bottom of the tunnel can be considered as a continuous section, in which the waves generated from a train may propagate without reflection waves. However, a discontinuous section filled with mixed granular materials may reflect waves, which can cause resonance of vibration. The filled materials and vibration propagation characteristics are studied in this research. Tunnel bottom filling materials that have ratios of granular material to concrete of 5.0 %, 11.5 %, and 18.0 % are investigated. Samples were made and tested to determine their material properties. Static numerical analyses were performed using the FEM program under train operation load; test results were found to satisfy the stability requirements. However, dynamic analysis results show that some mixed ratios may generate resonance vibration from train operation at certain speeds.