• Geomechanics and Engineering
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• v.38 no.6
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• pp.633-645
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• 2024

Disc cutters, used as excavation tools for rocks in a Tunnel Boring Machine (TBM), naturally undergo wear during the tunneling process, involving crushing and cutting through the ground, leading to various wear types. When disc cutters reach their wear limits, they must be replaced at the appropriate time to ensure efficient excavation. General disc cutter life prediction models are typically used during the design phase to predict the total required quantity and replacement locations for construction. However, disc cutters are replaced more frequently during tunneling than initially planned. Unpredictable disc cutter replacements can easily diminish tunneling efficiency, and abnormal wear is a common cause during tunneling in complex ground conditions. This study aims to overcome the limitations of existing disc cutter life prediction models by utilizing machine data generated during tunneling to predict disc cutter wear patterns and determine the need for replacements in real-time. Artificial intelligence classification algorithms, including K-nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Stacking, are employed to assess the need for disc cutter replacement. Binary classification models are developed to predict which disc cutters require replacement, while multi-class classification models are fine-tuned to identify three categories: no replacement required, replacement due to normal wear, and replacement due to abnormal wear during tunneling. The performance of these models is thoroughly assessed, demonstrating that the proposed approach effectively manages disc cutter wear and replacements in shield TBM tunnel projects.

• Journal of Industrial Technology
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• v.25 no.B
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• pp.55-62
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• 2005

This paper is the results of experimental and numerical works on analyzing the geotechnical engineering behavior and characteristics of excavated clay slope formed by the method of excavated replacement which is one of treatments in soft soil ground. For the centrifuge model tests, models of excavated clay slope were prepared by remolding the marine clayey soil sampled from the field. Tests were performed with changing the slope to investigate the behavior of them. On the other hand, numerical analyses were carried out to analyze the change of safety factor against instability of slope with time. Changes of pore water pressure, shear strength and displacement were also investigated. As results of centrifuge model tests with slopes of 1:1.5 and 1:3 using the confining body of simulating the effect of excavation, for the case of 1:1.5, slope failure occurred right after remove the confining body whereas relatively small displacements within the range of 3.2mm, implying to maintain the stability of slope, were observed for the case of 1:3 slope. From the results of numerical analyses using the software of PLAXIS to investigate the stability of slope after excavation, the minimum safety factor against slope failure was 1.28 for the case of 1:3 slope. The further researches in the future are required with considerations of build up of static pore water pressures during acceleration of centrifuge, depth of excavation influencing the behavior of the slope and permeability of the slope since excavation of the slope was not simulated well resulted from the limitations of apparatus at the stage of excavation during the centrifuge tests.

• Journal of Korean Tunnelling and Underground Space Association
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• v.22 no.5
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• pp.575-589
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• 2020

In recent years, Shield TBM construction has been continuously increasing in domestic tunnels. The main excavation tool in the shield TBM construction is a disc cutter which naturally wears during the excavation process and significantly degrades the excavation efficiency. Therefore, it is important to know the appropriate time of the disc cutter replacement. In this study, it is proposed a predictive model that can determine yes/no of disc cutter replacement using machine learning algorithm. To do this, the shield TBM machine data which is highly correlated to the disc cutter wears and the disc cutter replacement from the shield TBM field which is already constructed are used as the input data in the model. Also, the algorithms used in the study were the support vector machine, k-nearest neighbor algorithm, and decision tree algorithm are all classification methods used in machine learning. In order to construct an optimal predictive model and to evaluate the performance of the model, the classification performance evaluation index was compared and analyzed.

• Geomechanics and Engineering
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• v.29 no.3
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• pp.249-258
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• 2022

A disc cutter is an excavation tool on a tunnel boring machine (TBM) cutterhead; it crushes and cuts rock mass while the machine excavates using the cutterhead's rotational movement. Disc cutter wear occurs naturally. Thus, along with the management of downtime and excavation efficiency, abrasioned disc cutters need to be replaced at the proper time; otherwise, the construction period could be delayed and the cost could increase. The most common prediction models for TBM performance and for the disc cutter lifetime have been proposed by the Colorado School of Mines and Norwegian University of Science and Technology. However, design parameters of existing models do not well correspond to the field values when a TBM encounters complex and difficult ground conditions in the field. Thus, this study proposes a series of machine learning models to predict the disc cutter lifetime of a shield TBM using the excavation (machine) data during operation which is response to the rock mass. This study utilizes five different machine learning techniques: four types of classification models (i.e., K-Nearest Neighbors (KNN), Support Vector Machine, Decision Tree, and Staking Ensemble Model) and one artificial neural network (ANN) model. The KNN model was found to be the best model among the four classification models, affording the highest recall of 81%. The ANN model also predicted the wear rate of disc cutters reasonably well.

• Advances in concrete construction
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• v.13 no.6
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• pp.461-470
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• 2022

Solid waste management is of great concern in today's world. An enormous amount of waste is generated from various industrial activities. Concrete production utilizing some of the potential waste materials will add to the benefit of society. These benefits will include reduction of landfill burden, improved air quality, riverbed protection due to excessive sand excavation, economical concrete production and much more. This study aims to utilize waste granite powder (GP) originating from granite industries as a sand replacement in concrete. Fine GP was collected in the form of slurry from different granite cutting industries. In this study, GP was added in an interval of ten percent as 10%, 20%, 30%, 40% and 50% by weight of sand in concrete. Mechanical assets; compressive strength, flexural strength and splitting tensile strength were prominent for control and blended mixes. Modulus of elasticity (MoE) and abrasion tests were also performed on control and blended specimens of concrete. To provide a comprehensive clarification for enhanced performance of GP prepared concrete samples, scanning electron microscopy (SEM) and X-ray diffraction (XRD) were performed. Results indicate that 30% replacement of sand by weight with GP enhances the mechanical assets of concrete and even the results obtained for 50% replacement are also acceptable. Comprehensive analysis through SEM and XRD for 30% replacement was better than control one. The performance of GP added to concrete in terms of abrasion and modulus of elasticity was far better than the control mix. A significant outcome shows the appropriateness of granite fines to produce sustainable and environmentally friendly concrete.

• Journal of Korean Society of Water and Wastewater
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• v.16 no.6
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• pp.686-699
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• 2002

The purpose of this study is to estimate deterioration factors and weighting factors in pipe network which each local self-governments takes rehabilitation and replacement work present time. Deterioration factors in pipe network are able to effected of specific province or location related with water supply. Most of water supply pipes are laid under the ground, it is hard to quantify deterioration degree of water system. Moreover, the timing and economic limitation and insufficient information on the spot survey gives a difficulty to look over how old water supply system is. Accordingly, this study collects and analyses five data as the laying environment, visual analysis, analysis of soil contents, analysis of pipe material, and questionary survey data in water pipe of A city. The deterioration factor estimates 14 factors with excavation and experimental analysis and 9 factors without excavation and experimental analysis. Also, the weighting factors are estimated by using the multiple linear regressions and the linear programming. The estimated deterioration factor and weighting results are compared the analysis result of visual, pipe material, and soil contents with the Probabilistic Neural Network Model. Consequently, the model results of estimated 9 factors in this study and 14 factors show the 1-2% difference. The result show that the proposed model could be used to decide the deterioration condition of pipe line with real excavation and experimental analysis.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.1
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• pp.57-78
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• 2022

The disc cutter and cutterbit, which are the most important factors to increase the excavation efficiency of TBM, are key factors in the design and construction of the cutter head. The arrangement, spacing, number, size, and material of disc cutters suitable for the ground conditions determine the success or failure of TBM construction. The disc cutter, which is a representative consumable part in TBM construction, can cause enormous disruption to the construction cost as well as the construction cost unless accurate prediction of wear and replacement cycle is accompanied. Therefore, in this study, the method of calculating the replacement cycle of the disc cutter calculated at the time of design for the slurry shield TBM field, and the depth of wear and replacement location of the disc cutter that occurred during actual construction were compared by analyzing the field data. For a quantitative comparison, weathered soil/weathered rock, soft rock, and hard rock were classified according to the ground in the section showing constant excavation data, and the trajectory of circle was different depending on the location of the disc cutter, so it was compared and analyzed.

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.5
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• pp.641-656
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• 2019

In this study, a machine learning method was proposed to use in predicting optimal replacement period of shield TBM (Tunnel Boring Machine) disc cutter. To do this, a large dataset of ground condition, disc cutter replacement records and TBM excavation-related data, collected from a shield TBM tunnel site in Korea, was built and they were used to construct a disc cutter replacement period prediction model using a machine learning algorithm, SVM (Support Vector Machine) and to assess the performance of the model. The results showed that the performance of RBF (Radial Basis Function) SVM is the best among a total of three SVM classification functions (80% accuracy and 10% error rate on average). When compared between ground types, the more disc cutter replacement data existed, the better prediction results were obtained. From this results, it is expected that machine learning methods become very popularly used in practice in near future as more data is accumulated and the machine learning models continue to be fine-tuned.

• Journal of Ocean Engineering and Technology
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• v.20 no.5 s.72
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• pp.49-56
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• 2006

In this thesis, centrifuge model experiments and numerical analyses were carried out to investigate the behavior of an excavated slope in soft clay ground. Centrifuge model tests were performed with various slopes for the excavated ground, such as 1:1.5 and 1:2. Pore pressuresthe model ground were measured to find their effects on the stability of the excavated slope. These experiments showed that the model with 1:2.5 maintained its stability within a short period of time and failed gradually. Therefore, anexcavated slope of soft soil with this slope might maintain stable conditions within a certain time. The mode1 with a 1:3 slope was observed to maintain a very stable condition, showing insignificant deformation in the ground after being excavated. Numerical analyses with PLAXIS, a commerciallyavailable software implemented with the finite element numerical technique, were performed to find the pore pressure distribution within the ground mass and the deformation of the soil. From the results of numerical analysis, a negative pore pressure was developed after the excavation and thus the stability of the slope was maintained. The safety factor for slope failure was found to decrease with time because of the dissipation of negative pore pressure with time.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.6D
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• pp.839-848
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• 2011

TBM (Tunnel Boring Machine) tunnel should be carry out with the adopted machine until the end of excavation because of impossibility of replacement or modification of machine. Observation of the face of the tunnel is difficult, especially in EPB(Earth Pressure Balance) shield TBM, predict changes in the ground condition with analyzing data, collected during the excavation, and it should be reflected in construction. Until recently, subjects of studies on TBM are mainly the determination of machine and the development of advance rate prediction model, according to the characteristics of ground which is the target of excavation. However, study focused on the estimation of ground conditions and the improvement in operational methods using excavation data of TBM equipment, the principal of the excavation, has been done not so much. This study examine the variances in advance rate depending on changes in operating conditions and evaluate the optimal operating conditions of adopt machine, using working data obtained from EPB shield TBM project. The result of this study is suggested as follows. First, cutter head RPM and total thrust force are biggest influences on advance rate, Second, it is recommended for proper advance rate that total thrust force is controlled while optimum cutter head RPM is kept, Third, according to the increasing trend of total thrust force, the changes in ground conditions can be predicted, the appropriate operating conditions can be determined.