• Geomechanics and Engineering
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• v.37 no.3
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• pp.223-241
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• 2024

Evaluating the performance of Tunnel Boring Machines (TBMs) stands as a pivotal juncture in the domain of hard rock mechanized tunneling, essential for achieving both a dependable construction timeline and utilization rate. In this investigation, three advanced artificial neural networks namely, gated recurrent unit (GRU), back propagation neural network (BPNN), and simple recurrent neural network (SRNN) were crafted to prognosticate TBM-rate of penetration (ROP). Drawing from a dataset comprising 1125 data points amassed during the construction of the Alborze Service Tunnel, the study commenced. Initially, five geomechanical parameters were scrutinized for their impact on TBM-ROP efficiency. Subsequent statistical analyses narrowed down the effective parameters to three, including uniaxial compressive strength (UCS), peak slope index (PSI), and Brazilian tensile strength (BTS). Among the methodologies employed, GRU emerged as the most robust model, demonstrating exceptional predictive prowess for TBM-ROP with staggering accuracy metrics on the testing subset (R² = 0.87, NRMSE = 6.76E-04, MAD = 2.85E-05). The proposed models present viable solutions for analogous ground and TBM tunneling scenarios, particularly beneficial in routes predominantly composed of volcanic and sedimentary rock formations. Leveraging forecasted parameters holds the promise of enhancing both machine efficiency and construction safety within TBM tunneling endeavors.

• Geomechanics and Engineering
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• v.37 no.5
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• pp.475-498
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• 2024

The prediction of the susceptibility of soil to liquefaction using a limited set of parameters, particularly when dealing with highly unbalanced databases is a challenging problem. The current study focuses on different ensemble learning classification algorithms using highly unbalanced databases of results from in-situ tests; standard penetration test (SPT), shear wave velocity (V_s) test, and cone penetration test (CPT). The input parameters for these datasets consist of earthquake intensity parameters, strong ground motion parameters, and in-situ soil testing parameters. liquefaction index serving as the binary output parameter. After a rigorous comparison with existing literature, extreme gradient boosting (XGBoost), bagging, and random forest (RF) emerge as the most efficient models for liquefaction instance classification across different datasets. Notably, for SPT and V_s-based models, XGBoost exhibits superior performance, followed by Light gradient boosting machine (LightGBM) and Bagging, while for CPT-based models, Bagging ranks highest, followed by Gradient boosting and random forest, with CPT-based models demonstrating lower G_mean(error), rendering them preferable for soil liquefaction susceptibility prediction. Key parameters influencing model performance include internal friction angle of soil (ϕ) and percentage of fines less than 75 µ (F₇₅) for SPT and V_s data and normalized average cone tip resistance (q_c) and peak horizontal ground acceleration (a_max) for CPT data. It was also observed that the addition of V_s measurement to SPT data increased the efficiency of the prediction in comparison to only SPT data. Furthermore, to enhance usability, a graphical user interface (GUI) for seamless classification operations based on provided input parameters was proposed.

• Geomechanics and Engineering
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• v.36 no.3
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• pp.259-276
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• 2024

The undrained shear strength is widely acknowledged as a fundamental mechanical property of soil and is considered a critical engineering parameter. In recent years, researchers have employed various methodologies to evaluate the shear strength of soil under undrained conditions. These methods encompass both numerical analyses and empirical techniques, such as the cone penetration test (CPT), to gain insights into the properties and behavior of soil. However, several of these methods rely on correlation assumptions, which can lead to inconsistent accuracy and precision. The study involved the development of innovative methods using extreme gradient boosting (XGB) to predict the pile set-up component "A" based on two distinct data sets. The first data set includes average modified cone point bearing capacity (q_t), average wall friction (f_s), and effective vertical stress (σ_vo), while the second data set comprises plasticity index (PI), soil undrained shear cohesion (S_u), and the over consolidation ratio (OCR). These data sets were utilized to develop XGBoost-based methods for predicting the pile set-up component "A". To optimize the internal hyperparameters of the XGBoost model, four optimization algorithms were employed: Particle Swarm Optimization (PSO), Social Spider Optimization (SSO), Arithmetic Optimization Algorithm (AOA), and Sine Cosine Optimization Algorithm (SCOA). The results from the first data set indicate that the XGBoost model optimized using the Arithmetic Optimization Algorithm (XGB - AOA) achieved the highest accuracy, with R2 values of 0.9962 for the training part and 0.9807 for the testing part. The performance of the developed models was further evaluated using the RMSE, MAE, and VAF indices. The results revealed that the XGBoost model optimized using XGBoost - AOA outperformed other models in terms of accuracy, with RMSE, MAE, and VAF values of 0.0078, 0.0015, and 99.6189 for the training part and 0.0141, 0.0112, and 98.0394 for the testing part, respectively. These findings suggest that XGBoost - AOA is the most accurate model for predicting the pile set-up component.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.3D
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• pp.433-439
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• 2011

The frost depth(frost penetration) is used to install anti-frost heave layers in pavement designs. The freezing index is calculated by an annual accumulated value of multiplying the period of time with temperatures below zero, and the corresponding temperature. Therefore, the DAAT(daily average air temperature) calculation method may play an effect on the FI(freezing index). The Weather Observatory used to supply 4 average air temperatures per day, but currently supplies 8 per day. With this study, we divided the southern part(below FI=$350^{\circ}C{\cdot}day$) of the Korean peninsula into 6 areas according to site conditions(low embankment, embankment-cutting slope, and the cutting slope) and established a field measurement system for 15 positions to check the effects on the result of FI according to differing DAAT calculation methods. The air temperatures obtained by the field measurement system was used to calculate and compare the FI. As a result, the freezing index calculated based on the $DAAT_4(T_4)$ is normally greater by 3% than the one on $DAAT_8(T_8)$. In addition, the calibration equation for the freezing index using air temperatures was proposed through the research.

• Journal of Korean Society of Transportation
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• v.35 no.4
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• pp.332-347
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• 2017

Analysis of the interaction between the automated vehicles and manual vehicles is very important in analyzing the performance of automated cooperative driving environments. In particular, the automated vehicle platooning can affect the driving behavior of adjacent manual vehicles. The purpose of this study is to analyze the lane change behavior of the manual vehicles in automated vehicle platonning environment and to conduct the experiment and questionnaire surveys in three stages. In the first stage, a video questionnaire survey was conducted, and responsive behaviors of manual vehicles were investigated. In second stage, the driving simulator experiments were conducted to investigate the lane change behaviors of in automated vehicle platonning environments. To analyze the lane change behavior of the manual vehicles, lane change durations and acceleration noise, which are indicators of traffic flow stability, were used. The driving behavior of manual vehicles were compared across different market penetration rates (MPR) of automated vehicles and human factors. Lastly, NASA-TLX (NASA Task Load Index) was used to evaluate the workload of the manual vehicle drivers. As a result of the analysis, it was identified that manual vehicle drivers had psychological burdens while driving in automated vehicle platonning environments. Lane change durations were longer when the MPR of the automated vehicles increased, and acceleration noise were increased in the case of 30-40 years old or female drivers. The results from this study can be used as a fundamental for more realistic traffic simulations reflecting the interaction between the automated vehicles and manual vehicles. It is also expected to effectively support the establishment of valuable transportation management strategy in automated vehicle environments.

• Journal of the Korean Data and Information Science Society
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• v.27 no.1
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• pp.67-74
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• 2016

Due to the character of the climate of Korea, the pavement of a road is Influenced by freezing in winter season and thawing in thawing season. In the last few years, several articles have been devoted to the study to minimize the damage of freezing and thawing action. The purpose of this paper is to identify appropriacy of factors that influence road pavement thickness. We conduct the decision tree analysis on the field data of road pavement. The target variable is 'Frost penetration'. This value was calculated from the temperature data. The input variables are 'Region', 'Type of road pavement', 'Anti-frost layer', 'Month' and 'Air temperature'. The region was divided into 9 regions by freezing index $350{\sim}450^{\circ}C{\cdot}day$, $450{\sim}550^{\circ}C{\cdot}day$, $550{\sim}650^{\circ}C{\cdot}day$. The type of road pavement has three-section such as area of cutting, boundary area of cutting and bankin, lower area of banking. As the result, the variables that influence 'Frost penetration' are Month, followed by anti-frost layer, air temperature and region.

• Journal of the Korean Recycled Construction Resources Institute
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• v.5 no.3
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• pp.313-319
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• 2017

In general, various factors such as grain size, chemical composition, amorphous amount, amorphous Si and Al content of fly ash affect the reaction with cement. In this study, we investigate the effect of fly ash particle characteristics on compressive strength. The standard sand was pulverized to a particle size similar to that of fly ash and the compressive strength was measured by blending with the cement as in fly. Using the measured compressive strength results, strength enhancement by cement hydration reaction and strength enhancement by particle filling effect were confirmed. Strength increment by pozzolanic reaction of fly ash was calculated by using the compressive strength results of mortar substituted with standard powder. As a result of comparison between compressive strengths and the particle characteristics of fly ash, the blaine showed a weak correlation with the compressive strength and the PI(Pozzolanic Index) showed good correlation with the 10% penetration diameter(D10) and the 50% Respectively. Therefore, it is expected that PI will be a good means to evaluate the fly ash characteristics together with the chemical characteristics of fly ash.

• Asian-Australasian Journal of Animal Sciences
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• v.16 no.1
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• pp.1-5
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• 2003

Genetic parameters of live weight at slaughter (LWT), quantity index (QIX), yield grade (YGD), quality grade (QGD), pH of meat, and boiled meat tenderness in terms of mastication (BMAS), shear force (BSFR) and penetration (BPEN) in Hanwoo steers were estimated. Effects of sire, location and their interaction on these traits were also evaluated. Sire effects were found to be significant on all the traits studied except for pH and BSFR. The LWT, QIX and QGD were also significantly affected both by location and by interaction effect between sire${\times}$location. The BSFR and BPEN were significantly (p<0.01) affected by location but not significantly by sire${\times}$location interaction. The boiled meat tenderness and pH were negatively correlated ($r_g$ and $r_p$) with LWT, QIX and QGD. All the other traits were positively correlated with each other. Positive and high genetic correlation (+0.56) between LWT and QGD was obtained indicating that selection for LWT would improve QGD. The $h^2$ estimates were 0.43, 0.37, 0.37, 0.35 and 0.32 for QGD, LWT, pH, BSFR and BPEN, respectively.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.03a
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• pp.1466-1483
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• 2008

This paper discusses the development of an up-to-date computerized CPT (Cone Penetration Test) based soil engineering classification system to provide geotechnical engineers with a handy tool for their daily design activities. Five CPT soil engineering classification systems are incorporated in this effort. They include the probabilistic region estimation and fuzzy classification methods, both developed by Zhang and Tumay, the Schmertmann, the Douglas and Olsen, and the Robertson et al. methods. In the probabilistic region estimation method, a conformal transformation is used to determine the soil classification index, U, from CPT cone tip resistance and friction ratio. A statistical correlation is established between U and the compositional soil type given by the Unified Soil Classification System (USCS). The soil classification index, U, provides a soil profile over depth with the probability of belonging to different soil types, which more realistically and continuously reflects the in-situ soil characterization, which includes the spatial variation of soil types. The CPT fuzzy classification on the other hand emphasizes the certainty of soil behavior. The advantage of combining these two classification methods is realized through implementing them into visual basic software with three other CPT soil classification methods for friendly use by geotechnical engineers. Three sites in Louisiana were selected for this study. For each site, CPT tests and the corresponding soil boring results were correlated. The soil classification results obtained using the probabilistic region estimation and fuzzy classification methods are cross-correlated with conventional soil classification from borings logs and three other established CPT soil classification methods.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.03a
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• pp.730-739
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• 2010

Stress history increases in penetration resistance due to the increase in residual horizontal stress of granular soil. This study analyzes the effect of stress history on the results of CPT and DMT from calibration chamber specimen in OC as well as NC state. Test results show that the normalized cone resistance by mean effective stress correlates well with the relative density and the state parameter, whereas the normalized cone resistance with regard to vertical effective stress is a little affected by stress history. The horizontal stress index($K_D$) in DMT more reflects the influence of stress history on granular soil than the dilatometer modulus($E_D$) and cone resistance($q_c$). The $K_D/K_0$, in which the effect of stress history on $K_D$ is compensated by the at-rest coefficient of earth pressure, $K_0$, is related to relative density, state parameter and the normalized cone resistance by mean effective stress. It is also observed that the normalized dilatometer modulus by mean effective stress($E_D/{\sigma_m}'$) is unique correlated with the state parameter, regardless of stress history.