• Journal of the Korean Geosynthetics Society
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• v.10 no.4
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• pp.29-36
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• 2011

In this study, a series of the isotropic compression-expansion tests and the drained triaxial tests were performed on Pocheon granite soil with various the dry densities of $16.67kN/m^3$, $17.26kN/m^3$ and $17.65kN/m^3$. Using the tests results the characteristic of the parameters of Lade's single hardening constitutive model were investigated. The soil parameters such as kur and n related to elastic behavior, m and ${\eta}_1$ related to failure criterion, c and p related to hardening function and ${\psi}_2$ and ${\mu}$ related to plastic potential show in a positive linear relationship with the dry density. Since the soil parameters h and representing yield function do not change much to relative density and also are closely related to failure criterion, they can be replaced by failure criterion. We also observed that predicted values from the Lade's single hardening constitutive model were well consistent with the observed data.

• Geomechanics and Engineering
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• v.12 no.3
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• pp.441-464
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• 2017

The determination of the mixture parameters of stabilization has become a great concern in geotechnical applications. This paper presents an effort about the application of artificial intelligence (AI) techniques including radial basis neural network (RBNN), multi-layer perceptrons (MLP), generalized regression neural network (GRNN) and adaptive neuro-fuzzy inference system (ANFIS) in order to predict the unconfined compressive strength (UCS) of silty soil stabilized with bottom ash (BA), jute fiber (JF) and steel fiber (SF) under different freeze-thaw cycles (FTC). The dosages of the stabilizers and number of freeze-thaw cycles were employed as input (predictor) variables and the UCS values as output variable. For understanding the dominant parameter of the predictor variables on the UCS of stabilized soil, a sensitivity analysis has also been performed. The performance measures of root mean square error (RMSE), mean absolute error (MAE) and determination coefficient ($R^2$) were used for the evaluations of the prediction accuracy and applicability of the employed models. The results indicate that the predictions due to all AI techniques employed are significantly correlated with the measured UCS ($p{\leq}0.05$). They also perform better predictions than nonlinear regression (NLR) in terms of the performance measures. It is found from the model performances that RBNN approach within AI techniques yields the highest satisfactory results (RMSE = 55.4 kPa, MAE = 45.1 kPa, and $R^2=0.988$). The sensitivity analysis demonstrates that the JF inclusion within the input predictors is the most effective parameter on the UCS responses, followed by FTC.

• Geomechanics and Engineering
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• v.30 no.1
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• pp.11-26
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• 2022

One of the most important issues in tunneling, is the squeezing phenomenon. Squeezing can occur during excavation or after the construction of tunnels, which in both cases could lead to significant damages. Therefore, it is important to predict the squeezing and consider it in the early design stage of tunnel construction. Different empirical, semi-empirical and theoretical-analytical methods have been presented to determine the squeezing. Therefore, it is necessary to examine the ability of each of these methods and identify the best method among them. In this study, squeezing in a part of the Alborz service tunnel in Iran was estimated through a number of empirical, semi- empirical and theoretical-analytical methods. Among these methods, the most robust model was used to obtain a database including 300 data for training and 33 data for testing in order to develop a machine learning (ML) method. To this end, three ML models of Gaussian process regression (GPR), artificial neural network (ANN) and support vector regression (SVR) were trained and tested to propose a robust model to predict the squeezing phenomenon. A comparative analysis between the conventional and the ML methods utilized in this study showed that, the GPR model is the most robust model in the prediction of squeezing phenomenon. The sensitivity analysis of the input parameters using the mutual information test (MIT) method showed that, the most sensitive parameter on the squeezing phenomenon is the tangential strain (ε_θ^α) parameter with a sensitivity score of 2.18. Finally, the GPR model was recommended to predict the squeezing phenomenon in tunneling projects. This work's significance is that it can provide a good estimation of the squeezing phenomenon in tunneling projects, based on which geotechnical engineers can take the necessary actions to deal with it in the pre-construction designs.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.137-147
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• 2023

LiDAR is used in autonomous driving and various industrial fields to measure the size and distance of an object. In addition, the sensor also provides intensity images based on the amount of reflected light. This has a positive effect on sensor data processing by providing information on the shape of the object. LiDAR guarantees higher performance as the resolution increases but at an increased cost. These conditions also apply to LiDAR intensity images. Expensive equipment is essential to acquire high-resolution LiDAR intensity images. This study developed artificial intelligence to improve low-resolution LiDAR intensity images into high-resolution ones. Therefore, this study performed parameter analysis for the optimal super-resolution neural network model. The super-resolution algorithm was trained and verified using 2,500 LiDAR intensity images. As a result, the resolution of the intensity images were improved. These results can be applied to the autonomous driving field and help improve driving environment recognition and obstacle detection performance

• Steel and Composite Structures
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• v.51 no.5
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• pp.509-527
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• 2024

Bonding carbon fiber-reinforced polymer (CFRP) laminates have been extensively employed in the restoration of steel constructions. In addition to the mechanical properties of the CFRP, the bond strength (P_U) between the CFRP and steel is often important in the eventual strengthened performance. Nonetheless, the bond behavior of the CFRP-steel (CS) interface is exceedingly complicated, with multiple failure causes, giving the P_U challenging to forecast, and the CFRP-enhanced steel structure is unsteady. In just this case, appropriate methods were established by hybridized Random Forests (RF) and support vector regression (SVR) approaches on assembled CS single-shear experiment data to foresee the P_U of CS, in which a recently established optimization algorithm named Aquila optimizer (AO) was used to tune the RF and SVR hyperparameters. In summary, the practical novelty of the article lies in its development of a reliable and efficient method for predicting bond strength at the CS interface, which has significant implications for structural rehabilitation, design optimization, risk mitigation, cost savings, and decision support in engineering practice. Moreover, the Fourier Amplitude Sensitivity Test was performed to depict each parameter's impact on the target. The order of parameter importance was t_c> L_c > E_A > t_A > E_c > b_c > f_c > f_A from largest to smallest by 0.9345 > 0.8562 > 0.79354 > 0.7289 > 0.6531 > 0.5718 > 0.4307 > 0.3657. In three training, testing, and all data phases, the superiority of AO - RF with respect to AO - SVR and MARS was obvious. In the training stage, the values of R² and VAF were slightly similar with a tiny superiority of AO - RF compared to AO - SVR with R² equal to 0.9977 and VAF equal to 99.772, but large differences with results of MARS.

• Geomechanics and Engineering
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• v.36 no.5
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• pp.465-474
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• 2024

Uniaxial compressive strength (UCS) is a critical geomechanical parameter that plays a significant role in the evaluation of rocks. The practice of indirectly estimating said characteristics is widespread due to the challenges associated with obtaining high-quality core samples. The primary aim of this study is to investigate the feasibility of utilizing the gene expression programming (GEP) technique for the purpose of forecasting the UCS for various rock categories, including Schist, Granite, Claystone, Travertine, Sandstone, Slate, Limestone, Marl, and Dolomite, which were sourced from a wide range of quarry sites. The present study utilized a total of 170 datasets, comprising Schmidt hammer (SH), porosity (n), point load index (Is(50)), and P-wave velocity (Vp), as the effective parameters in the model to determine their impact on the UCS. The UCS parameter was computed through the utilization of the GEP model, resulting in the generation of an equation. Subsequently, the efficacy of the GEP model and the resultant equation were assessed using various statistical evaluation metrics to determine their predictive capabilities. The outcomes indicate the prospective capacity of the GEP model and the resultant equation in forecasting the unconfined compressive strength (UCS). The significance of this study lies in its ability to enable geotechnical engineers to make estimations of the UCS of rocks, without the requirement of conducting expensive and time-consuming experimental tests. In particular, a user-friendly program was developed based on the GEP model to enable rapid and very accurate calculation of rock's UCS, doing away with the necessity for costly and time-consuming laboratory experiments.

• Journal of Korean Tunnelling and Underground Space Association
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• v.15 no.6
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• pp.585-597
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• 2013

In this study, in order to evaluate stability of the room-and-pillar underground structure, a series of preliminary numerical analyses were performed. Design concept and procedure of an underground structure for obtaining a space are proposed, which should be different from structural design for the room-and-pillar in mine. With assumed material properties, a series of numerical analyses were performed by varying size ratios of room and pillar and then the failure modes and location at yielding initiation were investigated. From the results, relationship between the ratio of pillar width to the roof span (w/s) and overburden pressure at failure initiation shows a relatively linear relation, and the effect of w/s on structural stability is much more critical than the ratio of pillar width and height (w/H) which is a crucial parameter in design of the room-and-pillar mining. It means that roof tensile failure and shear failure at shoulder and pillar are necessary to be considered together for confirming overall structural stability of the room-and-pillar structure, rather than considering the pillar stability only in mining. Failure modes and location at failure initiation were varied with respect to the ratio of room and pillar widths. Therefore, it is necessary to simultaneously consider stability of both roof span and pillar for design of underground structure by the room-and-pillar method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.10
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• pp.393-405
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• 2020

Various geotechnical information is required to evaluate the stability of the ground and a foundation once liquefaction occurs due to earthquakes, such as the soil strength and groundwater level. The results of the Standard Penetration Test (SPT) conducted in Korea are registered in the National Geotechnical Information Portal System. If geotechnical information for a non-drilled area is needed, geostatistics can be applied. This paper is about the feasibility of obtaining ground information by the Empirical Bayesian Kriging (EBK) method and the Inverse Distance Weighting Method (IDWM). Esri's ArcGIS Pro program was used to estimate these techniques. The soil strength parameter of the drilling area and the level of groundwater obtained from the standard penetration test were cross-validated with the results of the analysis technique. In addition, Multichannel Analysis of Surface Waves (MASW) was conducted to verify the techniques used in the analysis. The Buk-gu area of Pohang was divided into 1.0 km×1.0 km and 110 zones. The cross-validation for the SPT N value and groundwater level through EBK and IDWM showed that both techniques were suitable. MASW presented an approximate section area, making it difficult to clearly grasp the distribution pattern and groundwater level of the SPT N value.

• Geophysics and Geophysical Exploration
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• v.16 no.2
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• pp.79-90
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• 2013

Geophysical investigation in non-destructive testing is economically less expensive than boring testing and providing geotechnical information over wide-area. But, it provides only limited geotechnical information, which is hardly used to the design. Accordingly, we performed electrical resistivity experiments on large scale of soil model to analyze the correlation between electrical resistivity response and soil water contents. The soils used in the experiments were the Jumunjin standard sand and weathered granite soil. Each soil particle size distribution and coefficient of uniformity of experimental material obtained in the experiments were maintained in a state of the homogeneous. The specifications of the model used in this study is $160{\times}100{\times}50$(cm) of acrylic, and each soil was maintained at the height 30 cm. The water content were measured using the 5TE sensors (water contents sensors) which is installed 7 ~ 8 cm apart vertically by plugging to floor. The results of the resistivity behavior pattern for Jumunjin standard sand was found to be sensitive to the water content, while the weathered granite soil was showing lower resistivity over the time, and there was no significant change in behavior pattern observed. So, it results that the Jumunjin standard sand's particle current conduction was better than the weathered granite soil's particle through contact with the distilled water. This lab test was also compared with the result of a test bed site composed of similar weathered soil. It was confirmed that these experiments were underlying research of non-destructive investigation techniques to improve the accuracy to estimate the geotechnical parameter.

• Journal of the Korean Geotechnical Society
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• v.37 no.2
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• pp.19-31
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• 2021

Micropiles are cast-in-place piles with small diameters. The advantage of micropile is low construction expense and simple procedures, so it is widely applied to existing buildings and structures for the reinforcement of foundation and seismic performances. The base expansion structure has been developed following the original mechanism of horizontal expansion steps under compressive loading. This kind of structure can be installed at the pile end to improve the bearing capacity by tip area enlargement and horizontal force increment to the pile surface area. However, 'Micropile with base expansion structure' cannot be put into practical use, because detailed verification for the developed technique has not been conducted so far. In this research, 3-D numerical analysis was conducted to figure out the bearing mechanism of base expansion micropile and to verify the bearing capacity improvement compared to the general micropiles. 3-D modelling of micropile with base expansion structure was carried out and input parameter was determined. Bearing mechanism induced by base expansion structure was analyzed by lab-scale modelling, and bearing capacity improvement was verified by field-scale analysis.