• Geomechanics and Engineering
• /
• v.17 no.1
• /
• pp.81-95
• /
• 2019

Due to the importance of soil reinforcement using geotextiles in geotechnical engineering, study and investigation into long-term performance, design life and survivability of geotextiles, especially due to installation damage are necessary and will affect their economy. During installation, spreading and compaction of backfill materials, geotextiles may encounter severe stresses which can be higher than they will experience in-service. This paper aims to investigate the installation damage of geotextiles, in order to obtain a good approach to the estimation of the material's strength reduction factor. A series of full-scale tests were conducted to simulate the installation process. The study includes four deliberately poorly-graded backfill materials, two kinds of subgrades with different CBR values, three nonwoven needle-punched geotextiles of classes 1, 2 and 3 (according to AASHTO M288-08) and two different relative densities for the backfill materials. Also, to determine how well or how poorly the geotextiles tolerated the imposed construction stresses, grab tensile tests and visual inspections were carried out on geotextile specimens (before and after installation). Visual inspections of the geotextiles revealed sedimentation of fine-grained particles in all specimens and local stretching of geotextiles by larger soil particles which exerted some damage. A regression model is proposed to reliably predict the installation damage reduction factor. The results, obtained by grab tensile tests and via the proposed models, indicated that the strength reduction factor due to installation damage was reduced as the median grain size and relative density of the backfill decreases, stress transferred to the geotextiles' level decreases and as the as-received grab tensile strength of geotextile and the subgrades' CBR value increase.

• Journal of the Korean GEO-environmental Society
• /
• v.12 no.8
• /
• pp.13-24
• /
• 2011

In the part of geotechnical engineering, soils are classified as either the coarse grained soil or the fine-grained soil following the fine content($F_c$=50%) according to the granularity, and appropriate design codes are used respectively to represent their mechanical behaviour. However, sand-clay mixtures, which are typically referred to as intermediate soils, cannot be easily categorized as either sand or clay. In this study, several monotonic undrained shear tests were carried out on Silica sand fine mixtures with various proportions, and a wide range of soil structures, ranging from one with sand dominating the soil structure to one with fines controlling the behaviour, were prepared using compaction method or pre-consoldation methods in prescribed energy. The shear strength of mixtures below the threshold fines content is observed that as the fines content increases, maximum deviator stress ratio decrease for dense samples while an increase is noted for loose samples. Then, by using the concept of fines content and granular void ratio, the monotonic shear strength of the mixtures was estimated. It was found that the shear behavior of mixtures is greatly dependent on the skeleton structure of sand particles.

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

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.23 no.6
• /
• pp.485-502
• /
• 2021

In order to evaluate the impact of ground subsidence and superstructures that are inevitably caused by tunnel excavation, a total of seven major influencing factors of surface subsidence and structural soundness reduction were set, and a Parameter Study using numerical analysis was conducted. Stability analysis was performed using scheme of Boscardin and Cording method and the maximum subsidence amount and the angular displacement, and correlation analysis was performed for each major influencing factor. In addition, it was applied that used the mutual behavior of the ground and the structure by parameter analysis in the site of the 𐩒𐩒𐩒 tunnel located in Hwaseong-si, Gyeonggi-do, and the applicability of the site was analyzed. As a result, the error was found to be 1.0%, and it could be used as a basic material for determining the appropriate tunnel route under various conditions when evaluating the stability of the structure according to tunnel excavating at the design stage.

• Journal of the Korean Geosynthetics Society
• /
• v.21 no.3
• /
• pp.21-27
• /
• 2022

For carbon neutrality, interest in R&D and infrastructure construction for hydrogen energy, an eco-friendly energy source, is growing worldwide. In particular, for hydrogen stations installed in downtown areas, underground hydrogen infrastructure are being considered to increase a safety distance from hydrogen tank explosions to adjacent structures. In order to design an appropriate location and depth of the underground hydrogen infrastructure, it is necessary to evaluate the impact of the explosion of the underground hydrogen infrastructure on adjacent structures. In this paper, a numerical model was developed to analyze the effect of the underground hydrogen infrastructure explosion on adjacent structures, and the over pressure of the hydrogen tank was evaluated using the equivalent TNT (Trinitrotoluene) model. In addition, parametric analysis was performed to estimate the stability of adjacent structures according to the construction conditions of the underground hydrogen infrastructure.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.24 no.3
• /
• pp.279-292
• /
• 2022

Unexpected anomalies in the geotechnical investigation at design stage may cause problems during tunnel excavation. Therefore, it is important to predict the ground condition ahead of a tunnel face during tunnel excavation in order to prevent tunnel collapse. Despite the fulfillment of an electrical resistivity survey at the tunnel face, the existing electrical resistivity survey program can produce distorted results by the limitation of tunnel modelling. In this background, this study develops a modelling program for an electrical resistivity survey considering the tunnel shape. Numerical simulation and inverse calculation were performed for the electrical resistivity survey in the tunnel using the developed program. As a result, it was proved that the developed program could predict accurately the anomalous object's location and condition ahead of the tunnel face.

• The Journal of Engineering Geology
• /
• v.33 no.1
• /
• pp.151-167
• /
• 2023

This study compares the revised method in loose saturated sandy ground where the LNG storage tank will be installed with an evaluation method by one-dimensional effective stress analysis using the UBC3D-PLM model. Various laboratory and field tests were conducted to establish the parameters necessary for evaluation. The revised liquefaction evaluation method using the seismic response analysis result and N value from standard penetration testing evaluated the possibility of liquefaction as high, but assessment using effective stress analysis, which can consider various liquefaction resistance factors, found the site to be somewhat stable against liquefaction. One-dimensional finite element analysis using UBC3D-PLM modeling facilitated easier assessment of stability against liquefaction than the other methods and minimized the area required for reinforcement against liquefaction. In addition, it is expected that two-and three-dimensional numerical analysis considering the foundation of the LNG storage tank can identify the seismic design and behavior when liquefaction occurs.

• Computers and Concrete
• /
• v.32 no.3
• /
• pp.327-337
• /
• 2023

Concrete is the most widely used building material, with various types including high- and ultra-high-strength, reinforced, normal, and lightweight concretes. However, accurately predicting concrete properties is challenging due to the geotechnical design code's requirement for specific characteristics. To overcome this issue, researchers have turned to new technologies like machine learning to develop proper methodologies for concrete specification. In this study, we propose a highly accurate deep learning-based predictive model to investigate the compressive strength (UCS) of lightweight concrete with natural aggregates (pumice). Our model was implemented on a database containing 249 experimental records and revealed that water, cement, water-cement ratio, fine-coarse aggregate, aggregate substitution rate, fine aggregate replacement, and superplasticizer are the most influential covariates on UCS. To validate our model, we trained and tested it on random subsets of the database, and its performance was evaluated using a confusion matrix and receiver operating characteristic (ROC) overall accuracy. The proposed model was compared with widely known machine learning methods such as MLP, SVM, and DT classifiers to assess its capability. In addition, the model was tested on 25 laboratory UCS tests to evaluate its predictability. Our findings showed that the proposed model achieved the highest accuracy (accuracy=0.97, precision=0.97) and the lowest error rate with a high learning rate (R2=0.914), as confirmed by ROC (AUC=0.971), which is higher than other classifiers. Therefore, the proposed method demonstrates a high level of performance and capability for UCS predictions.

• Geomechanics and Engineering
• /
• v.32 no.6
• /
• pp.583-600
• /
• 2023

The resilient modulus (M_R) of various pavement materials plays a significant role in the pavement design by a mechanistic-empirical method. The M_R determination is done by experimental tests that need time and money, along with special experimental tools. The present paper suggested a novel hybridized extreme gradient boosting (XGB) structure for forecasting the M_R of modified base materials subject to wet-dry cycles. The models were created by various combinations of input variables called deep learning. Input variables consist of the number of W-D cycles (WDC), the ratio of free lime to SAF (CSAFR), the ratio of maximum dry density to the optimum moisture content (DMR), confining pressure (σ₃), and deviatoric stress (σ_d). Two XGB structures were produced for the estimation aims, where determinative variables were optimized by particle swarm optimization (PSO) and black widow optimization algorithm (BWOA). According to the results' description and outputs of Taylor diagram, M1 model with the combination of WDC, CSAFR, DMR, σ₃, and σ_d is recognized as the most suitable model, with R² and RMSE values of BWOA-XGB for model M1 equal to 0.9991 and 55.19 MPa, respectively. Interestingly, the lowest value of RMSE for literature was at 116.94 MPa, while this study could gain the extremely lower RMSE owned by BWOA-XGB model at 55.198 MPa. At last, the explanations indicate the BWO algorithm's capability in determining the optimal value of XGB determinative parameters in M_R prediction procedure.

• Computers and Concrete
• /
• v.32 no.2
• /
• pp.149-163
• /
• 2023

One of the main design parameters traditionally utilized in projects of geotechnical engineering is the uniaxial compressive strength. The present paper employed three artificial intelligence methods, i.e., the stochastic fractal search (SFS), the multi-verse optimization (MVO), and the vortex search algorithm (VSA), in order to determine the compressive strength of concrete (CSC). For the same reason, 1030 concrete specimens were subjected to compressive strength tests. According to the obtained laboratory results, the fly ash, cement, water, slag, coarse aggregates, fine aggregates, and SP were subjected to tests as the input parameters of the model in order to decide the optimum input configuration for the estimation of the compressive strength. The performance was evaluated by employing three criteria, i.e., the root mean square error (RMSE), mean absolute error (MAE), and the determination coefficient (R²). The evaluation of the error criteria and the determination coefficient obtained from the above three techniques indicates that the SFS-MLP technique outperformed the MVO-MLP and VSA-MLP methods. The developed artificial neural network models exhibit higher amounts of errors and lower correlation coefficients in comparison with other models. Nonetheless, the use of the stochastic fractal search algorithm has resulted in considerable enhancement in precision and accuracy of the evaluations conducted through the artificial neural network and has enhanced its performance. According to the results, the utilized SFS-MLP technique showed a better performance in the estimation of the compressive strength of concrete (R²=0.99932 and 0.99942, and RMSE=0.32611 and 0.24922). The novelty of our study is the use of a large dataset composed of 1030 entries and optimization of the learning scheme of the neural prediction model via a data distribution of a 20:80 testing-to-training ratio.