This study explores development of prediction model for seismic site classification through the integration of machine learning techniques with horizontal-to-vertical spectral ratio (HVSR) methodologies. To improve model accuracy, the research employs outlier detection methods and, synthetic minority over-sampling technique (SMOTE) for data balance, and evaluates using seven machine learning models using seismic data from KiK-net. Notably, light gradient boosting method (LGBM), gradient boosting, and decision tree models exhibit improved performance when coupled with SMOTE, while Multiple linear regression (MLR) and Support vector machine (SVM) models show reduced efficacy. Outlier detection techniques significantly enhance accuracy, particularly for LGBM, gradient boosting, and voting boosting. The ensemble of LGBM with the isolation forest and SMOTE achieves the highest accuracy of 0.91, with LGBM and local outlier factor yielding the highest F1-score of 0.79. Consistently outperforming other models, LGBM proves most efficient for seismic site classification when supported by appropriate preprocessing procedures. These findings show the significance of outlier detection and data balancing for precise seismic soil classification prediction, offering insights and highlighting the potential of machine learning in optimizing site classification accuracy.

The reasonable setting of coal pillar width plays a key role in guaranteeing the steadiness of surrounding rock of fully mechanized caving gateroad driving along the next goaf. Based on the engineering background of the Bayangaole mine, the discrete element method was used to simulate the fracture evolution of coal pillars with different pillar widths. The results show that the damage rate of the coal pillar increases with the decrease in the width of the coal pillar. Once the coal pillar width is smaller than 6 m, cracks run through the coal pillar, and the coal pillar is completely damaged. In the middle of the coal pillar, which has a width of 6 m and above, there is a relatively complete area with low damage. The results show that the pillar width of 6 m is the most appropriate. Field tests prove that the reserved width of a 6 m small coal pillar can effectively control the surrounding rock deformation, ensuring the overall steadiness of the gateroad in the thick coal seam. It is hoped that this study will offer some reference for the determination of the reasonable size of the coal pillar.

In this study, the earthquake risk assessment of single-story RC precast buildings in Turkey was carried out using loss curves. In this regard, Kocaeli, a seismically active city in the Marmara region, and this building class, which is preferred intensively, were considered. Quality and period parameters were defined based on structural and geometric properties. Depending on these parameters, nine main sub-classes were defined to represent the building stock in the region. First, considering the mean fragility curves and four different central damage ratio models, vulnerability curves for each sub-class were computed as a function of spectral acceleration. Then, probabilistic seismic hazard analyses were performed for stiff and soft soil conditions for different earthquake probabilities of exceedance in 50 years. In the last step, 90 loss curves were derived based on vulnerability and hazard results. Within the scope of the study, the comparative parametric evaluations for three different earthquake intensity levels showed that the structural damage ratio values for nine sub-classes changed significantly. In addition, the quality parameter was found to be more effective on a structure's damage state than the period parameter. It is evident that since loss curves allow direct loss ratio calculation for any hazard level without needing seismic hazard and damage analysis, they are considered essential tools in rapid earthquake risk estimation and mitigation initiatives.

With increasing demand for nuclear power generation, nuclear structures are being planned and constructed worldwide. A grave safety concern is that these structures are sensitive to large-magnitude shaking, e.g., during earthquakes. Seismic response analysis, which requires P- and S-wave velocities, is a key element in nuclear structure design. Accordingly, it is important to determine the P- and S-wave velocities in the Gyeongju and Pohang regions of South Korea, which are home to nuclear power plants and have a history of seismic activity. P- and S-wave velocities can be obtained indirectly through a correlation with physical properties (e.g., N values, Young's modulus, and uniaxial compressive strength), and researchers worldwide have proposed regression equations. However, the Gyeongju and Pohang regions of Korea have not been considered in previous studies. Therefore, a database was constructed for these regions. The database includes physical properties such as N values and P- and S-wave velocities of the soil layer, as well as the uniaxial compressive strength, Young's modulus, and P- and S-wave velocities of the bedrock layer. Using the constructed database, the geological characteristics and distribution of physical properties of the study region were analyzed. Furthermore, models for predicting P- and S-wave velocities were developed for soil and bedrock layers in the Gyeongju and Pohang regions. In particular, the model for predicting the S-wave velocity for the soil layers was compared with models from previous studies, and the results indicated its effectiveness in predicting the S-wave velocity for the soil layers in the Gyeongju and Pohang regions using the N values. The proposed models for predicting P- and S-wave velocities will contribute to predicting the damage caused by earthquakes.

The purpose of this paper is to depict the effect of gravity on a nonlocal thermoelastic medium with initial stress. The Lord-Shulman and Green-Lindsay theories with fractional derivative order serve as the foundation for the formulation of the fundamental equations for the problem. To address the problem and acquire the exact expressions of physical fields, appropriate non-dimensional variables and normal mode analysis are used. MATLAB software is used for numerical calculations. The projected outcomes in the presence and absence of the gravitational field, along with a nonlocal parameter, are compared. Additional comparisons are made for various fractional derivative order values. It is evident that the variation of physical variables is significantly influenced by the fractional derivative order, nonlocal parameter and gravity field.

To evaluate the safety status of deteriorated segments in a submarine shield tunnel during its service life, a seepage model was established based on a cross-sea shield tunnel project. This model was used to study the migration patterns of erosive ions within the shield segments. Based on these laws, the degree of deterioration of the segments was determined. Using the derived analytical solution, the internal forces within the segments were calculated. Lastly, by applying the formula for calculating safety factors, the variation trends in the safety factors of segments with different degrees of deterioration were obtained. The findings demonstrate that corrosive seawater presents the evolution characteristics of continuous seepage from the outside to the inside of the tunnel. The nearby seepage field shows locally concentrated characteristics when there is leakage at the joint, which causes the seepage field's depth and scope to significantly increase. The chlorine ion content decreases gradually with the increase of the distance from the outer surface of the tunnel. The penetration of erosion ions in the segment is facilitated by the presence of water pressure. The ion content of the entire ring segment lining structure is related in the following order: vault < haunch < springing. The difference in the segment's rate of increase in chlorine ion content decreases as service time increases. Based on the analytical solution calculation, the segment's safety factor drops more when the joint leaks than when its intact, and the change rate between the two states exhibits a general downward trend. The safety factor shows a similar change rule at different water depths and continuously decreases at the same segment position as the water depth increases. The three phases of "sudden drop-rise-stability" are represented by a "spoon-shaped" change rule on the safety factor's change curve. The issue of the poor applicability of indicators in earlier studies is resolved by the analytical solution, which only requires determining the loss degree of the segment lining's effective bearing thickness to calculate the safety factor of any cross-section of the shield tunnel. The analytical solution's computation results, however, have some safety margins and are cautious. The process of establishing the evaluation model indicates that the secondary lining made of molded concrete can also have its safety status assessed using the analytical solution. It is very important for the safe operation of the tunnel and the safety of people's property and has a wide range of applications.

Direct simple shear test is an effective method to measure strength and deformation properties of soil. However, existing direct simple shear apparatus have some shortcomings. The paper has developed a novel dual stress/strain-controlled direct simple shear apparatus. The novel apparatus has the following advantages: A rectangular specimen is used that effectively avoid common issues associated with conventional cylindrical specimens, such as specimen tilting. The utilization of deformation control rods ensures a uniform shear deformation of the specimen. Vertically integrated force transmission structure is improved that avoids issues arising from changes in pivot points due to lever tilting. Incorporating this novel direct simple shear apparatus, shear strength and shear creep tests of clay were performed. Shear strength parameters and shear creep behaviors are analyzed. The results of these experiments show that the novel apparatus can measure accurately the shear rheological properties of soil. This study provides strong guidance for studying the mechanical properties of soil in engineering practice.

The presence of excavations or cavities beneath the foundations of a building can have a significant impact on their stability and cause extensive damage. Traditional methods for calculating the bearing capacity and subsidence of foundations over cavities can be complex and time-consuming, particularly when dealing with conditions that vary. In such situations, machine learning (ML) and deep learning (DL) techniques provide effective alternatives. This study concentrates on constructing a prediction model based on the performance of ML and DL algorithms that can be applied in real-world settings. The efficacy of eight algorithms, including Regression Analysis, k-Nearest Neighbor, Decision Tree, Random Forest, Multivariate Regression Spline, Artificial Neural Network, and Deep Neural Network, was evaluated. Using a Python-assisted automation technique integrated with the PLAXIS 2D platform, a dataset containing 272 cases with eight input parameters and one target variable was generated. In general, the DL model performed better than the ML models, and all models, except the regression models, attained outstanding results with an R² greater than 0.90. These models can also be used as surrogate models in reliability analysis to evaluate failure risks and probabilities.