This research studies the effect of geotechnical factors on EPB-TBM performance parameters. The modeling was performed using simple and multivariate linear regression methods, artificial neural networks (ANNs), and Sugeno fuzzy logic (SFL) algorithm. In ANN, 80% of the data were randomly allocated to training and 20% to network testing. Meanwhile, in the SFL algorithm, 75% of the data were used for training and 25% for testing. The coefficient of determination (R²) obtained between the observed and estimated values in this model for the thrust force and cutterhead torque was 0.19 and 0.52, respectively. The results showed that the SFL outperformed the other models in predicting the target parameters. In this method, the R² obtained between observed and predicted values for thrust force and cutterhead torque is 0.73 and 0.63, respectively. The sensitivity analysis results show that the internal friction angle (φ) and standard penetration number (SPT) have the greatest impact on thrust force. Also, earth pressure and overburden thickness have the highest effect on cutterhead torque.

Determination of jointed rock mass properties plays a significant role in the design and construction of underground structures such as tunneling and mining. Rock mass classification systems such as Rock Mass Rating (RMR), Rock Mass Index (RMi), Rock Mass Quality (Q), and deformation modulus (Em) are determined from the jointed rock masses. However, parameters of jointed rock masses can be affected by the tunnel depth below the surface due to the effect of the in situ stresses. In addition, the geomechanical properties of rocks change due to the effect of metamorphism. Therefore, the main objective of this study is to apply correlation analysis to investigate the relationships between rock mass properties and some parameters related to the depth of the tunnel studied. For this purpose, the field work consisted of determining rock mass parameters in a tunnel alignment (~7.1 km) at varying depths from 21 m to 431 m below ground surface. At the same excavation depths, thirty-seven rock types were also sampled and tested in the laboratory. Correlations were made between vertical stress and depth, horizontal/vertical stress ratio (k) and depth, k and Em, k and RMi, k and point load index (PLI), k and Brazilian tensile strength (BTS), Em and uniaxial compressive strength (UCS), UCS and PLI, UCS and BTS. Relationships were significant (significance level=0.000) at the confidence interval of 95% (r = 0.77-0.88) between the data pairs for the rocks taken from depths greater than 166 m where the ratio of horizontal to vertical stress is between 0.6 and 1.2. The in-situ stress parameters affected rock mass properties as well as metamorphism which affected the geomechanical properties of rock materials by affecting the behavior of minerals and textures within rocks. This study revealed that in-situ stress parameters and metamorphism should be reviewed when tunnel studies are carried out.

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.

In the current work, a poro-thermoelastic half-space issue with temperature-dependent characteristics and an inclined load is examined in the framework of the three-phase-lag model (3PHL) while taking into account the effects of magnetic and gravity fields. The resulting coupled governing equations are non-dimensional and are solved by normal mode analysis. To investigate the impacts of the gravitational field, magnetic field, inclined load, and an empirical material constant, numerical findings are graphically displayed. MATLAB software is used for numerical calculations. Graphs are used to visualize and analyze the computational findings. It is found that the physical quantities are affected by the magnetic field, gravity field, the nonlocal parameter, the inclined load, and the empirical material constant.

Dynamic properties are pivotal in soil analysis, yet their experimental determination is hampered by complex methodologies and the need for costly equipment. This study aims to predict dynamic soil properties using static properties that are relatively easier to obtain, employing machine learning techniques. The static properties considered include soil cohesion, friction angle, water content, specific gravity, and compressional strength. In contrast, the dynamic properties of interest are the velocities of compressional and shear waves. Data for this study are sourced from 26 boreholes, as detailed in a geotechnical investigation report database, comprising a total of 130 data points. An importance analysis, grounded in the random forest algorithm, is conducted to evaluate the significance of each dynamic property. This analysis informs the prediction of dynamic properties, prioritizing those static properties identified as most influential. The efficacy of these predictions is quantified using the coefficient of determination, which indicated exceptionally high reliability, with values reaching 0.99 in both training and testing phases when all input properties are considered. The conventional method is used for predicting dynamic properties through Standard Penetration Test (SPT) and compared the outcomes with this technique. The error ratio has decreased by approximately 0.95, thereby validating its reliability. This research marks a significant advancement in the indirect estimation of the relationship between static and dynamic soil properties through the application of machine learning techniques.

Artificial Neural Networks (ANNs) are artificial learning algorithms that provide successful results in solving many machine learning problems such as classification, prediction, object detection, object segmentation, image and video classification. There is an increasing number of studies that use ANNs as a prediction tool in soil classification. The aim of this research was to understand the role of hyperparameter optimization in enhancing the accuracy of ANNs for soil type classification. The research results has shown that the hyperparameter optimization and hyperparamter optimized ANNs can be utilized as an efficient mechanism for increasing the estimation accuracy for this problem. It is observed that the developed hyperparameter tool (HyperNetExplorer) that is utilizing the Covariance Matrix Adaptation Evolution Strategy (CMAES), Genetic Algorithm (GA) and Jaya Algorithm (JA) optimization techniques can be successfully used for the discovery of hyperparameter optimized ANNs, which can accomplish soil classification with 100% accuracy.

The "Korea Institute of Geoscience and Mineral (KIGAM) Quake" is a web-based open platform developed for publicly serving seismological data from 61 stations operated by KIGAM in Korea. The service provides meta-information related to observatory sites, sensors, and recorders necessary for utilizing the seismological data, as well as mainly observed continuous and strong-motion waveforms. The data is available through both the web and International Federation of Digital Seismograph Networks (FDSN) web services (open API), a unified data-providing interface in seismology. The platform aims to strengthen its open nature by offering a signal processing function for strong ground motions that can be controlled by user requests. The processed results can be downloaded in ASCII format, designed to meet the increased demands and accessibility in the earthquake engineering field. The platform also offers earthquake research information produced by KIGAM, such as recent major earthquake source information and academic annual report of earthquakes. Additionally, a site flat file was constructed for the geotechnical characteristics of 61 KIGAM station (KGNET) sites based on direct investigations and estimations.

Influenced by the alternating effects of dynamic and static pressure during the mining process of close range coal seams, the surrounding rock support of cross mining roadway is difficult and the deformation mechanism is complex, which has become an important problem affecting the safe and efficient production of coal mines. The paper takes the inclined longwall mining of the 10304 working face of Zhongheng coal mine as the engineering background, analyzes the key strata fracture mechanism of the large inclined right-angle trapezoidal mining field, explores the stress distribution characteristics and transmission law of the surrounding rock of the roadway affected by the mining of the inclined coal seam, and proposes a segmented and hierarchical support method for the cross mining roadway affected by the mining of the close range coal seam group. The research results indicate that based on the derived expressions for shear and tensile fracture of key strata, the ultimate pushing distance and ultimate suspended area of a right angle trapezoidal mining area can be calculated and obtained. Within the cross mining section, along the horizontal direction of the coal wall of the working face, the peak shear stress is located near the middle of the boundary. The cracks on the floor of the cross mining roadway gradually develop in an elliptical funnel shape from the shallow to the deep. The dual coupling support system composed of active anchor rod support and passive U-shaped steel shed support proposed in this article achieves effective control of the stability of cross mining roadways, which achieves effective control of floor by coupling active support and preventive passive support to improve the strength of the surrounding rock itself. The research results are of great significance for guiding the layout, support control, and safe mining of cross mining roadways, and to some extent, can further enrich and improve the relevant theories of roof movement and control.