Geological Journal最新文献

The state parameter (ѱ) accounts for both relative density and effective stress, which influence the cyclic stress or liquefaction characteristic of the soil significantly. This study presents a ѱ-based probabilistic liquefaction evaluation method using six soft computing (SC) techniques. The liquefaction probability of failure (P_L) is calculated using the first-order second moment (FOSM) method based on the cone penetration test (CPT) database. Then, six SC techniques, such as Gaussian process regression (GPR), relevance vector machine (RVM), functional network (FN), genetic programming (GP), minimax probability machine regression (MPMR) and multivariate adaptive regression splines (MARS), are used to predict P_L. The performance of these models is examined using nine statistical indices. Additionally, plots such as regression plots, Taylor diagrams, error matrix and rank analysis are shown to assess the SC model's performance. Finally, sensitivity analysis is performed using the cosine amplitude method (CAM) to assess the influence of input parameters on output. The current study demonstrates that SC models based on state parameter predict P_L effectively. RVM and MPMR models closely follow the GPR model in terms of performance, which is superior to the other models. Notably, two equations are generated using GP and MARS models to predict P_L. The results of the sensitivity analysis reveal the magnitude of earthquake (M_w) as the most sensitive parameter. The outcomes of this research will offer risk evaluations for geotechnical engineering designs and expand the use of state parameter-based SC models in liquefaction analysis.

Tunnel engineering is a complex and multidisciplinary field that requires the integration of geological expertise, advanced modeling techniques, and practical engineering solutions. The research compiled in the Special Issue "Tunnels and Tunneling" makes significant contributions to the field by addressing the diverse geological conditions and intricate challenges inherent in tunnel construction. These insights are crucial for enhancing the safety, efficiency, and sustainability of tunnel projects worldwide. The studies in this Special Issue provide a comprehensive understanding of the various challenges and innovative solutions in tunnel engineering. They offer valuable insights and practical guidelines for designing, constructing, and maintaining safe and stable tunnel structures across different geological settings. In addition, geological challenges in specific regions, such as the Three Gorges Reservoir area, the Hengduan Mountains, and the Tibetan Plateau, require tailored approaches. A key theme in many of the comparative studies is the importance of accurate risk assessment to ensure tunnel safety. In regions prone to geological hazards, landslide susceptibility mapping and risk assessment are critical. Innovative approaches, such as machine learning models, are highlighted for their potential to predict and manage landslide risks effectively.

In this article, the influence of soil condition on the dynamic response of a tunnel and the surrounding soil was studied by both experimental model tests and numerical simulations. We tested a 1/20-scale tunnel model with three different soil conditions: upper soft soil and lower hard soil, homogeneous soft soil and homogeneous hard soil. We also applied dynamic loads, sweep loads and train loads on the model tunnel for time domain and frequency domain analysis. The experimental and numerical results revealed that the interface between the soft and hard soil strata has an obvious amplification effect on the vibration wave. With the propagation of the vibration wave to the surface, the damping effect of the soil above the tunnel becomes the main factor affecting the dynamic response of soil. The internal force response of the tunnel structure is for the most part concentrated in the section under the excitation load, which is mainly affected by the soil properties beneath the tunnel.

With the advent of new terminologies to categorize and characterize the simple and complex monogenetic volcanoes, also came the semantic issues, which caused a predicament for the usage of terms like simple, complex, polycyclic, polymagmatic, complex monogenetic volcanoes with polygenetic inheritance. To analyse and validate this nomenclature, we studied an overlapping volcanic structure located south of the present-day town of Irapuato, Central Mexico, that appears to be a monogenetic complex at first sight. Field observations, tephra stratigraphy, petrography and geochemistry of the tephra deposits confirms that the structure is in fact a cluster of three simple (San Joaquin tuff ring and two scoria mounds) and one complex, polycyclic, polymagmatic (La Sanabria-San Roque tuff ring) monogenetic volcanoes formed by independent events, governed by distinct conduits and magma bodies of different origin (subduction-related, OIB and E-MORB origin) and separated by different tephra sequences of dissimilar components and depositional characteristics. We estimate the magma volumes (using the juvenile content and their vesicularity percentage) to be at 0.40–1.31 × 10⁸, 0.25 × 10⁸ and 0.42–0.90 × 10⁸ m³ for San Joaquin, La Sanabria and San Roque, reckoning an eruption duration of 77 and 48 and 81 days, respectively (considering an average eruption rate of 6 m³/s from the well-documented shallow crater (<30 m), Ukinrek maars in Alaska) occurring within the age range of 40–70 k years (crater diameter and depth ratio). This study not only aided to validate the above-mentioned terms for monogenetic volcanoes, but also reconsider a few of them and avoid confusion with the polygenetic counterparts.