Miguel A. Millán^1,*, Rubén A. Galindo², Fausto Molina-Gómez¹

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2923-2941, 2025, DOI:10.32604/cmes.2025.069356 - 30 September 2025

Abstract Volcanic terrains exhibit a complex structure of pyroclastic deposits interspersed with sedimentary processes, resulting in irregular lithological sequences that lack lateral continuity and distinct stratigraphic patterns. This complexity poses significant challenges for slope stability analysis, requiring the development of specialized techniques to address these issues. This research presents a numerical methodology that incorporates spatial variability, nonlinear material characterization, and probabilistic analysis using a Monte Carlo framework to address this issue. The heterogeneous structure is represented by randomly assigning different lithotypes across the slope, while maintaining predefined global proportions. This contrasts with the more common approach… More >

Junwei Han¹, Guohua Li¹, Wu Zhong¹, Yuchen Yang¹, Maoheng Li^2,3, Zhiwei Chen^2,3, Ruichang Ge^2,3, Lijuan Huang^2,3,*

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.12, pp. 2757-2773, 2024, DOI:10.32604/fdmp.2024.056130 - 23 December 2024

Abstract A comprehensive method to evaluate the factors affecting the production capacity of horizontal wells in Carboniferous volcanic rocks after fracturing is investigated. A systematic approach combining gray correlation analysis, hierarchical analysis and fuzzy evaluation is proposed. In particular, first the incidence of reservoir properties and fracturing parameters on production capacity is assessed. These parameters include reservoir base geological parameters (porosity, permeability, oil saturation, waterproof height) as well as engineering parameters (fracture half-length, fracture height, fracture conductivity, fracture distance). Afterwards, a two-by-two comparison judgment matrix of sensitive parameters is constructed by means of hierarchical analysis, and More >

Jiaqi Wang¹, Pengfei Sun¹, Leilei Chen², Jianfeng Yang³, Zhenghe Liu¹, Haojie Lian^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1381-1418, 2023, DOI:10.32604/cmes.2023.023693 - 26 June 2023

Abstract Geological hazard is an adverse geological condition that can cause loss of life and property. Accurate prediction and analysis of geological hazards is an important and challenging task. In the past decade, there has been a great expansion of geohazard detection data and advancement in data-driven simulation techniques. In particular, great efforts have been made in applying deep learning to predict geohazards. To understand the recent progress in this field, this paper provides an overview of the commonly used data sources and deep neural networks in the prediction of a variety of geological hazards. More >