Georisk-Assessment and Management of Risk for Engineered Systems and G

Papers
(The H4-Index of Georisk-Assessment and Management of Risk for Engineered Systems and G is 14. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2021-08-01 to 2025-08-01.)
ArticleCitations
Effects of random heterogeneity of soil on VH failure envelopes of the torpedo anchor118
Numerical modelling of the keying process for a suction embedded plate anchor in spatially varying clays54
Experimental modelling of spudcan penetration in spatially variable soils24
A real-time intelligent classification model using machine learning for tunnel surrounding rock and its application19
Reliability-based optimization in climate-adaptive design of embedded footing19
Worst-case effect in bearing capacity of spread foundations considering safety factors and anisotropy in soil spatial variability19
The modified life cycle cost method for the risk-based design of excavation projects18
Cross-project utilisation of tunnel boring machine (TBM) construction data: a case study using big data from Yin-Song diversion project in China16
Time series analysis and gated recurrent neural network model for predicting landslide displacements15
Effect of uncertainties in geometry, inter-layer boundary and shear strength properties on the probabilistic stability of a 3D embankment slope15
Risk prediction of coal mine rock burst based on machine learning and feature selection algorithm14
On the use of different data assimilation schemes in a fully coupled hydro-mechanical slope stability analysis14
Characterizing multivariate, asymmetric, and multimodal distributions of geotechnical data with dual-stage missing values: BASIC-H14
Load and resistance factor design versus reliability-based design of shallow foundations14
Report for ISSMGE TC309/TC304/TC222 and ASCE Geo-Institute Risk Assessment and Management Committee Fourth Machine Learning in Geotechnics Dialogue on “Machine Learning Supremacy Projects”14
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