Hang Lin

Prediction of Joint Roughness Coefficient Using Metaheuristic-Optimized Random Forest Model

Reliable estimation of the joint roughness coefficient (JRC) is important for quantitative characterization of joint roughness and provides an important geometric parameter for subsequent joint shear strength assessment. However, single-parameter empirical models cannot fully capture the combined geometric characterist...

Estimation of Rock Shear Strength Parameters from Geophysical Indicators Using an Equilibrium-Optimized Multilayer Perceptron

A hybrid machine learning framework for estimating cohesion and internal friction angle from geophysical and mechanical indicators is developed and provides an indirect data-driven tool for estimating shear strength parameters and can complement engineering-geological investigation and rock engineering design.

Transmission of Seismic Waves With Tensile Components Across Cemented Rock Discontinuities Exhibiting Tension‐Softening Behavior

Fault slip involves not only shear but also tensile motions, causing repeated opening and closure of fault‐zone discontinuities. This cyclic deformation progressively changes the mechanical stiffness, fundamentally impacting seismic wave transmission. However, most existing analytical models neglect tensile component...

Factor of safety prediction for open-pit slope using IoT-enabled hybrid machine learning digital twin with real-time adaptive updating