Predicting compressive strength of hollow concrete prisms using machine learning techniques and explainable artificial intelligence (XAI).

The design of masonry structures requires accurate estimation of compressive strength (CS) of hollow concrete masonry prisms. Generally, the CS of masonry prisms is determined by destructive laboratory testing which results in time and resource wastage. Thus, this study aims to provide machine learning-based predictive models for CS of hollow concrete masonry blocks using different algorithms including Multi Expression Programming (MEP), Random Forest Regression (RFR), and Extreme Gradient Boosting (XGB) etc.

Method for rock fracture prediction and early warning: Insight from fusion of multi-physics field information.

Understanding the precursors leading to rock fracture is crucial for ensuring safety in mining and geotechnical engineering projects. To effectively discern these precursors, a collaborative monitoring approach that integrates multiple sources of information is imperative. This paper considered a rock multi-parameter monitoring loading system, incorporating infrared radiation and acoustic emission monitoring technologies to simultaneously track the rock fracture process.

Investigation of the acoustic emission and fractal characteristics of coal with varying water contents during uniaxial compression failure.

To investigate the effect of water on the mechanical properties and acoustic emission (AE) characteristics of coal in the failure and deformation processes. Coal samples of different content were subjected to uniaxial compression tests and AE signals were monitored. The characteristics of the AE signals were further analyzed using fractal analysis.

Cattaneo-Christov heat flow model for copper-water nanofluid heat transfer under Marangoni convection and slip conditions.

This report is devoted to the study of the flow of MHD nanofluids through a vertical porous plate with a temperature-dependent surface tension using the Cattaneo-Christov heat flow model. The energy equation was formulated using the Cattaneo-Christov heat flux model instead of Fourier's law of heat conduction. The Tiwari-Das model was used to take into account the concentration of nanoparticles when constructing the momentum equation.