English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In this study, based on compiled numerical-experimental datasets, important variables, including porosity, density, compressional and shear wave velocity, were utilised to develop a novel model for predicting coal permeability with high accuracy. Multiple linear regression, response surface methodology and gene expression programming were employed to develop empirical models based on the foregoing variables. The performance of the developed models was evaluated using statistical indices. The results showed that the GEP-based model has the best prediction performance compared with other techniques. The proposed GEP-based model with its explicit structure can be readily used in practical applications by field engineers.

A data-driven field-scale approach to estimate the permeability of fractured rocks