Machine Learning and Data Science in Geotechnics Cover Image for Volume 1, Issue 1
Current Issue
Volume 1,
Issue 1,
15 December 2025

About the Journal

Machine Learning and Data Science in Geotechnics aims to disseminate original contributions in the emerging fields of machine learning, artificial intelligence, big data analysis, and statistical approaches, with a focus on addressing various geotechnical engineering challenges.

You can publish an open access article in this gold open access journal by paying an article processing charge (APC).

Submission information

Accepted manuscript types and word counts

  • Research Paper — up to 5000 words
  • Editorial — up to 1000 words

Please see the article types and word counts section on the about this journal page for more information. The word count includes all text, figures and tables. When estimating your total, please allow 280 words for each figure or table. Read more about the article types for this journal.

Reference style

Use the Harvard reference style. Read more about the reference types for this journal.

Keywords

Please include up to 6 short, relevant keywords that describe the main topics of your paper. You must include a minimum of three keywords from this MS Excel file. Download the keyword excel file for this journal.

Peer review type

Double anonymous peer review. All submissions are sent to at least 2 independent and anonymous reviewers.

Open access options

This is an open access journal. If you choose to publish in this journal, you will be required to pay an article processing charge (APC). Read more about our open access options.

ORCiD

All authors, including co‑authors, must have a valid ORCiD. Visit the ORCiD website to register.

Accepted languages

This journal only accepts manuscripts written in UK or US English.

Accepted submission files

Article files should be provided in Microsoft Word format.

View author guidelines

Calls for papers

Closes: 25th February 2027

This special issue explores practical applications of machine learning in geotechnical engineering, focusing on real-world implementation, decision support, and sustainable, resilient infrastructure solutions.

Guest editor(s): Dr Meghdad Bagheri