Submit a Manuscript to the Journal
Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards

For a Special Issue on
Machine Learning and AI in Geotechnics

• The potential for machine learning (ML) and artificial intelligence to shape geotechnical engineering practice is immense.
• However, the agenda for machine learning in geotechnics should not be focus on applying or developing algorithms alone.
• The purpose of this special issue is to invite contributions that can engage the needs of practice and seek synergy between data-driven methods and current knowledge/accumulated experience under the "data-centric geotechnics" agenda.
• The data-centric geotechnics agenda focuses on using all real-world data (including all sources) firmly grounded on our existing knowledge base (not selective input data for a physical model or abstract data-driven analysis connected to geotechnics in a peripheral way) and deriving robust data-informed decisions at a specific site (not decisions for an ideal world or decisions of minor concern to geotechnical engineers).
• Its success is therefore measured by its value to practice and more specifically, value to routine practice (where site data is imperfect, with sparsity being the most well-known attribute).
• The value of ML research in data-centric geotechnics can be classified as: (1) Type 1 (incremental value) involving available data and existing conventional applications, (2) Type 2a (potentially high value) involving available data and new applications, (3) Type 2b (high value) involving new data and existing applications, and (4) Type 3 (disruptive value) involving new data and completely novel applications (e.g., precision construction).
• The intent of this special issue is to solicit contributions in Type 2 and Type 3 ML research for geotechnics.

• Select "Machine Learning and AI in Geotechnics” when submitting your paper to ScholarOne
• Expected publication date is Volume 17, Issue 1, 2023

Instructions for AuthorsSubmit an Article