The Application of Machine Learning in Geotechnical Engineering, 2nd Edition

Dear Colleagues,

Natural geological bodies are the objects of geotechnical engineering; their mechanical properties and internal structure are very complex. Most of the geotechnical engineering problems involve the coupling of multiple fields and multiple phases. Unsafe geotechnical engineering can result in serious engineering disasters, such as landslide and surface subsidence, etc., which cannot be solved well using traditional methods (e.g., theoretical methods, numerical methods and experimental methods). The development of artificial intelligence has supported better solutions to geotechnical engineering problems, and machine learning methods have been applied widely, currently representing a hot research topic. The present Special Issue intends to present new applications of machine learning methods in the field of geotechnical engineering, from planning and design to construction. The topics of interest include, but are not limited to, the applications of machine learning methods for slope engineering, underground engineering, and foundation engineering, the applications of machine learning methods in geomechanics, etc.

This Special Issue will publish high-quality original research papers on topics including, but not limited to, the following:

• Applications of artificial neural networks;
• Applications of deep learning methods;
• Applications of swarm intelligence;
• Applications of evolutionary algorithms;
• Applications of big data analysis;
• Applications of biological computation;
• Applications of nature-inspired computation;
• Applications of support vector machine, support vector regression, etc.;
• Intelligent forecasting of geotechnical engineering disasters.

Prof. Dr. Wei Gao
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• artificial neural networks
• deep learning
• big data
• swarm intelligence
• evolutionary algorithms
• geotechnical engineering
• slope engineering
• underground engineering
• foundation engineering
• geomechanics

• The Application of Machine Learning in Geotechnical Engineering in Applied Sciences (19 articles)