Why geotechnical engineering isn’t ready yet for machine learning?
Why geotechnical engineering isn’t ready yet for machine learning?
Data science technologies are universally known, but only some people understand the foundations and how to implement them operationally on real projects. DATA science is the common ground between business knowledge (in our case geotechnical engineering), computer science, and advanced mathematics. The example of geotechnics is a typical case, as soil data is a significant part of construction projects. So why is it not common to use those algorithms in geotechnical engineering in 2023? This article describes the current state of the art in which geotechnical engineers, designers, and contractors manage the transfer of geotechnical data in Poland, France, and the Anglo-Saxon world. The authors of this article believe that the solution for implementing data science in geotechnical engineering is first to address the issue of data transfer. This involves providing a specially designed software that will allow companies, at first, to build their own geotechnical database. It is only at this point that one can begin to think about going further, by processing the data and using geostatistical and artificial intelligence techniques.
Tomasz Daktera; L. Janodet
18th European Conference on Soil Mechanics and Geotechnical Engineering (ECSMGE2024)
C - Risk analysis and safety evaluation
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