How can Artificial Intelligence be used in Geotechnical Engineering?
shrinkage limit is the maximum water content at which if we reduce water content further than soil volume doesn't change, we can also say that at shrinkage limit, water is in just saturated stage. let's see the graph of water content and volume change with shrinkage limit ws= shrinkage limit wp= plaRead more
shrinkage limit is the maximum water content at which if we reduce water content further than soil volume doesn’t change,
we can also say that at shrinkage limit, water is in just saturated stage.
let’s see the graph of water content and volume change with shrinkage limit

ws= shrinkage limit
wp= plastic limit
wl=liquid limit
at below shrinkage water content water is spill out/remove from voids of soil and that voids fill with the air. Hence voids doesn’t change, so volume doesn’t change. and soil become 3 phase structure air, water and solid particles.
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AdityaBhandakkar
Hi, Geotechnical engineering deals with materials (e.g., soil and rock) that, by their very nature, exhibit varied and behavior due to the physical processes associated with the formation of these materials. Modeling such materials' behavior is complicated and usually beyond the ability of most tradRead more
Hi,
Geotechnical engineering deals with materials (e.g., soil and rock) that, by their very nature, exhibit varied and behavior due to the physical processes associated with the formation of these materials. Modeling such materials’ behavior is complicated and usually beyond the ability of most traditional forms of physically-based engineering methods. Artificial intelligence (AI) is becoming more popular and particularly amenable to modeling most geotechnical engineering materials’ complex behavior because it has demonstrated superior predictive ability compared to traditional methods. Over the last decade, AI has been applied successfully to virtually every problem in geotechnical engineering. However, despite this success, AI techniques are still facing classical opposition due to some inherent reasons such as lack of transparency, knowledge extraction, and model uncertainty, which will discuss in detail in this chapter. Among the available AI, techniques are artificial neural networks (ANNs), genetic programming (GP), evolutionary polynomial regression (EPR), support vector machines, M5 model trees, and K-nearest neighbors (Elshorbagy et al.,2010). This chapter will focus on three AI techniques, including ANNs, GP, and EPR.
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