On the Underutilization of Artificial Intelligence Models in Geotechnical Practice

Many problems in geotechnical engineering are prime candidates for algorithmic learning, or “artificial intelligence” (AI), given the complex, interrelated, multivariate relationships needed to define the behaviors and responses of geo-materials. There has recently been a rapid increase in the use of AI in developing geotechnical prediction models. This is not surprising, considering the ease of implementing AI, its potential advantages over traditional statistical methods, and the immense attention it receives in the media and popular culture. However, despite a growing presence in the literature, AI models are still widely ignored by geotechnical practitioners. This paper aims to investigate the reasons behind this underutilization through the representative lens of one important topic in the geotechnical profession: soil liquefaction. The authors conducted a literature review of 75 AI liquefaction prediction models to identify why these and other AI models are not being adopted. This review reveals several reasons, including the lack of comparison to existing, familiar models, departure from established best practices, questionable uses of AI, overly complex presentation styles, and failure to provide models that can actually be used. These recurrent shortcomings must be addressed to enable the effective use, and realize the full potential, of AI in the geotechnical engineering profession. It is important to note that these shortcomings do not indicate that AI itself is inherently problematic or that all previous efforts have been in vain. The lessons learned from this review are relevant to all areas of geotechnical engineering and could help to improve the future direction and perceptions of AI in the profession.