Perceived Maturity of AI and Machine Learning in Geotechnical Engineering: Insights from an International Survey Based on the Gartner Hype Cycle

Artificial intelligence (AI) and machine learning (ML) are rapidly attracting attention in geotechnical engineering. However, despite the growing volume of technical research, there remains limited understanding of how mature these technologies are perceived by the professional community and how ready they are for broader practical adoption. This study reports the results of an international questionnaire survey of 111 respondents, including researchers, practitioners, and public-sector engineers. Using an eight-stage scale based on the Gartner Hype Cycle, participants evaluated the perceived maturity of AI/ML in geotechnical engineering and provided qualitative comments on applications, challenges, and expectations. When the eight stages were grouped into three phases—Expectation Building (Stages 1–3), Bridging the Gap (Stages 4–6), and Practical Adoption (Stages 7–8)—most respondents placed current AI/ML developments in the Expectation Building phase, while a substantial proportion viewed the field as being in a transitional Bridging the Gap phase. Only a smaller fraction considered AI/ML to have reached practical adoption. This pattern was broadly consistent across affiliation groups. Thematic analysis of free-text responses identified recurrent concerns regarding data availability and quality, validation and uncertainty evaluation, interpretability and trustworthiness, and the need for clearer guidance and education. These findings indicate that the maturation of AI/ML in geotechnical engineering is shaped not only by technical progress, but also by practical, institutional, and human factors. By clarifying how AI/ML maturity is currently perceived across the community, this study provides a structured empirical basis for aligning future research, education, and implementation strategies toward responsible and sustainable adoption.