Why "AI" Models for Predicting Soil Liquefaction have been Ignored, Plus Some that Shouldn't Be
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915493774884864 |
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| author | Maurer, Brett W. Sanger, Morgan D. |
| author_facet | Maurer, Brett W. Sanger, Morgan D. |
| contents | Soil liquefaction remains an important and interesting problem that has attracted the development of enumerable prediction models. Increasingly, these models are utilizing algorithmic learning, or "artificial intelligence" (AI). The rapid growth of AI in the liquefaction literature is unsurprising, given its ease of implementation and potential advantages over traditional statistical methods. However, AI liquefaction models have been widely ignored by practitioners and researchers alike; the objective of this paper is to investigate "why?" Through a sample review of 75 publications, we identify several good reasons. Namely, these models frequently: (i) are not compared to state-of-practice models, making it unclear why they should be adopted; (ii) depart from best practices in model development; (iii) use AI in ways that may not be useful; (iv) are presented in ways that overstate their complexity and make them unapproachable; and (v) are discussed but not actually provided, meaning that no one can use the models even if they wanted to. These prevailing problems must be understood, identified, and remedied, but this does not mean that AI itself is problematic, or that all prior efforts have been without merit or utility. Instead, understanding these recurrent shortcomings can help improve the direction and perceptions of this growing body of work. Towards this end, we highlight papers that are generally free from these shortcomings, and which demonstrate applications where AI is more likely to provide value in the near term: permitting new modeling approaches and potentially improving predictions of liquefaction phenomena. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_10966 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Why "AI" Models for Predicting Soil Liquefaction have been Ignored, Plus Some that Shouldn't Be Maurer, Brett W. Sanger, Morgan D. Computational Engineering, Finance, and Science Soil liquefaction remains an important and interesting problem that has attracted the development of enumerable prediction models. Increasingly, these models are utilizing algorithmic learning, or "artificial intelligence" (AI). The rapid growth of AI in the liquefaction literature is unsurprising, given its ease of implementation and potential advantages over traditional statistical methods. However, AI liquefaction models have been widely ignored by practitioners and researchers alike; the objective of this paper is to investigate "why?" Through a sample review of 75 publications, we identify several good reasons. Namely, these models frequently: (i) are not compared to state-of-practice models, making it unclear why they should be adopted; (ii) depart from best practices in model development; (iii) use AI in ways that may not be useful; (iv) are presented in ways that overstate their complexity and make them unapproachable; and (v) are discussed but not actually provided, meaning that no one can use the models even if they wanted to. These prevailing problems must be understood, identified, and remedied, but this does not mean that AI itself is problematic, or that all prior efforts have been without merit or utility. Instead, understanding these recurrent shortcomings can help improve the direction and perceptions of this growing body of work. Towards this end, we highlight papers that are generally free from these shortcomings, and which demonstrate applications where AI is more likely to provide value in the near term: permitting new modeling approaches and potentially improving predictions of liquefaction phenomena. |
| title | Why "AI" Models for Predicting Soil Liquefaction have been Ignored, Plus Some that Shouldn't Be |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2509.10966 |