Comparative Study between Two Performance-Based Liquefaction Triggering Models for the Standard Penetration Test

Franke, Kevin W.; Wright, Alexander D.; Ekstrom, Levi T.

doi:10.1061/(asce)gt.1943-5606.0001094

Cited by 9 publications

(2 citation statements)

References 26 publications

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“…Of course, SOP models have several advantages in that they: (1) to date have inherently transparent structures; (2) are generally evolutionary, being relatively incremental improvements on previously trusted methods; and (3) have been compared and tested against one another in subsequent studies (e.g. Franke et al, 2014; Geyin et al, 2020; Idriss and Boulanger, 2012). None of these mitigating factors presently apply to AI models, making the lack of comparative tests especially problematic.…”

Section: Recurrent Shortcomings (Why Existing Ai Liquefaction Models ...mentioning

confidence: 99%

Why “AI” models for predicting soil liquefaction have been ignored, plus some that shouldn’t be

Maurer

Sanger

2023

Earthquake Spectra

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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 article is to investigate “why?” Through a sample review of 75 publications, we identify several good reasons. Namely, these models frequently (1) are not compared to state-of-practice models, making it unclear why they should be adopted; (2) depart from best practices in model development; (3) use AI in ways that may not be useful; (4) are presented in ways that overstate their complexity and make them unapproachable; and (5) 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. Toward 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.

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Section: Recurrent Shortcomings (Why Existing Ai Liquefaction Models ...mentioning

confidence: 99%

Why “AI” models for predicting soil liquefaction have been ignored, plus some that shouldn’t be

Maurer

Sanger

2023

Earthquake Spectra

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“…The inconsistencies in the AASHTO liquefaction procedure that will subsequently be demonstrated are not inherent to any single liquefaction triggering model but occur because of the manner in which the liquefaction triggering model is used in the pseudoprobabilistic approach. Furthermore, the performance-based procedures presented in this paper are not limited to any single probabilistic liquefaction triggering model and can incorporate alternative probabilistic triggering models if so desired (10).…”

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confidence: 99%

Simplified Uniform Hazard Liquefaction Analysis for Bridges

Franke

Mayfield

Wright

2014

Transportation Research Record

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Many bridge designers currently rely on the design specifications provided by AASHTO to evaluate liquefaction hazards for foundations and embankments. These guide specifications recommend a pseudo-probabilistic approach (i.e., an approach in which probabilistic estimates of seismic loading are incorporated into a deterministic liquefaction hazard analysis) for most conventional projects. A more consistent approach for the assessment of liquefaction hazards would involve a performance-based or uniform hazard evaluation of liquefaction potential. However, a performance-based liquefaction analysis is complex and difficult to implement in practice. This paper describes a simplified procedure for uniform hazard liquefaction analysis that uses the standard penetration test to closely approximate the results of a performance-based liquefaction analysis at a desired uniform hazard level. The simplified procedure is compatible with current AASHTO load and resistance factor bridge design specifications and is intended for use on bridges and other transportation-related structures. The procedure is summarized and shown to produce more consistent results for liquefaction hazards across different seismic environments than the existing AASHTO pseudo-probabilistic procedure. An example application of the simplified procedure is presented and discussed, and limitations for the procedure's proper use are provided.

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Performance-Based Estimation of Lateral Spread Displacement in the State of California: A Case Study for the Implementation of Performance-Based Design in Geotechnical Practice

Franke

Fullmer

Lasson

et al. 2022

Proceedings of the 4th International Conference on Performance Based Design in Earthquake Geotechnical Engineering (Beijing 202

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Comparative Study between Two Performance-Based Liquefaction Triggering Models for the Standard Penetration Test

Cited by 9 publications

References 26 publications

Why “AI” models for predicting soil liquefaction have been ignored, plus some that shouldn’t be

Why “AI” models for predicting soil liquefaction have been ignored, plus some that shouldn’t be

Simplified Uniform Hazard Liquefaction Analysis for Bridges

Performance-Based Estimation of Lateral Spread Displacement in the State of California: A Case Study for the Implementation of Performance-Based Design in Geotechnical Practice

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