Liquefaction surface-manifestation is a popular proxy of damage potential for infrastructure. Models for predicting it are thus commonly used, and often codified, in earthquake engineering practice. One such model is that of Ishihara (1985) who proposed empirical “H₁–H₂” curves considering the influence of the non-liquefied crust on surface expression. Yet, while widely used and cited, these curves were trained on just ∼300 data points from two earthquakes. Accordingly, this study evaluates and updates the Ishihara (1985) model using 14,400 data points from 24 earthquakes, while also comparing against three other manifestation models from the literature. In addition to retraining the H₁–H₂ model via traditional regression, new variants are developed via machine- and deep-learning. Each of the new H₁–H₂ models outperforms the original in unbiased testing and is suitable for application. Ultimately, however, this paper also explores the limits of H₁–H₂ models and the apparent inefficiency and/or insufficiency of their predictor variables. In this regard, the models developed herein may perform better than any other, yet new models are still needed to account for factors influential in producing surface manifestation in a more explicit and mechanistic manner.

液化表面表现是基础设施潜在损害的流行代表。因此，用于预测它的模型在地震工程实践中被普遍使用，并且经常被编纂。一个这样的模型是 Ishihara（1985）的模型，他提出了经验“ H ₁ - H ₂ ”曲线，考虑了非液化地壳对地表表达的影响。然而，虽然被广泛使用和引用，但这些曲线仅在两次地震的约 300 个数据点上进行了训练。因此，本研究使用来自 24 次地震的 14,400 个数据点评估和更新 Ishihara (1985) 模型，同时还与文献中的其他三种表现模型进行比较。除了重新训练H ₁ – H ₂通过传统回归模型，通过机器和深度学习开发新的变体。每个新的H ₁ - H ₂模型在无偏测试中都优于原始模型，并且适合应用。然而，最终，本文还探讨了H ₁ - H ₂模型的局限性以及其预测变量的明显低效率和/或不足。在这方面，本文开发的模型可能比任何其他模型表现得更好，但仍需要新模型来以更明确和机械的方式解释影响产生表面表现的因素。