用自编码器提取钻探数据特征，提升地震液化侧向扩散预测准确率。
Investigating the effect of CPT in lateral spreading prediction using Explainable AI
- 用自编码器将200组钻探数据压缩为10个潜在特征
- 模型准确率达83%以上，优于传统指标或无数据情况
- 揭示1-3米深度土壤行为最关键，适合岩土工程AI研究者
本研究提出一种自编码器方法，从圆锥贯入试验（CPT）剖面中提取潜在特征，评估将CPT数据引入人工智能模型的潜力。我们利用自编码器将200组土体类型指数（Ic）和归一化锥尖阻力（qc1Ncs）的CPT剖面压缩为10个潜在特征，同时保留关键信息。随后，结合场地参数使用XGBoost模型预测2011年克赖斯特彻奇地震中的侧向扩散发生情况。使用潜在CPT特征的模型性能优于采用传统CPT指标或无CPT数据的模型，准确率超过83%。可解释人工智能分析显示，最关键的潜在特征对应于1至3米深度范围内的土壤行为，凸显该深度区间对液化评估的重要性。自编码器方法为机器学习液化模型提供了一种自动化、高效的数据压缩与特征提取技术。
原文摘要 · Abstract (English)
This study proposes an autoencoder approach to extract latent features from cone penetration test profiles to evaluate the potential of incorporating CPT data in an AI model. We employ autoencoders to compress 200 CPT profiles of soil behavior type index (Ic) and normalized cone resistance (qc1Ncs) into ten latent features while preserving critical information. We then utilize the extracted latent features with site parameters to train XGBoost models for predicting lateral spreading occurrences in the 2011 Christchurch earthquake. Models using the latent CPT features outperformed models with conventional CPT metrics or no CPT data, achieving over 83% accuracy. Explainable AI revealed the most crucial latent feature corresponding to soil behavior between 1-3 meter depths, highlighting this depth range's criticality for liquefaction evaluation. The autoencoder approach provides an automated technique for condensing CPT profiles into informative latent features for machine-learning liquefaction models.
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