Liquefaction is a devastating consequence of earthquakes that occurs in loose, saturated soil deposits, resulting in catastrophic ground failure. Accurate prediction of such geotechnical parameter is crucial for mitigating hazards, assessing risks, and advancing geotechnical engineering. This study introduces a novel predictive model that combines Extreme Learning Machine (ELM) with Dingo Optimization Algorithm (DOA) to estimate strain energy-based liquefaction resistance. The hybrid model (ELM-DOA) is compared with the classical ELM, Adaptive Neuro-Fuzzy Inference System with Fuzzy C-Means (ANFIS-FCM model), and Sub-clustering (ANFIS-Sub model). Also, two data pre-processing scenarios are employed, namely traditional linear and non-linear normalization. The results demonstrate that non-linear normalization significantly enhances the prediction performance of all models by approximately 25% compared to linear normalization. Furthermore, the ELM-DOA model achieves the most accurate predictions, exhibiting the lowest root mean square error (484.286 J/m³), mean absolute percentage error (24.900%), mean absolute error (404.416 J/m³), and the highest correlation of determination (0.935). Additionally, a Graphical User Interface (GUI) has been developed, specifically tailored for the ELM-DOA model, to assist engineers and researchers in maximizing the utilization of this predictive model. The GUI provides a user-friendly platform for easy input of data and accessing the model's predictions, enhancing its practical applicability. Overall, the results strongly support the proposed hybrid model with GUI serving as an effective tool for assessing soil liquefaction resistance in geotechnical engineering, aiding in predicting and mitigating liquefaction hazards.

液化是地震造成的破坏性后果，发生在松散的饱和土壤沉积物中，导致灾难性的地面破坏。此类岩土参数的准确预测对于减轻灾害、评估风险和推进岩土工程至关重要。本研究引入了一种新颖的预测模型，该模型将极限学习机 (ELM) 与 Dingo 优化算法 (DOA) 相结合，以估计基于应变能的液化阻力。将混合模型 (ELM-DOA) 与经典 ELM、具有模糊 C 均值的自适应神经模糊推理系统（ANFIS-FCM 模型）和子聚类（ANFIS-Sub 模型）进行比较。此外，还采用了两种数据预处理方案，即传统的线性归一化和非线性归一化。结果表明，与线性归一化相比，非线性归一化使所有模型的预测性能显着提高了约 25%。此外，ELM-DOA 模型实现了最准确的预测，表现出最低的均方根误差 (484.286 J/m ³ )、平均绝对百分比误差 (24.900%)、平均绝对误差 (404.416 J/m ³ ) 和测定相关性最高 (0.935)。此外，还开发了专为 ELM-DOA 模型定制的图形用户界面 (GUI)，以帮助工程师和研究人员最大限度地利用该预测模型。 GUI 提供了一个用户友好的平台，可以轻松输入数据和访问模型的预测，从而增强其实际适用性。总体而言，结果强烈支持所提出的混合模型，其中 GUI 作为评估岩土工程中土壤抗液化性的有效工具，有助于预测和减轻液化危害。