The seismic events on February 6, 2023, in the province of Kahramanmaraş/Türkiye, caused severe damage and the collapse of numerous structures due to underlying soil issues. This catastrophe revealed the inevitable requirement to evaluate the effect of soil profile on structural safety. In the present study, novel artificial intelligence (AI) functions based on the three-dimensional finite element (3D FE) method considering various soil parameters were developed to predict the effects of earthquakes. A 3D FE model of the ten-story building with a known soil profile and structural elements was created in the first stage, accounting for the soil-pile-structure interaction. After model validation, numerous parametric time history earthquake analyses were performed using the February 6 Pazarcık/Kahramanmaraş (Mw = 7.7) earthquake records. Therefore, the effects of soil parameters on acceleration, settlement, and lateral deformations were investigated. An innovative coding infrastructure, leveraging the power of AI, was developed to generate optimal network solutions automatically for creating high-order regression prediction functions. The 3D FE data was integrated into the code, and subsequently, an artificial neural network was utilized to formulate a function that yielded statistically significant outcomes. The created function accurately predicted the accelerations, settlements, and deformations. A novel method for indicating the potential deformations and accelerations inflicted by earthquakes based on soil parameters was introduced. This methodology can serve as a practical guide for researchers and project implementers in the initial design phases.

中文翻译：

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使用 2 月 6 日卡赫拉曼马拉什地震记录开发基于 3D 有限元方法的新型人工智能功能，用于预测各种土壤的地震影响


2023 年 2 月 6 日发生在土耳其卡赫拉曼马拉什省的地震事件由于底层土壤问题造成了严重破坏和许多建筑物倒塌。这场灾难揭示了评估土壤剖面对结构安全影响的必然要求。在本研究中，开发了基于三维有限元（3D FE）方法并考虑各种土壤参数的新型人工智能（AI）功能来预测地震的影响。第一阶段创建了具有已知土壤剖面和结构元素的十层建筑的 3D 有限元模型，解释了土壤-桩-结构相互作用。模型验证后，使用 2 月 6 日 Pazarcık/Kahramanmaraş (Mw = 7.7) 地震记录进行了大量参数时程地震分析。因此，研究了土体参数对加速度、沉降和横向变形的影响。开发了一种创新的编码基础设施，利用人工智能的力量，自动生成最佳网络解决方案，以创建高阶回归预测函数。 3D FE 数据被集成到代码中，随后利用人工神经网络来制定函数，从而产生具有统计意义的结果。创建的函数准确地预测了加速度、沉降和变形。介绍了一种基于土壤参数来指示地震造成的潜在变形和加速度的新方法。该方法可以为研究人员和项目实施者在初始设计阶段提供实用指南。