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Data-driven model for seismic assessment, design, and retrofit of structures using explainable artificial intelligence
Computer-Aided Civil and Infrastructure Engineering ( IF 8.5 ) Pub Date : 2024-09-17 , DOI: 10.1111/mice.13338
Khurram Shabbir, Mohamed Noureldin, Sung-Han Sim

Retrofitting building designs is crucial given the global aging infrastructure and increased in frequency of natural hazards like earthquakes. While traditional data-driven models are widely used for predicting building conditions, there has been limited exploration of recent artificial intelligence (AI) techniques in structural design. This study introduces a novel explainable AI framework that utilizes data-driven models for assessing, designing, and retrofitting of structures. The framework highlights the key global features of the model and further investigates them locally to adjust the input design parameters. It suggests the necessary changes in these inputs to achieve the desired structural performance. To achieve this, the framework employs interpretability techniques such as feature importance, feature interactions, Shapley Additive exPlanations, local interpretable model-agnostic explanations, partial dependence plot (PDP), and individual conditional expectation to highlight the important features. Additionally, a novel counterfactual) technique is applied for the first time as a design tool in seismic assessment and retrofitting of structures. The effectiveness of this framework is validated on a real benchmark structure through nonlinear time history analysis and natural earthquakes. The results show that the proposed framework is highly effective, especially under design-level earthquake conditions in achieving the necessary change in stiffness and strength of structures to meet the required seismic design objectives across different earthquake scenarios. This framework holds promise for wider adoption and applications in various other structural and civil engineering domains.

中文翻译:


使用可解释的人工智能对结构进行地震评估、设计和改造的数据驱动模型



鉴于全球基础设施老化和地震等自然灾害频率的增加,改造建筑设计至关重要。虽然传统的数据驱动模型广泛用于预测建筑状况,但最近人工智能 (AI) 技术在结构设计中的探索有限。本研究引入了一种新颖的可解释 AI 框架,该框架利用数据驱动的模型来评估、设计和改造结构。该框架突出显示了模型的关键全局特征,并进一步在本地研究它们以调整输入设计参数。它建议对这些输入进行必要的更改,以实现所需的结构性能。为了实现这一目标,该框架采用了可解释性技术,例如特征重要性、特征交互、Shapley 加法解释、局部可解释模型不可知解释解释、部分依赖图 (PDP) 和个体条件期望来突出重要特征。此外,一种新颖的反事实)技术首次作为设计工具应用于结构的地震评估和改造。通过非线性时程分析和自然地震,在真实的基准结构上验证了该框架的有效性。结果表明,所提出的框架非常有效,尤其是在设计级地震条件下,可以实现结构刚度和强度的必要变化,以满足不同地震情景下所需的抗震设计目标。该框架有望在其他各种结构和土木工程领域得到更广泛的采用和应用。
更新日期:2024-09-17
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