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A new macro-element model encapsulating the dynamic moment-rotation behaviour of raft foundations

机译:封装筏形基础动力弯矩-旋转行为的新型宏单元模型

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摘要

The interaction of shallow foundations with the underlying soil during dynamic loading can have both positive and negative effects on the behaviour of the superstructure. Although the negative impacts are generally considered within design codes, seldom is design performed in such a way as to maximise the potential beneficial characteristics. This is, in part, due to the complexity of modelling the soil–structure interaction. Using the data from dynamic centrifuge testing of raft foundations on dry sand, a simple moment–rotation macro-element model has been developed, which has been calibrated and validated against the experimental data. For the prototype tested, the model is capable of accurately predicting the underlying moment–rotation backbone shape and energy dissipation during cyclic loading. Utilising this model within a finite-element model of the structure could potentially allow a coupled analysis of the full soil–foundation–structure system's seismic response in a simplified manner compared to other methods proposed in literature. This permits the beneficial soil–structure interaction characteristics, such as the dissipation of seismic energy, to be reliably included in the design process, resulting in more efficient, cost-effective and safe designs. In this paper the derivation of the model is presented, including details of the calibration process. In addition, an appraisal of the likely resultant error of the model prediction is presented and visual examples of how well the model mimics the experimental data are provided.
机译:在动态荷载作用下浅层基础与下层土壤的相互作用对上部结构的行为既有正面影响也有负面影响。尽管通常在设计规范中考虑负面影响,但很少以最大化潜在有益特性的方式进行设计。这部分是由于对土壤-结构相互作用建模的复杂性。利用干砂上筏板基础的动态离心测试数据,开发了一个简单的力矩-旋转宏元模型,该模型已根据实验数据进行了校准和验证。对于测试的原型,该模型能够准确预测潜在的力矩-旋转主干的形状以及在周期性载荷下的能量耗散。与文献中提出的其他方法相比,在结构的有限元模型中使用该模型可能会允许以简化的方式对整个土-基础-结构系统的地震响应进行耦合分析。这样可以在设计过程中可靠地包括有益的土壤与结构相互作用特性,例如消散地震能量,从而实现更有效,更具成本效益和更安全的设计。本文介绍了模型的推导,包括校准过程的详细信息。另外,对模型预测的可能结果误差进行了评估,并提供了模型模拟实验数据的可视化示例。

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