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首页> 外文期刊>International journal of geomechanics >Load-Settlement Modeling of Axially Loaded Drilled Shafts Using CPT-Based Recurrent Neural Networks
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Load-Settlement Modeling of Axially Loaded Drilled Shafts Using CPT-Based Recurrent Neural Networks

机译：基于CPT的递归神经网络的轴向载荷轴的载荷沉降建模。

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The design of pile foundations requires good estimation of the pile load-carrying capacity and settlement. Design for bearing capacity and design for settlement have been traditionally carried out separately. However, soil resistance and settlement are influenced by each other, and the design of pile foundations should thus consider the bearing capacity and settlement inseparably. This requires the full load-settlement response of piles to be well predicted. However, it is well known that the actual load-settlement response of pile foundations can be obtained only by load tests carried out in situ, which are expensive and time-consuming. In this paper, recurrent neural networks (RNNs) were used to develop a prediction model that can resemble the full load-settlement response of drilled shafts (bored piles) subjected to axial loading. The developed RNN model was calibrated and validated using several in situ full-scale pile load tests, as well as cone penetration test (CPT) data. The results indicate that the developed RNN model has the ability to reliably predict the load-settlement response of axially loaded drilled shafts and can thus be used by geotechnical engineers for routine design practice.
机译：桩基的设计需要对桩的承载能力和沉降进行良好的估算。传统上，承载力设计和沉降设计是分开进行的。但是，土的抗力和沉降是相互影响的，因此桩基的设计应分开考虑承载力和沉降。这就要求对桩的全部载荷-沉降响应进行良好的预测。但是，众所周知，桩基础的实际载荷沉降响应只能通过现场进行的载荷试验来获得，这既昂贵又费时。在本文中，使用递归神经网络（RNN）建立了一个预测模型，该模型可以类似于钻探轴（钻孔桩）在轴向载荷下的满负荷沉降响应。使用几个原位满量程桩载荷测试以及圆锥渗透测试（CPT）数据对开发的RNN模型进行校准和验证。结果表明，所开发的RNN模型具有可靠地预测轴向加载钻探轴的载荷—沉降响应的能力，因此可以被岩土工程师用于常规设计实践。

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