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The application effects of various machine learning models in predicting deep foundation pit supporting structure deformation surrounding surface settlement and assessing foundation pit

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Deep foundation pits involving complex soil water structure interactions are often at a high risk of failure under heavy rainfall Predicted deformation is an important index for early

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LSTM network is used to process the deformation data of deep foundation pit and random gradient descent momentum Nesterov RMSProp AdaGmd and Adam algorithms are selected in the same

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To synchronously predict horizontal displacement of enclosure structure pit bottom uplift and surface subsidence of foundation pits a multidimensio

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This paper aimed to analyze the characteristics of excavation deformation of a deep foundation pit with internal support in the Dalian Donggang Business District by combining the

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This paper establishes a finite element simulation model based on engineering practice the Grey Wolf optimization algorithm optimizes the extreme learning machine neural network and the GWO ELM

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The fundamental principles of machine learning models including neural networks support vector machines and Bayesian networks are elaborated in the context of their application to deep

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With the increasing urbanization in China monitoring and predicting the deformation of deep excavations has become increasingly critical

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However the deformation of a foundation pit is affected by many factors such as geological conditions the surrounding environment the support scheme and the excavation form so it is worth exploring