Machine learning methods have advantages in predicting excavation-induced lateral wall *** to lack of sufficient field data,training data for prediction models were often derived from the results of numerical simulati...
详细信息
Machine learning methods have advantages in predicting excavation-induced lateral wall *** to lack of sufficient field data,training data for prediction models were often derived from the results of numerical simulations,leading to poor prediction *** on a specific quantity of data,a multivariate adaptive regression splines method(MARS)was introduced to predict lateral wall deflections caused by deep excavations in thick water-rich *** of lateral wall deflections to affecting factors was *** is disclosed that dewatering mode has the most significant influence on lateral wall deflections,while the soil cohesion has the least *** crossvalidation analysis,weights were introduced to modify the MARS method to optimize the prediction *** of the predicted and measured deflections shows that the prediction based on the modified multivariate adaptive regression splines method(MMARS)is more accurate than that based on the traditional MARS *** prediction model established in this paper can help engineers make predictions for wall displacement,and the proposed methodology can also serve as a reference for researchers to develop prediction models.
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