Optimized functional linked neural network for predicting diaphragm wall deflection induced by braced excavations in clays

作     者：Chengyu Xie Hoang Nguyen Yosoon Choi Danial Jahed Armaghani Chengyu Xie;Hoang Nguyen;Yosoon Choi;Danial Jahed Armaghani

作者机构：School of Environment and ResourcesXiang tan UniversityXiangtan 411105China Department of Surface MiningMining FacultyHanoi University of Mining and GeologyDuc ThangBac Tu LiemHanoiViet Nam Innovations for Sustainable and Responsible Mining(ISRM)GroupHanoi University of Mining and GeologyDuc ThangBac Tu LiemHanoiViet Nam Department of Energy Resources EngineeringPukyong National UniversityBusan 48513Republic of Korea Department of Urban PlanningEngineering Networks and SystemsInstitute of Architecture and ConstructionSouth Ural State University76Lenin ProspectChelyabinsk 454080Russia 

出 版 物：《Geoscience Frontiers》 (地学前缘（英文版）)

年 卷 期：2022年第13卷第2期

页      面：34-51页

核心收录：

学科分类：081401[工学-岩土工程] 08[工学] 0708[理学-地球物理学] 0814[工学-土木工程] 

基　　金：financially supported by the Natural Science Foundation of Hunan Province(2021JJ30679) 

主　　题：Diaphragm wall deflection Braced excavation Finite element analysis Clays Meta-heuristic algorithms Functional linked neural network 

摘      要：Deep excavation during the construction of underground systems can cause movement on the ground,especially in soft clay *** high levels,excessive ground movements can lead to severe damage to adjacent *** this study,finite element analyses(FEM)and the hardening small strain(HSS)model were performed to investigate the deflection of the diaphragm wall in the soft clay layer induced by braced *** geometric and mechanical properties of the wall were investigated to study the deflection behavior of the wall in soft ***,1090 hypothetical cases were surveyed and simulated based on the HSS model and FEM to evaluate the wall deflection *** results were then used to develop an intelligent model for predicting wall deflection using the functional linked neural network(FLNN)with different functional expansions and activation *** the FLNN is a novel approach to predict wall deflection;however,in order to improve the accuracy of the FLNN model in predicting wall deflection,three swarm-based optimization algorithms,such as artificial bee colony(ABC),Harris’s hawk’s optimization(HHO),and hunger games search(HGS),were hybridized to the FLNN model to generate three novel intelligent models,namely ABC-FLNN,HHO-FLNN,*** results of the hybrid models were then compared with the basic FLNN and MLP *** revealed that FLNN is a good solution for predicting wall deflection,and the application of different functional expansions and activation functions has a significant effect on the outcome predictions of the wall *** is remarkably interesting that the performance of the FLNN model was better than the MLP model with a mean absolute error(MAE)of 19.971,root-mean-squared error(RMSE)of 24.574,and determination coefficient(R^(2))of ***,the performance of the MLP model only obtained an MAE of 20.321,RMSE of 27.091,and R^(2)of ***,the results also indicated that the proposed