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Predicting and validating the load-settlement behavior of large-scale geosynthetic-reinforced soil abutments using hybrid intelligent modeling 被引量:1
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作者 Muhammad Nouman Amjad Raja Syed Taseer Abbas Jaffar +1 位作者 Abidhan Bardhan Sanjay Kumar Shukla 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2023年第3期773-788,共16页
Settlement prediction of geosynthetic-reinforced soil(GRS)abutments under service loading conditions is an arduous and challenging task for practicing geotechnical/civil engineers.Hence,in this paper,a novel hybrid ar... Settlement prediction of geosynthetic-reinforced soil(GRS)abutments under service loading conditions is an arduous and challenging task for practicing geotechnical/civil engineers.Hence,in this paper,a novel hybrid artificial intelligence(AI)-based model was developed by the combination of artificial neural network(ANN)and Harris hawks’optimisation(HHO),that is,ANN-HHO,to predict the settlement of the GRS abutments.Five other robust intelligent models such as support vector regression(SVR),Gaussian process regression(GPR),relevance vector machine(RVM),sequential minimal optimisation regression(SMOR),and least-median square regression(LMSR)were constructed and compared to the ANN-HHO model.The predictive strength,relalibility and robustness of the model were evaluated based on rigorous statistical testing,ranking criteria,multi-criteria approach,uncertainity analysis and sensitivity analysis(SA).Moreover,the predictive veracity of the model was also substantiated against several large-scale independent experimental studies on GRS abutments reported in the scientific literature.The acquired findings demonstrated that the ANN-HHO model predicted the settlement of GRS abutments with reasonable accuracy and yielded superior performance in comparison to counterpart models.Therefore,it becomes one of predictive tools employed by geotechnical/civil engineers in preliminary decision-making when investigating the in-service performance of GRS abutments.Finally,the model has been converted into a simple mathematical formulation for easy hand calculations,and it is proved cost-effective and less time-consuming in comparison to experimental tests and numerical simulations. 展开更多
关键词 Geosynthetic-reinforced soil(GRS) ABUTMENTS settlement estimation Predictive modeling Artificial intelligence(AI) Artificial neural network(ANN)-Harris hawks’optimisation(HHO)
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Study on Ground Deformation during Shield Tunnel Construction
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作者 Zhongkun Zhang 《Journal of Construction Research》 2021年第2期19-26,共8页
Through the systematic analysis of the ground settlement generated by the process of shield tunneling,the relationships between ground deformation and construction parameters are studied in this paper.Based on the ass... Through the systematic analysis of the ground settlement generated by the process of shield tunneling,the relationships between ground deformation and construction parameters are studied in this paper.Based on the assumption of linear small deformation,a mathematical model of the relationship between ground deformation and construction parameters is set up.The principle and method of optimization for estimating ground deformation is studied.The actual measured data are compared with the results of theoretical analysis in a case.Considering different ground formations in different construction sites with different adverse effects on surface and underground structures,the ground surface deformations caused by shield tunneling is an aimed topic in this paper.The contributions and research implications are the revealed relationships between the ground deformation and the shield tunneling parameters during construction. 展开更多
关键词 Shield tunnel Ground settlement Construction parameters Mathematical model Maximum estimation Shield tunnel Ground settlement Construction parameters Mathematical model Maximum estimation
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