In addition to confined investigations on tall geosynthetic reinforced soil(GRS)walls,a remarkable database of such walls must be analyzed to diminish engineers’concerns regarding the American Association of State Hi...In addition to confined investigations on tall geosynthetic reinforced soil(GRS)walls,a remarkable database of such walls must be analyzed to diminish engineers’concerns regarding the American Association of State Highway and Transportation Officials(AASHTO)Simplified or Simplified Stiffness Method in projects.There are also uncertainties regarding reinforcement load distributions of GRS walls at the connections.Hence,the current study has implemented a combination of finite element method(FEM)and artificial neural network(ANN)to distinguish the performance of short and tall GRS walls and assess the AASHTO design methods based on 88 FEM and 10000 ANN models.There were conspicuous differences between the effectiveness of stiffness(63%),vertical spacing(22%),and length of reinforcements(14%)in the behavior of short and tall walls,along with predictions of geogrid load distributions.These differences illustrated that using the Simplified Method may exert profound repercussions because it does not consider wall height.Furthermore,the Simplified Stiffness Method(which incorporates wall height)predicted the reinforcement load distributions at backfill and connections well.Moreover,a Multilayer Perceptron(MLP)algorithm with a low average overall relative error(up to 2.8%)was developed to propose upper and lower limits of reinforcement load distributions,either at backfill or connections,based on 990000 ANN predictions.展开更多
文摘In addition to confined investigations on tall geosynthetic reinforced soil(GRS)walls,a remarkable database of such walls must be analyzed to diminish engineers’concerns regarding the American Association of State Highway and Transportation Officials(AASHTO)Simplified or Simplified Stiffness Method in projects.There are also uncertainties regarding reinforcement load distributions of GRS walls at the connections.Hence,the current study has implemented a combination of finite element method(FEM)and artificial neural network(ANN)to distinguish the performance of short and tall GRS walls and assess the AASHTO design methods based on 88 FEM and 10000 ANN models.There were conspicuous differences between the effectiveness of stiffness(63%),vertical spacing(22%),and length of reinforcements(14%)in the behavior of short and tall walls,along with predictions of geogrid load distributions.These differences illustrated that using the Simplified Method may exert profound repercussions because it does not consider wall height.Furthermore,the Simplified Stiffness Method(which incorporates wall height)predicted the reinforcement load distributions at backfill and connections well.Moreover,a Multilayer Perceptron(MLP)algorithm with a low average overall relative error(up to 2.8%)was developed to propose upper and lower limits of reinforcement load distributions,either at backfill or connections,based on 990000 ANN predictions.
