Study On Modeling And Prediction Of The Ultimate Bearing Capacity Based On The Generalized Gray Model

The prediction of the ultimate bearing capacity is an important program in the geotechnical engineering.There are many ways to get the ultimate bearing capacity now.Out of them the predicted method is a hot spot because of its simpleness, practicability and economy.It refers to use the data from the no-destroy static load test to model and predict the ultimate bearing capacity.The ultimate bearing capacity is a system interfered by many factors.The factors' great characteristics are data's polytropy and imperfection,parameter's uncertainty.The gray system theory's research objective is the system with incomplete information.So it is reasonable to study the ultimate bearing capacity.In this paper we only study the single pile's and bolt's ultimate bearing capacity.At present only the GM(1,1) is used to predict the ultimate bearing capacity.As a classical model in the gray system,the GM(1,1) has some disadvantages itself.For example,the model is only fit for the smooth and equidistant sequence.In practical engineering,because of many factors' interfere,the sequences are complicated and polytropic.So a great task is to improve and put forward some new models to satisfy the sequences.This paper,practically,improved and put forward some new models based on the generalized accumulated generating operation.They are non-equidistant GM(1,1),non-equidistant and equidistant GM(1,1) with jump point and non-equidistant and equidistant GM(1,1) with multi-stage.We analyze the models' accumulated generating methods,properties and parameter space.GM(1,1) power model is studied further.We analyze the model's parameter space,curve's shape and properties,solufion's form and method.Gray optimization model is an important part of the gray system theory.In this paper we study the gray multi-objective linear programming(GMLP) and gray bi-level linear programming(GBLP).Some concepts are put forward.The properties and solution are studied.Gray model's great characteristic is simple and applied.And the particle swarm optimization(PSO) also has the advantages such as comparative simplicity,easy operation,and has been used in many fields.So we use PSO to solve the gray model's parameters.As one computation techniques,PSO also has the disadvantage of premature convergence.So we improve PSO and put forward Multi-Swarm PSO (MSPSO),Multi-Best PSO(MBPSO) and Multi-Swarm and Multi-Best PSO (MSBPSO).The searching efficiency is improved greatly by information sharing between swarms and mutual competition between best values.In this paper,the gray models based on MSBPSO are used to predict the ultimate bearing capacity of single pile and bolt.For every example we use different models. The simulation results show that the new models' errors are smaller than GM(1,1)'s and the GM(1,1) power model is the best.It is because it has many forms of curve. So it can satisfy many forms of sequences' models.The examples show that the new models can solve the prediction of the ultimate bearing capacity preferably.We also use MSBPSO to solve the gray optimization models(GMLP,GBLP) about the bearing capacity and the effects are very good.In this paper the Visual Basic language is used to design the software system "Prediction of the Ultimate Bearing Capacity Based on the Generalized Gray Model". The system contains each model improved and put forward in this paper.At the same time the system operates easily and has the functions of prediction,simulation and analysis.It would play a contributive role in generalizing the theory studied in the paper.This dissertation was from Specialized Research Fund for the Doctoral Program of Higher Education of China:Study on Modeling and Prediction of the Ultimate Bearing Capacity Based on the Generalized Accumulated Generating Operation (N0.200804970005).