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Study On Dynamic Compaction Parameter Prediction System In Loess Sites

Posted on:2011-03-12Degree:MasterType:Thesis
Country:ChinaCandidate:B LiFull Text:PDF
GTID:2132360305964773Subject:Geotechnical engineering
Abstract/Summary:PDF Full Text Request
In order to eliminate the collapsible of loess' adverse effects, many scholars and engineering staff have made a large number of exploration and practice at home and abroad. Many foundation treatment methods which can eliminate loess collapsibility have been invented, Dynamic Compaction is one of them. It is remarkable for its effectiveness, economic and easy convenient construction, shorter construction period and other advantages, and it has been in a very wide range of application and promotion. Among the design parameters of Dynamic Compaction, the two parameters effective reinforcement depth and compaction subsidence of ground play an important role in Dynamic Compaction' design and construction. Therefore, studying these two important parameters has a very important practical significance. In this paper, Dynamic Compaction Consolidation of the foundation principles has been systems analyzed. Based on two different macro and micro perspective, Dynamic Compaction mechanism has been discussed and described. As the complexity of the reinforcement mechanism and some uncertainty exists, a variety of effective reinforcement depth and compaction subsidence of ground of the specific amount of calculation is not very appropriate in the past,. This article summarizes the analysis of the loess region Dynamic Compaction works, analysis and obtained effective depth of Dynamic Compaction affect the amount of the main factors:tamping energy per unit area, the number of tamping blow, the water content and void ratio. A way to use BP artificial neural network method to model was put forward on this basis. The principle was described and the specific implementation steps has been gave; Simulation experiment was made in the MATLAB platform, validity and practicality of this method is good; finally, designed and established a complete set of Dynamic Compaction parameter prediction system software, Further enhancing the BP prediction method in application operability and provided a reference for the engineering practice.
Keywords/Search Tags:dynamic compaction, collapsible loess, damp subsidence, BP neural network, effective consolidation depth
PDF Full Text Request
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