Optimization Design Method Of Deep Foundation Excavation And Peripheral Building Subsidence

With modern city construction, economy development and thinking about the safe of deep foundation excavation peripheral building are leading to a higher request for increasing deep foundation excavation ----optimum design and building subsidence forecast. So it is necessary to study further basic principle and method of optimum design for deep foundation excavation engineering and method of subsidence forecast.Deep foundation excavation is a complicated and systemic project, whose design faces the contradiction between reliability and economy, so optimization design of Deep foundation excavation was proposed. Because traditional optimization design method can't meet the demands of practice, intelligent optimization design method of deep foundation excavation was introduced. Based on fore passed research, the optimization selection of deep foundation excavation scheme has been deeply studied, and the application of intelligent optimization design method in deep foundation pit has been discussed and the common types of deep foundation pit are summarized, with a brief description of its character and applicability. The principles and basis of deep foundation pit supporting type optimization selection are analyzed. Based on the analysis of disadvantages of its traditional optimum method, a case-Based Reasoning method of Euclidean distance based is present.The thesis constructs an index system and hierarchical structure graph of deep foundation excavation through the application of system engineering and establishes multi-objectives blur optimum decision-making model and its application in the actual engineering has been proved to gain superior decision result.In now deep foundation excavation, the importance of subsidence forecast is more and more prominent. This article introduced some existing commonly used forecast methods and proposes the view to the later development. Taking BP artificial neural networks as the foundation. Using its formidable non-linear mapping ability. Establishes the forecast mold which deep foundation excavation peripheral building was subsidence. In Carrying on the network training by the use measured data of the foundation. Carries on the forecast to the biggest subsidence of deep foundation excavation peripheral building. The result indicated that the forecast value and the actual value is like well. It demonstrated that this method is validity on carrying on the subsidence forecast to building.