Study On Foundation Pit Deformation Monitoring Based On Superstation

A deformable body’s position, shape and size change easily in the spatial domain and time domain under the influence of a variety of factors in the outside world, so deformation is a common natural phenomenon. Deformable body’s deformation in a certain degree is allowed, which will lead to a disaster if exceeding the allowed values. With the continuous development of economic construction, more and more high-rise buildings in the city stand up, but the size and depth of the building foundation pits are also expanding. The foundation pits’deformation monitoring plays an important guarantee for the safety of the construction. Establishing a rational, scientific foundation deformation monitoring, as well as the deformation trend analysis has important practical significance to the project construction.By studying on designing pit deformation monitoring program, obtaining and processing data, analyzing precision, denoising, and predicting deformation, this paper put forward a specific implementation plan for observing deformation with the super-station instrument. The main contents are as follows:1. This paper studies how to well-develop a monitoring program, select appropriate monitoring equipment and monitoring methodology, select reasonable monitoring sites and monitoring cycle, to meet the contents and requirements for the pit deformation monitoring.2. General Total Station deformation monitoring need erected at the fixed point, while the super-station instrument needn’t, which has the advantage of real-time positioning. This paper studies the data processing method and differential correction value calculation method of the polar coordinates deformation monitoring with super-station instrument.3. Forecasting of pit deformation monitoring has great significance to preventing a major disaster. In this paper, wavelet denoising is used to remove the deformation monitoring singular value. Based on this, the optimized deformation data is predicted combining with BP neural network, and the overall deformation process and trends of the pit are studied.