Study On R-Surface Wave And Nerual Network Technology Of Dynamic Compaction Foundation Detecting

Nowadays the real estate and construction of highway are flourishing in Fujian Province. A lot of projects are built on a site of the mountain area or along the rivers. In order to the foundation design and construction of the project, some high protruding area are dug up, while low concave area filled with soil are treated by dynamic compaction . Dynamic compaction is a kind of foundation treatments, which can improve the bearing capacity and compactness of soil mass. The effect of the dynamic compaction directly influences the stability of the superstructure, it is important for the foundation design to test the effect of dynamic compaction. The detection of foundation improvement now mainly depends on drilling hole test and standard penetration test, dynamic penetration test and plate loading test. But in the large area dynamic foundation, the relatively less measure points can't totally reflect the effect of dynamic compaction. It is necessary to develop a fast way of exploration and data processing. The thesis deals with transient R-surface wave processing and artificial neural networks and develops a way of detecting and estimating the degree of compaction, uniformity, the bearing capacity of the dynamic foundation.R-surface wave theory and its engineering application are discussed. The R-surface wave equation and artificial neural network BP algorithm are deduced. A direct and inverse calculation processing of transient R-surface wave is improved ,an artificial neural network processing software is designed. Using mounts of R-surface wave data sampled by practical engineering of dynamic compaction. The compaction degree and uniformity of dynamic foundation are systematically discussed in the thesis. The collected data of in situ testing ,combined with R-wave velocity parameter, was used to develop the way to estimate the bearing capacity by multi-parameter and neural network. The innovation points in this thesis are: (1) Before the collection of transient R-surface wave data, selected the parameter of impulse, receive, and observation in the experiment. (2) Estimated the bearing capacity of foundation with multi-parameter. The results show that the R-surface wave method is non-destructive, fast detecting method, and can be used in large area dynamic foundation compaction detecting. It gets advantage of non-destructive detection, successfully handles the problems in the early exploration method, such as sparseness of measure points, high cost of money and time. BP neural networkalgorithm contains kinds of non-linear calculation method. It avoids the interference of single testing and acquires the effect of the combined testing data. By the statistic of multi-parameter and the recognition of multi-parameter, the treated foundation bearing capacity can be stimulated. It is helpful for us to reveal the characteristic of R-surface wave and the principle of artificial neural networks, and improve the application of R-surface wave and BP algorithm in the theory and practice.