Abstract
Deformation monitoring, as a key link of information construction, runs through the entire process of the building design period, construction period and operation period[1]. At present, more mature static prediction methods include hyperbolic method, power polynomial method and Asaoka method. But these methods have many problems and shortcomings. In this paper, based on the characteristics of building foundation settlement and the methods widely discussed in this field, a wavelet neural network model with self-learning, self-organization and good nonlinear approximation ability is applied to the prediction problem of building settlement[2]. Using comparative analysis and induction method. The 20-phase monitoring data representing the deformation monitoring points of different settlement states of the line tunnel, using the observation data sequence of the first 15 phases respectively to take the cumulative settlement and interval settlement as training samples, through the BP artificial neural network and the improved wavelet neural network, for the last five periods Predict the observed settlement.Through the comparison, it is found that whether the interval settlement or the cumulative settlement is used, the prediction results of the wavelet neural network are basically better than the prediction results of the BP artificial neural network, and the number of trainings is greatly reduced. The adaptive prediction of the wavelet neural network. The ability is particularly obvious, and the prediction accuracy is significantly improved. Therefore, it can be shown that the wavelet neural network is indeed used in the settlement monitoring and forecast of buildings, which can obtain higher prediction accuracy and better prediction effect, and is a prediction method with great development potential.
Highlights
With the rapid development of the city, and in order to pursue more residential space, various high-rise buildings are rising from the ground, while in the buildings under load, the ground gene is disturbed by the load, causing the building to settle
The arbitrary approximation characteristics of nonlinear continuous functions in wavelet analysis are well distinguished in time and frequency, making the wavelet neural network constructed based on wavelet analysis theory more suitable for learning local nonlinear and rapidly changing functions
There is no local minimum point, the convergence speed is faster, and the accuracy is higher. Regardless of whether it is interval settlement or cumulative settlement, it can be seen from the prediction results that the prediction results of the wavelet neural network are basically better than the prediction results of the BP artificial neural network, and the number of trainings is greatly reduced
Summary
With the rapid development of the city, and in order to pursue more residential space, various high-rise buildings are rising from the ground, while in the buildings under load, the ground gene is disturbed by the load, causing the building to settle. For the purpose of real-time prediction, we can try to apply neural network to the problem of building settlement. Artificial neural network has the characteristics of large-scale parallel processing, distributed storage, high redundancy and nonlinear calculation. It has a very high computing speed, strong association ability, adaptability and self-organization ability. These characteristics are very suitable for real-time prediction of building settlement. It is concluded that the wavelet neural network can be used in building settlement monitoring and forecasting, which provides certain basis and guidance for building settlement monitoring
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