Seismic Performance of Vertically Prestressed Semicircular Anti-Slide Piles Based on Deep Machine-learning Algorithms

Abstract

Deep machine learning algorithm is a method involving technology, data analysis, statistics, monitoring and so on. Its main goal is to extract, transfer, process and manage information from the data, so as to realize the acquisition and application of knowledge. Deep machine learning algorithms are those machine learning algorithms based on multi-layer neural network structure, with strong learning ability and expression ability. The semicircular vertical prestressed anchor cable anti-slide pile is a new technology for the treatment of large-scale landslides on the bank slopes of Jianghe Reservoir and mountain highways. In this structure, the prestressed steel strand is vertically placed in the anti-slide pile, which can reduce the failure of the prestressed anchor cable in water and the loss of the free length. Its semicircular section fully utilizes the eccentric stress characteristics. It not only enhances the compressive performance of the compression zone of the anti-slide pile, but also improves the weakness of the tensile zone of the anti-slide pile. At the same time, compared to traditional circular piles, the cross-sectional area of semicircular piles is reduced, significantly reducing the amount of materials used, and reducing disasters caused by landslide instability and damage caused by large-scale excavation of slopes. The engineering economy and safety are significant. Through theoretical analysis, this paper constructs key technologies such as structural calculation theory and design methods, and uses model tests to verify their correctness and practicality, thereby revealing the mechanical impact mechanism of semi circular vertical prestressed structures. The experimental data show that the seismic performance of horizontal and vertical prestressed anti-slide piles is different, and the seismic performance of vertical prestressed piles has advantages in the early stage of earthquake, with a change rate of 38.5% in axial force and 20.2% in shear strength. The application of deep machine learning algorithms in the seismic performance of anti-skid piles has potential advantages, which can provide more accurate and efficient prediction and optimization methods to support the seismic design and performance evaluation of sliding piles.