Research on interface recognition technology for drilled concrete pile based on PSO-GA-BP neural network

Bin Chen; Hong Zhang; Pengfei Li; Changgang Xu; Lifeng Fan

doi:10.1117/12.3055587

12 December 2024 Research on interface recognition technology for drilled concrete pile based on PSO-GA-BP neural network

Bin Chen, Hong Zhang, Pengfei Li, Changgang Xu, Lifeng Fan

Author Affiliations +

Proceedings Volume 13439, Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024); 1343934 (2024) https://doi.org/10.1117/12.3055587
Event: Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024), 2024, Xiamen, China

Abstract

The drilling grouting pile, with its mature construction technology, high bearing capacity, and wide range of applications, has been widely used in the foundation of road, railway, and bridge structures. Nonetheless, improper placement of the concrete injection interface within the drilling hole can easily cause issues like over-injection and other engineering difficulties, leading to wasted materials and higher expenses. The traditional method of judge cement covering using a rope and a weighty mallet is both time-overwhelming and labor-intensive, creation it difficult to achieve accurate consequence. To direction this topic, this newspaper current a novel technology for identify the cement vaccination connection, which leverages a multi-sensor data acquisition cloud platform in conjunction with the PSO-GA-BP neural network approach. By collecting and analyzing data on turbidity, conductivity, and pH levels, the PSO-GA-BP neural network model effectively identifies the injection interface, determines its position and height, and facilitates intelligent monitoring of the concrete injection process. This innovation effectively resolves challenges associated with both over-injection and under-injection.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Bin Chen, Hong Zhang, Pengfei Li, Changgang Xu, and Lifeng Fan "Research on interface recognition technology for drilled concrete pile based on PSO-GA-BP neural network", Proc. SPIE 13439, Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024), 1343934 (12 December 2024); https://doi.org/10.1117/12.3055587

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KEYWORDS

Neural networks

Interfaces

Particles

Education and training

Particle swarm optimization

Neurons

Genetic algorithms

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