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Research on the Design of Blasting Protection Hose Thickness Based on Machine Learning

Published: 24 October 2024 Publication History

Abstract

Design research on the throwing characteristics of blasting flying rocks and the thickness of the protective water belt is limited. In order to further improve the accuracy of the prediction results, based on the experimental data of engineering blasting and Fluent simulation data, a four-input (object density, object radius, distance and initial velocity) and an output (the maximum distance the object entered the water). Establish the XGBoost prediction model, and compare it with the traditional BP neural network and RBF network. In order to obtain the best prediction performance, cuckoo is used to optimize the hyperparameters of the three models. The results show that all three models have good prediction accuracy and generalization ability. The prediction performance of the CSA-XGBoost model is better, the root mean square error is smaller, and the regression coefficient is higher. This model can be used to quickly predict the minimum thickness of the protective water belt, providing new insights and reliable methods for blasting safety protection work.

References

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    CAIBDA '24: Proceedings of the 2024 4th International Conference on Artificial Intelligence, Big Data and Algorithms
    June 2024
    1206 pages
    ISBN:9798400710247
    DOI:10.1145/3690407
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Association for Computing Machinery

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    Publication History

    Published: 24 October 2024

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    Author Tags

    1. Blast-induced flying rocks
    2. Cuckoo search algorithm
    3. Neural networks
    4. Protective water barriers
    5. XGBoost

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