Abstract: The shallow overburden pipe jacking over operation tunnel construction project in chalk stratum has the risk of deformation of the soil layer and the existing tunnel, which increases the difficulty of pipe jacking over construction, and the risk assessment and control become the key technology for the safe and successful completion of the construction. Aiming at the problems of the current deformation risk assessment and control method, such as the assessment system is not comprehensive, systematic and objective enough, the prediction accuracy is not efficient enough, and there is a lack of quantitative analysis, etc., a deformation risk assessment and control method is proposed to combine the heuristic optimization algorithm of human behaviour and deep machine learning algorithm for pipe jacking up to and across operation tunnels on shallow overburden of chalky sand stratum. Firstly, by analyzing the construction process of pipe jacking tunnel, the deformation risk factors of the construction process and the deformation risk control scheme are given; then, a deformation risk assessment and control algorithm with improved deep limit learning machine is proposed by combining human heuristic optimization algorithm; finally, the proposed deformation assessment and control model is applied to the deformation risk assessment and control problem of pipe jacking over operation tunnel on shallow overburden of pulverised sand stratum, and a finite element computational model is used to construct the data. Finally, the proposed deformation assessment and control model is applied to the problem of deformation risk assessment and control in a tunnel with shallow overburden in chalky sand stratum by using finite element computational model to construct the data set, training the deformation risk assessment and control model, and using the monitoring data as the test set to validate the validity of the proposed model algorithm, and solving the problem of the poor prediction accuracy of the control algorithm for deformation risk assessment and control of a tunnel with shallow overburden in a tunnel with shallow overburden in chalky sand stratum.

Fusheng Liu, “A Data-Driven Deep Machine Learning Approach for Tunnel Deformation Risk Assessment” International Journal of Advanced Computer Science and Applications(IJACSA), 15(11), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151127

@article{Liu2024,
title = {A Data-Driven Deep Machine Learning Approach for Tunnel Deformation Risk Assessment},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151127},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151127},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {11},
author = {Fusheng Liu}
}