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A platform for research: civil engineering, architecture and urbanism
A Rock Mass Strength Prediction Method Integrating Wave Velocity and Operational Parameters Based on the Bayesian Optimization Catboost Algorithm
https://orcid.org/https://orcid.org/0009-0005-7217-2412
https://orcid.org/https://orcid.org/0000-0001-5375-4955
https://orcid.org/https://orcid.org/0000-0002-3588-3478
Tunnel Boring Machines (TBMs) have been the main equipment for tunneling and underground construction due to their high safety performance and tunneling efficiency. However, the unknown and changing geological conditions during construction pose a challenge to TBM construction. As one of the essential parameters of rock properties, accurate acquisition of uniaxial compressive strength (UCS) is crucial for TBMs to adapt to changing ground conditions in a timely manner. Therefore, this study proposes a Catboost intelligent model based on Bayesian Optimization to predict UCS. Rock mass are velocity information and key TBM operational parameters are used as model input variables. The Gaussian data augmentation method is used to compensate for the difficulty of obtaining field data in large quantities. The Zhujiang Delta Water Resources Allocation Engineering field data are used in the model, and the obtained evaluation indicators MAPE, RMSE, VAF and a20-index are obtained as 9.91%, 499.38 MPa, 90.7% and 0.95, respectively. In addition, another project is selected to verify the applicability of the model. The validation results also confirm that the model is valid and reliable when applied to practical engineering.
A Rock Mass Strength Prediction Method Integrating Wave Velocity and Operational Parameters Based on the Bayesian Optimization Catboost Algorithm
https://orcid.org/https://orcid.org/0009-0005-7217-2412
https://orcid.org/https://orcid.org/0000-0001-5375-4955
https://orcid.org/https://orcid.org/0000-0002-3588-3478
Tunnel Boring Machines (TBMs) have been the main equipment for tunneling and underground construction due to their high safety performance and tunneling efficiency. However, the unknown and changing geological conditions during construction pose a challenge to TBM construction. As one of the essential parameters of rock properties, accurate acquisition of uniaxial compressive strength (UCS) is crucial for TBMs to adapt to changing ground conditions in a timely manner. Therefore, this study proposes a Catboost intelligent model based on Bayesian Optimization to predict UCS. Rock mass are velocity information and key TBM operational parameters are used as model input variables. The Gaussian data augmentation method is used to compensate for the difficulty of obtaining field data in large quantities. The Zhujiang Delta Water Resources Allocation Engineering field data are used in the model, and the obtained evaluation indicators MAPE, RMSE, VAF and a20-index are obtained as 9.91%, 499.38 MPa, 90.7% and 0.95, respectively. In addition, another project is selected to verify the applicability of the model. The validation results also confirm that the model is valid and reliable when applied to practical engineering.
A Rock Mass Strength Prediction Method Integrating Wave Velocity and Operational Parameters Based on the Bayesian Optimization Catboost Algorithm
https://orcid.org/https://orcid.org/0009-0005-7217-2412
https://orcid.org/https://orcid.org/0000-0001-5375-4955
https://orcid.org/https://orcid.org/0000-0002-3588-3478
Tunnel Boring Machines (TBMs) have been the main equipment for tunneling and underground construction due to their high safety performance and tunneling efficiency. However, the unknown and changing geological conditions during construction pose a challenge to TBM construction. As one of the essential parameters of rock properties, accurate acquisition of uniaxial compressive strength (UCS) is crucial for TBMs to adapt to changing ground conditions in a timely manner. Therefore, this study proposes a Catboost intelligent model based on Bayesian Optimization to predict UCS. Rock mass are velocity information and key TBM operational parameters are used as model input variables. The Gaussian data augmentation method is used to compensate for the difficulty of obtaining field data in large quantities. The Zhujiang Delta Water Resources Allocation Engineering field data are used in the model, and the obtained evaluation indicators MAPE, RMSE, VAF and a20-index are obtained as 9.91%, 499.38 MPa, 90.7% and 0.95, respectively. In addition, another project is selected to verify the applicability of the model. The validation results also confirm that the model is valid and reliable when applied to practical engineering.
A Rock Mass Strength Prediction Method Integrating Wave Velocity and Operational Parameters Based on the Bayesian Optimization Catboost Algorithm
KSCE J Civ Eng
Wang, Yaxu (author) / Wang, Ruirui (author) / Wang, Jiwen (author) / Li, Ningbo (author) / Cao, Hongyi (author)
KSCE Journal of Civil Engineering ; 27 ; 3148-3162
2023-07-01
15 pages
Article (Journal)
Electronic Resource
English
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