Application of the Cohesion Softening–Friction Softening and the Cohesion Softening–Friction Hardening Models of Rock Mass Behavior to Estimate the Specific Energy of TBM, Case Study: Amir–Kabir Water Conveyance Tunnel in Iran

Mirahmadi, Majid; Dehkordi, M. Soleiman

doi:10.1007/s10706-018-0617-5

Geotech Geol Eng

2018

DOI: 10.1007/s10706-018-0617-5

|View full text |Cite

Application of the Cohesion Softening–Friction Softening and the Cohesion Softening–Friction Hardening Models of Rock Mass Behavior to Estimate the Specific Energy of TBM, Case Study: Amir–Kabir Water Conveyance Tunnel in Iran

Majid Mirahmadi

M. Soleiman Dehkordi

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2019

2024

Publication Types

Select...

Article3

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A soft ground micro TBM’s specific energy prediction using an eXplainable neural network through Shapley additive explanation and Optuna

Kilic,

Ikeda,

Narihiro

et al. 2024

Bull Eng Geol Environ

View full text Add to dashboard Cite

In tunnel construction, efficiently predicting the energy usage of tunnel boring machines (TBMs) is critical for optimizing operations and reducing costs. This research proposes a novel method for predicting the specific energy of micro slurry tunnel boring machines (MSTBMs) using an explainable neural network (xNN) that leverages operator-monitored data. The xNN model provides transparency and interpretability by integrating the Shapley additive explanation (SHAP) technique, enabling tunneling engineers and operators to gain valuable insights into the prediction process. Extensive data from MSTBM umbrella pipe support excavation are the foundation for training, testing, and unseen data in the xNN model. The specific energy formula derived from the operational parameters of the MSTBM defines the dependent variable for the xNN model. The test dataset evaluates the model’s performance with an R² of 98.7%, an MSE of 2.40, and an MAE of 0.003, demonstrating its accuracy and reliability. Ten percent of the dataset was reserved as unseen data to assess the model’s generalization capabilities. Upon evaluation, the model achieved an R2 value of 89%, an MAE of 0.01, and a root mean squared error (RMSE) of 0.01. The xNN empowers operators to optimize operational parameters and promote more efficient and sustainable tunneling practices by identifying influential factors affecting energy consumption through its interpretable nature. This research has significant implications for the future of underground construction, paving the way for improved resource management.

show abstract

A soft ground micro TBM’s specific energy prediction using an eXplainable neural network through Shapley additive explanation and Optuna

Kilic,

Ikeda,

Narihiro

et al. 2024

Bull Eng Geol Environ

View full text Add to dashboard Cite

show abstract

A quantitative display index that considers tensile failure to predict the full sliding surface of a landslide

2019

View full text Add to dashboard Cite

A Study on the Distribution and Evolution of Mining Induced Stress under the Condition of Multiple Mining

Wu¹,

Luo

et al. 2020

Geotech Geol Eng

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Application of the Cohesion Softening–Friction Softening and the Cohesion Softening–Friction Hardening Models of Rock Mass Behavior to Estimate the Specific Energy of TBM, Case Study: Amir–Kabir Water Conveyance Tunnel in Iran

Cited by 3 publications

References 22 publications

A soft ground micro TBM’s specific energy prediction using an eXplainable neural network through Shapley additive explanation and Optuna

A soft ground micro TBM’s specific energy prediction using an eXplainable neural network through Shapley additive explanation and Optuna

A quantitative display index that considers tensile failure to predict the full sliding surface of a landslide

A Study on the Distribution and Evolution of Mining Induced Stress under the Condition of Multiple Mining

Contact Info

Product

Resources

About