Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Assessment of shear strength of fine-grained and coarse-grained soil using actual EPB-TBM operating data
https://orcid.org/0000-0002-0385-5931
https://orcid.org/0000-0002-6909-2439
AbstractThe necessity of predicting geotechnical parameters in soft ground tunnelling is crucial for selecting the appropriate tunnel boring machine (TBM), evaluating the operating limit of earth pressure balance (EPB) machines’ parameters, and ensuring the safety and efficiency of TBMs during tunnel construction. In this research, various EPB operating parameters such as cutterhead torque, thrust force, chamber pressure, and cutterhead rotation speed (RPM) were utilized to estimate geotechnical parameters like friction angle (φ) and shear strength (τ) for engineering geological units ET1 to ET5 (fine-grained and coarse-grained soils) along the tunnels route, which serve as indicative units for the entire tunnels path. Statistical methods and computational techniques, namely support vector regression (SVR) and feed-forward neural network (FFNN), were trained using EPB operating parameters and geotechnical data from Tehran metro line 6—southern extension sector (TML-SE6) and the East–west section of line 7, Tehran metro project (TML-EW7). A comprehensive dataset comprising borehole logging results along the tunnel path was gathered, with 85% of the data randomly selected for training and the remaining 15% reserved for model testing. Various loss functions and statistical metrics were employed to evaluate the accuracy and precision of the method. The results of the proposed models demonstrate satisfactory and reliable accuracy of the approaches.
Assessment of shear strength of fine-grained and coarse-grained soil using actual EPB-TBM operating data
https://orcid.org/0000-0002-0385-5931
https://orcid.org/0000-0002-6909-2439
AbstractThe necessity of predicting geotechnical parameters in soft ground tunnelling is crucial for selecting the appropriate tunnel boring machine (TBM), evaluating the operating limit of earth pressure balance (EPB) machines’ parameters, and ensuring the safety and efficiency of TBMs during tunnel construction. In this research, various EPB operating parameters such as cutterhead torque, thrust force, chamber pressure, and cutterhead rotation speed (RPM) were utilized to estimate geotechnical parameters like friction angle (φ) and shear strength (τ) for engineering geological units ET1 to ET5 (fine-grained and coarse-grained soils) along the tunnels route, which serve as indicative units for the entire tunnels path. Statistical methods and computational techniques, namely support vector regression (SVR) and feed-forward neural network (FFNN), were trained using EPB operating parameters and geotechnical data from Tehran metro line 6—southern extension sector (TML-SE6) and the East–west section of line 7, Tehran metro project (TML-EW7). A comprehensive dataset comprising borehole logging results along the tunnel path was gathered, with 85% of the data randomly selected for training and the remaining 15% reserved for model testing. Various loss functions and statistical metrics were employed to evaluate the accuracy and precision of the method. The results of the proposed models demonstrate satisfactory and reliable accuracy of the approaches.
Assessment of shear strength of fine-grained and coarse-grained soil using actual EPB-TBM operating data
https://orcid.org/0000-0002-0385-5931
https://orcid.org/0000-0002-6909-2439
AbstractThe necessity of predicting geotechnical parameters in soft ground tunnelling is crucial for selecting the appropriate tunnel boring machine (TBM), evaluating the operating limit of earth pressure balance (EPB) machines’ parameters, and ensuring the safety and efficiency of TBMs during tunnel construction. In this research, various EPB operating parameters such as cutterhead torque, thrust force, chamber pressure, and cutterhead rotation speed (RPM) were utilized to estimate geotechnical parameters like friction angle (φ) and shear strength (τ) for engineering geological units ET1 to ET5 (fine-grained and coarse-grained soils) along the tunnels route, which serve as indicative units for the entire tunnels path. Statistical methods and computational techniques, namely support vector regression (SVR) and feed-forward neural network (FFNN), were trained using EPB operating parameters and geotechnical data from Tehran metro line 6—southern extension sector (TML-SE6) and the East–west section of line 7, Tehran metro project (TML-EW7). A comprehensive dataset comprising borehole logging results along the tunnel path was gathered, with 85% of the data randomly selected for training and the remaining 15% reserved for model testing. Various loss functions and statistical metrics were employed to evaluate the accuracy and precision of the method. The results of the proposed models demonstrate satisfactory and reliable accuracy of the approaches.
Assessment of shear strength of fine-grained and coarse-grained soil using actual EPB-TBM operating data
Geo-Engineering
Samadi, Hanan (Autor:in) / Hassanpour, Jafar (Autor:in) / Rostami, Jamal (Autor:in)
27.09.2024
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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