چکیده مقاله
The need to predict geotechnical parameters in soft ground is highly essential in evaluating the performance parameters of earth pressure balance machine EPB TBM and ensuring the safety and efficiency of the tunnel boring machine during tunnel construction In this study, several EPB operating parameters, including cutterhead torque, thrust force, chamber pressure, and the cutterhead speed revolution RPM were taken into account to estimate the geotechnical parameters such as friction angle φ and shear strength τ for engineering geological units ET1 to ET5 in the tunnel route which are indicator units for the whole path A soft computing technique called Support Vector Machine Ls SVM was trained with EPB operating parameters and geotechnical specifications obtained from Tehran metro line 6 southern extension sector TML SE6 and the East west lot of line 7, Tehran metro project TML EW7 A comprehensive dataset consists of borehole logging results along the tunnel path was collected and 85% of the data for training were randomly selected, while the remaining were considered for model testing For the purpose of assessing the performance of the applied method and for evaluating the accuracy and precision, several loss functions, including mean absolute deviation MAD , mean square error MSE , mean absolute percentage error MAPE , and relative absolute error RAE were brought on stream The results of the proposed models indicate an acceptable and reliable accuracy of the approaches Comparison of machine learning and multiple variable regression MVR method with measured data, it can be seen that the deviation intervals of the predicted values are small and the prediction values resulted from support vector machine SVM and MVR is in good agreement with the measured values of geotechnical parameters In other words, the mentioned models, especially SVM, can predict the geotechnical parameters at the tunnel face Hence, it can be applied for the accurate prediction of geotechnical parameters based on EPB operating data
کلیدواژهها
نویسندگان
Hanan Samadi
MSc , School of Geology, College of Science, University of Tehran, Tehran, Iran,
Jafar Hassanpour
Associate Professor, School of Geology, College of Science, University of Tehran, Tehran, Iran,
شیوه ارجاع
Samadi, Hanan and Hassanpour, Jafar,1402,Use of Soft Computing Technic for Predicting GeotechnicalParameters Based on EPB-TBM Performance,The 14th Iran Tunnel Conference,Tehran
ارائهشده در
مجموعه مقالات چهاردهمین کنفرانس تونل ایران1 خرداد 1402 · تهران