Application of deep neural networks in predicting the penetration rate of tunnel boring machines

• Published in: Bulletin of engineering geology and the environment Vol. 78; no. 8; pp. 6347 - 6360
• Main Authors: Koopialipoor, Mohammadreza, Tootoonchi, Hossein, Jahed Armaghani, Danial, Tonnizam Mohamad, Edy, Hedayat, Ahmadreza
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2019
• Subjects: Earth and Environmental Science; Earth Sciences; Foundations; Geoecology/Natural Processes; Geoengineering; Geotechnical Engineering & Applied Earth Sciences; Hydraulics; Nature Conservation; Original Paper; Penetration rate; Deep neural network; Tunnel boring machine; Artificial neural network
• ISSN: 1435-9529; 1435-9537
• DOI: 10.1007/s10064-019-01538-7

Performance prediction in mechanized tunnel projects utilizing a tunnel boring machine (TBM) is a prerequisite to accurate and reliable cost estimation and project scheduling. A wide variety of artificial intelligence methods have been utilized in the prediction of the penetration rate of TBMs. This study focuses on developing a model based on deep neural networks (DNNs), which is an advanced version of artificial neural networks (ANNs), for prediction of the TBM penetration rate based on the data obtained from the Pahang–Selangor raw water transfer tunnel in Malaysia. To evaluate and document the success and reliability of the new DNN model, an ANN model based on five different data categories from the established database was developed and compared with the DNN model. Based on the results obtained of the coefficient of determination and root mean square error (RMSE), a significant increase in the performance prediction of the penetration rate is achieved by developing a DNN predictive model. The DNN model demonstrated better performance for penetration rate estimation compared with the ANN model and it can be introduced as a newly developed model in the field of TBM performance assessment.