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Rastbood, Armin, Majdi, Abbas, Gholipour, Yaghoob. (1396). Prediction of structural forces of segmental tunnel lining using FEM based artificial neural network. , 51(1), 71-78. doi: 10.22059/ijmge.2017.223801.594650
Armin Rastbood; Abbas Majdi; Yaghoob Gholipour. "Prediction of structural forces of segmental tunnel lining using FEM based artificial neural network". , 51, 1, 1396, 71-78. doi: 10.22059/ijmge.2017.223801.594650
Rastbood, Armin, Majdi, Abbas, Gholipour, Yaghoob. (1396). 'Prediction of structural forces of segmental tunnel lining using FEM based artificial neural network', , 51(1), pp. 71-78. doi: 10.22059/ijmge.2017.223801.594650
Rastbood, Armin, Majdi, Abbas, Gholipour, Yaghoob. Prediction of structural forces of segmental tunnel lining using FEM based artificial neural network. , 1396; 51(1): 71-78. doi: 10.22059/ijmge.2017.223801.594650

Prediction of structural forces of segmental tunnel lining using FEM based artificial neural network

International Journal of Mining and Geo-Engineering
مقاله 9، دوره 51، شماره 1، شهریور 2017، صفحه 71-78    اصل مقاله (1.69 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/ijmge.2017.223801.594650
نویسندگان
Armin Rastbood* 1؛ Abbas Majdi2؛ Yaghoob Gholipour3
1School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran.
2School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran, .Post Code: 1439957131
3School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran. Post Code: 1439957131
چکیده
To judge about the performance of designed support system for tunnels, structural forces i.e. peak values of axial and shear forces and moments are critical parameters. So in this study, at first a complete database using finite element method was prepared. Then, a model of artificial neural network (ANN) using multi-layer perceptron was developed to estimate lining structural forces. Sensitivity analysis showed that among input variables, the cover of the tunnel is most influencing variable. To prove the efficiency of developed ANN model, coefficient of efficiency (CE), coefficient of correlation (R2), variance account for (VAF), and root mean square error (RMSE) calculated. Obtained results demonstrated a promising precision and high efficiency of the presented ANN method to estimate the structural forces of tunnel lining composed from concrete segments instead of alternative costly and tedious solutions.
کلیدواژه‌ها
Artificial Neural Network؛ lining؛ Multi-Layer Perceptron؛ Segment؛ tunnel
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تعداد مشاهده مقاله: 856
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