caa a22 4500 605454078 CHVBK 20210128100205.0 cr unu---uuuuu 210128e20150801xx s 000 0 eng 10.1007/s10064-014-0660-2 doi (NATIONALLICENCE)springer-10.1007/s10064-014-0660-2 Development of artificial neural networks and multiple regression models for the NATM tunnelling-induced settlement in Niayesh subway tunnel, Tehran [Elektronische Daten] [S. Mohammadi, F. Naseri, S. Alipoor] Here we investigate maximum settlement prediction of the Niayesh subway tunnel, excavated by employing the New Austrian Tunnelling Method in the Tehran metropolitan area, by several approaches, such as the semi-empirical method, linear and non-linear multiple regression method (MR), and finally by a programming Multi-Layered Perception (MLP) with a Back Propagation training algorithm. The geology at the site is mostly composed of conglomerates with pebbles and boulders. The maximum settlement is estimated based on the semi-empirical relations represented by several researchers. The input data set for MR and MLP models are soil characteristic [cohesion (C), internal friction angle (φ), elasticity modulus (E) and unit weight (Gs)], excavation depth (Z 0), soil type (S t) and PLAXIS 2D settlement prediction by the Hardening Soil model. Among all MLP and MR models, MLP models and especially model 6, the model based on E, Z, φ, Gs, C and S t variables, seem to be reliable and agreeable to numerical results. The performance of MR, MLP, and optimized MLP models are evaluated by comparing statistic parameters, including coefficient correlations (R), root mean square error (RMSE), mean error (ME) and Akaike information criterion (AIC), whose values for model 6 are 0.93, 1.66, 0.89 and 13.16, respectively. Therefore, compared to other MLP and MR models, the optimized MLP model shows a relatively high level of accuracy. Additionally, model 4, the model based on E, Z, φ and Gs variables, shows in MLP analysis that unit weight does not have significant effect on maximum settlement. Springer-Verlag Berlin Heidelberg, 2014 Niayesh subway tunnel nationallicence Excavation-induced settlement nationallicence Numerical analysis nationallicence MR models nationallicence MLP models nationallicence Mohammadi S. Department of Geology, Faculty of Sciences, Bu-Ali Sina University, Mahdieh Ave, 65175-38695, Hamedan, Iran aut Naseri F. Department of Geology, Faculty of Sciences, Bu-Ali Sina University, Mahdieh Ave, 65175-38695, Hamedan, Iran aut Alipoor S. Department of Geology, Faculty of Sciences, Bu-Ali Sina University, Mahdieh Ave, 65175-38695, Hamedan, Iran aut Bulletin of Engineering Geology and the Environment Springer Berlin Heidelberg 74/3(2015-08-01), 827-843 1435-9529 74:3<827 2015 74 10064 https://doi.org/10.1007/s10064-014-0660-2 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10064-014-0660-2 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Mohammadi S. Department of Geology, Faculty of Sciences, Bu-Ali Sina University, Mahdieh Ave, 65175-38695, Hamedan, Iran aut NATIONALLICENCE 700 1- Naseri F. Department of Geology, Faculty of Sciences, Bu-Ali Sina University, Mahdieh Ave, 65175-38695, Hamedan, Iran aut NATIONALLICENCE 700 1- Alipoor S. Department of Geology, Faculty of Sciences, Bu-Ali Sina University, Mahdieh Ave, 65175-38695, Hamedan, Iran aut NATIONALLICENCE 773 0- Bulletin of Engineering Geology and the Environment Springer Berlin Heidelberg 74/3(2015-08-01), 827-843 1435-9529 74:3<827 2015 74 10064