In tunnelling in soil mass, in groundwater existing mode, liquefaction, elastic displacements and settlement in soils upon the tunnel, are the risks may attack the excavated underground space stability. In this case study that were performed on second line of Mashhad city subway route, information catched from Standard Penetration Test, in situ and laboratorial tests, were used to optimum numerical values search for soil engineering parameters that could optimize the TBM stationing level. In order to this goal attaining, intelligent, numerical and probabilistic methods were used and the reliability of intelligent and numerical methods with the Safety Factors of tunnel stability, investigated simultaneously. The results were denoting the accordance of intelligent models such as Genetic Algorithm (GA) and Multi objective Genetic Algorithm with Finite Element model's output. So these models could be complement of each others in planning and designing of tunnels and using of them advised in tunneling and excavations.
Ground settlement due to tunneling and the effects of the engineering geological factors on its dimensions and extensions, is a very important problem in shallow tunnel excavation projects in urban areas. Empirical method is one of the usual methods to study this subject. The empirical and dimension-less parameters of VL and k are the most important parameters in relation to this method that are estimated according to engineering geological factors. In this research, the values of these parameters were initially estimated based on preceding studies and the ground settlement was predicted using these estimated values of VL and k. In next stage, the results of predictions were compared with the real (measured) settlements happened due to Abuzar tunnel excavation. As the real settlements are less than the predicted ones, it was concluded that the real VL must be lower than the predicted values or the real k must be higher than the predicted values. With regard to the high dependency of these parameters to the soil cohesion, it seems natural cementation of Tehran alluvia has acted as a factor to increase the soil cohesion and has caused to decrease ground settlement due to excavation of Abuzar tunnel. For validation of this hypothesis, preceding findings about alluvia cementation were reviewed and the results of in-situ and laboratory shear and triaxial tests were compared with together. Then it is concluded that the higher cohesions of in-situ shear tests are occurred due to natural cementation of materials existing in Abuzar tunnel route
Maximum surface settlement (MSS) is an important parameter for the design and operation of earth pressure balance (EPB) shields that should determine before operate tunneling. Artificial intelligence (AI) methods are accepted as a technology that offers an alternative way to tackle highly complex problems that can’t be modeled in mathematics. They can learn from examples and they are able to handle incomplete data and noisy. The adaptive network–based fuzzy inference system (ANFIS) and hybrid artificial neural network (ANN) with biogeography-based optimization algorithm (ANN-BBO) are kinds of AI systems that were used in this study to build a prediction model for the MSS caused by EPB shield tunneling. Two ANFIS models were implemented, ANFIS-subtractive clustering method (ANFIS-SCM) and ANFIS-fuzzy c–means clustering method (ANFIS-FCM). The estimation abilities offered using three models were presented by using field data of achieved from Bangkok Subway Project in Thailand. In these models, depth, distance from shaft, ground water level from tunnel invert, average face pressure, average penetrate rate, pitching angle, tail void grouting pressure and percent tail void grout filling were utilized as the input parameters, while the MSS was the output parameter. To compare the performance of models for MSS prediction, the coefficient of correlation (R2) and mean square error (MSE) of the models were calculated, indicating the good performance of the ANFIS-SCM model.
Backfill name | UR-S | GR-S | UR-W | GR-W |
Maximum stress (kPa) | 416 | 725 | 520 | 960 |
Settlement at failure (mm) | 4.6 | 9.0 | 15.5 | 14.9 |
Plastic settlement (mm) | 3.5 | 7.0 | 12.5 | 12.0 |
Number of load cycles | 10 | 20 | 20 | 30 |
Bearing capacity ratio (BCR) | 1 | 1.74 | 1.25 | 2.32 |
Performance rating | 4 | 2 | 3 | 1 |
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