Journal of Engineering Geology

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The development of large cities requires the use the underground networks for the construction of transportation infrastructures and facilities. Construction of tunnels in soft grounds induces generally soil movement, which could seriously affect the stability and integrity of existing structures. In order to reduce such movements, in particular in urban areas, contractors use more and more the tunnel boring machines (TBM) for the construction of tunnels. Hence in urban environment, Prediction of the ground movements caused by the tunnel excavation is a major engineering challenge. In this paper is used a three-dimensional numerical model and ABAQUS cod for the prediction of soil movements induced during tunnel construction in part of the line 3 of Tehran subway using EPB excavation machine. This investigation include most shield tunneling components such as face pressure, the grouting pressure, excavation machine and frictional contact with soil and shield. Observations of the results demonstrate that the maximum surface settlement in this section is 2.5 cm that is 0.5 cm more than the its allowable value. Simultaneously with surface settlements occur horizontal movements within soil mass, which have different forms in two horizontal directions, and with the expansion of depth they increasing.

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This paper presents a case study of the instability mechanism, to verify and reinforcement method adopted construct collapsed zone of Sabzkuh water conveyance tunnel in southwest Iran. The instability problems were encountered during tunnel excavation due to the failure, changes in stress field lead to deformation causing dilation and increasing the permeability of sand and gravel layers, local fault gouge zones, landslide and in turn significant reduction in shear strength and collapse in tunnel. IPE Arch Support Technique (IAST) was, used for T1 part of Sabzkuh tunnel zone in order to reinforce the ground around tunnel and to cross the zone falling. In this study, Finite Element Method was employed for the quantitative reinforcement effect with deformation modulus of ground, IPE length and size. As a result, the settlement increases as length increases and decreases with the increase of the deformation modulus of ground and IPE size.  

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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.

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