Due to the complexity of underground environmental conditions and operational incidents, advanced and accurate monitoring of the underground metro shield tunnel structures is crucial for maintenance and the prevention of mishaps. In the past few decades, numerous deep learning-based damage identification studies have been conducted on aboveground civil infrastructure. However, a few studies have been conducted for underground metro shield tunnels. This paper presents a deep learning-based damage identification study for underground metro shield tunnels. Based on previous experimental studies, a numerical model of a metro tunnel was utilized, and the vibration data obtained from the model under a moving load analysis was used for the evaluation. An existing deep auto-encoder (DAE) that can support deep neural networks was utilized to detect structural damage accurately by incorporating raw vibration signals. The dynamic analysis of a metro tunnel FEM model was conducted with different severity levels of the damage at different locations and elements on the structure. In addition, root mean square (RMS) was used to locate the damage at the different locations in the model. The results were compared under different schemes of white noise, varying levels of damage, and an intact state. To test the applicability of the proposed framework on a small dataset, the approach was also utilized to investigate the damage in a simply supported beam and compared with two deep learning-based methods (SVM and LSTM). The results show that the proposed DAE-based framework is feasible and efficient for the damage identification, damage size evaluation, and damage localization of the underground metro shield tunnel and a simply supported beam with comparison of two deep models.

In this study, the cyclic behavior of reinforced concrete (RC) beam with openings strengthened using carbon fiber-reinforced polymers (FRPs) was experimentally investigated. Seven rectangular RC beams were cast and strengthened through external bonding of carbon fiber-reinforced polymer (CFRP) sheets around the beam web opening with different orientations to evaluate the maximum resistance, secant stiffness, strength degradation, ductility, energy dissipation capacity and behavior of the specimens’ failure mode under cyclic load. One solid beam without an opening (i.e., control specimen) and six beams constructed with circular web openings typically located in the middle of the beam and adjacent to the supports were used in the experiments. Among the six specimens with opening configuration, two beams were unstrengthened, and the remaining four specimens were strengthened with two layers of FRP sheets with vertical and inclined scheme orientation. Numerical studies were performed on ABAQUS software, and finite element modelling analysis results were verified through experiments. Results demonstrated that the use of FRP sheets has a significant effect on the cyclic behavior of RC beams, thereby improving the maximum strength and ultimate displacement to approximately 66.67% and 77.14%, respectively. The validated finite element models serve as a numerical platform to apply beneficial parametric studies, where the effects of opening size and bond length are investigated.

In this study, the behavior of reinforced concrete beams reinforced with FRP bars was investigated. A total of seventeen models were carried out based on the finite element software (ABAQUS). The concrete damage plasticity modeling was considered. Two types of fiber polymer bars, CFRP and GFRP as longitudinal reinforcement for concrete beam were used. The validation of numerical results was confirmed by experimental results, then the parametric study was conducted to evaluate the effect of change in different parameters, such as (diameter size, number of bars), type of FRP bars, longitudinal arrangement for FRP bars. All results were analyzed and discussed through, load-deflection diagram, according, to the difference parameter considered. The results showed that the use of FRP bars in rebar concrete beam improves the beam stiffness and enhance the cracking load. The load capacity enhanced in the range of (7.88-64.82%) when used CFRP bars. The load-carrying capacity of beams strengthened with CFRP is higher than that of strengthened with GFRP. Furthermore, the use of FRP bars in bottom and steel in top reinforcement is useful to overcome the large deflection, and improving the beam ductility. Finally, the results of finite element models were compared with the prediction equation, according to ACI440.1R-15.