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    Capacity-spectrum push-over analysis of rock-lining interaction model for seismic evaluation of tunnels
    (Techno-Press, 2024) Majidian, Sina; Tapkin, Serkan; Tercan, Emre
    Evaluation of tunnel performance in seismic -prone areas demands efficient means of estimating performance at different hazard levels. The present study introduces an innovative push -over analysis approach which employs the standard earthquake spectrum to simulate the performance of a tunnel. The numerical simulation has taken into account the lining and surrounding rock to calculate the rock -tunnel interaction subjected to a static push -over displacement regime. Elastic perfectly plastic models for the lining and hardening strain rock medium were used to portray the development of plastic hinges, nonlinear deformation, and performance of the tunnel structure. Separately using a computational algorithm, the non-linear response spectrum was approximated from the average shear strain of the rock model. A NATM tunnel in Turkey was chosen for parametric study. A seismic performance curve and two performance thresholds are introduced that are based on the proposed nonlinear seismic static loading approach and the formation of plastic hinges. The tunnel model was also subjected to a harmonic excitation with a smooth response spectrum and different amplitudes in the fully -dynamic phase to assess the accuracy of the approach. The parametric study investigated the effects of the lining stiffness and capacity and soil stiffness on the seismic performance of the tunnel.
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    Computational Intelligence Based Point of Interest Detection by Video Surveillance Implementations
    (Zarka Private Univ, 2023) Tercan, Emre; Tapkin, Serkan; Kucuk, Furkan; Demirtas, Ali; Ozbayoglu, Ahmet; Turker, Abdussamet
    Latest advancement of the computer vision literature and Convolutional Neural Networks (CNN) reveal many opportunities that are being actively used in various research areas. One of the most important examples for these areas is autonomous vehicles and mapping systems. Point of interest detection is a rising field within autonomous video tracking and autonomous mapping systems. Within the last few years, the number of implementations and research papers started rising due to the advancements in the new deep learning systems. In this paper, our aim is to survey the existing studies implemented on point of interest detection systems that focus on objects on the road (like lanes, road marks), or objects on the roadside (like road signs, restaurants or temporary establishments) so that they can be used for autonomous vehicles and automatic mapping systems. Meanwhile, the roadside point of interest detection problem has been addressed from a transportation industry perspective. At the same time, a deep learning based point of interest detection model based on roadside gas station identification will be introduced as proof of the anticipated concept. Instead of using an internet connection for point of interest retrieval, the proposed model has the capability to work offline for more robustness. A variety of models have been analysed and their detection speed and accuracy performances are compared. Our preliminary results show that it is possible to develop a model achieving a satisfactory real-time performance that can be embedded into autonomous cars such that streaming video analysis and point of interest detection might be achievable in actual utilisation for future implementations.
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    Energy-driven TBM health status estimation with a hybrid deep learning approach
    (Pergamon-Elsevier Science Ltd, 2024) Li, Yongsheng; Zhang, Limao; Pan, Yue; Tapkin, Serkan; Song, Xieqing
    This study provides a hybrid deep learning approach that enables the researchers to estimate the Tunnel Boring Machine (TBM) health status during excavation process using energy consumption data. By analyzing the energy forms and paths in the tunneling process, the key energy factors' consumption performance behavior affecting TBM safety is identified. The Ensemble Empirical Mode Decomposition (EEMD) method and a Convolution neural network-Long short term memory-Depth Neural Network (CNN-LSTM-DNN, CLDNN) model are integrated for Health Performance Parameter (HPP) prediction. A classification method is constructed by the Generative Adversarial Imputation Networks (GAIN) model for health status estimation. The SHapley Additive exPlanations (SHAP) method is performed to quantify the relationships between energy consumption and system health status. A tunneling case in Singapore is used to test the effectiveness of the developed methodology. It is found that (1) the proposed method provides TBM cutter wear and cooling oil prediction with a R-square (R2) of 0.8591 and 0.8727, respectively, and (2) classification results indicate that the estimation F1-score of 0.9863 can be achieved based on health status evaluation indicator (HSEI). The novelty of the proposed approach lies in its capabilities of (1) fully extracting and utilizing the energy consumption information in every regard to predict TBM health performance; (2) providing a good one-step-ahead health status estimation for the TBM excavation process. Therefore, this study proposes a feasible direction for the application of energy consumption data in TBM health status estimation.
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    Structural Investigation of Masonry Arch Bridges Using Various Nonlinear Finite-Element Models
    (Asce-Amer Soc Civil Engineers, 2022) Tapkin, Serkan; Tercan, Emre; Motsa, Siphesihle Mpho; Drosopoulos, Georgios; Stavroulaki, Maria; Maravelakis, Emmanuel; Stavroulakis, Georgios
    This article presents an investigation of the structural behavior of a masonry arch bridge in Turkey. An analytical study has been conducted to provide the geometry of the structure, using laser scanning. A point cloud describing the geometry was obtained and properly transformed into a format that is appropriate for structural analysis software (CAE). Then, nonlinear finite-element models were developed to simulate structural responses of the bridge. The goal of the article is to highlight the influence of both continuum and discrete approaches and related constitutive laws on the responses of the bridge. Thus, continuum damage laws and a discrete model consisting of unilateral contact-friction interfaces were developed. Different load cases were tested and a comparison between the results obtained from the different approaches was considered. The failure mechanisms and the ultimate strengths were derived, and core points of the models were highlighted. The output of this work shows how the different failure models predict the behavior of the masonry arches. It also shows that the three-hinge mechanism, which has been depicted in classical studies for single-span arch masonry bridges under a horizontal settlement of supports, may also be obtained for multiarch bridges. Similarly, downward, vertical settlement of supports may result in the development of two hinges, as in single-span arches. Finally, the beneficial influence of the backfill in limiting the failure in the arch is addressed.