Author
Jelena Ninić
Other affiliations: Ruhr University Bochum
Bio: Jelena Ninić is an academic researcher from University of Nottingham. The author has contributed to research in topics: Building information modeling & Surrogate model. The author has an hindex of 10, co-authored 29 publications receiving 296 citations. Previous affiliations of Jelena Ninić include Ruhr University Bochum.
Papers
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TL;DR: This paper aims to present the state-of-the-art development and future trends of BIM, machine learning, computer vision and their related technologies in facilitating the digital transition of tunnelling and underground construction.
92 citations
TL;DR: The surrogate model is designed to be used in real-time to predict interval fields of the surface settlements in each stage of the advancement of the tunnel boring machine for selected realisations of the steering parameters to support the steering decisions of the machine driver.
59 citations
TL;DR: The proposed technique combines the capacity of a process-oriented 3D simulation model for mechanized tunnelling with the computational efficiency of surrogate (or meta) models based on artificial neural networks to accurately describe the complex geological and mechanical interactions of the Tunnelling process.
46 citations
TL;DR: A method for the simulation supported steering of the mechanized tunneling process in real time during construction is proposed and the performance of the proposed simulation-based model update and computational steering procedure is shown.
45 citations
TL;DR: In this paper, a coupled 3D Finite Element model of the tunnel advancement process including the ring-wise installation of the lining and the hardening process of the grouting material is used to analyze the actual spatiotemporal evolution of the loading on the segmental lining during tunnel construction.
39 citations
Cited by
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09 Mar 2012
TL;DR: Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems as mentioned in this paper, and they have been widely used in computer vision applications.
Abstract: Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.
2,069 citations
TL;DR: This study presented the state of practice of DL in geotechnical engineering, and depicted the statistical trend of the published papers, as well as describing four major algorithms, including feedforward neural, recurrent neural network, convolutional neural network and generative adversarial network.
Abstract: With the advent of big data era, deep learning (DL) has become an essential research subject in the field of artificial intelligence (AI). DL algorithms are characterized with powerful feature learning and expression capabilities compared with the traditional machine learning (ML) methods, which attracts worldwide researchers from different fields to its increasingly wide applications. Furthermore, in the field of geochnical engineering, DL has been widely adopted in various research topics, a comprehensive review summarizing its application is desirable. Consequently, this study presented the state of practice of DL in geotechnical engineering, and depicted the statistical trend of the published papers. Four major algorithms, including feedforward neural (FNN), recurrent neural network (RNN), convolutional neural network (CNN) and generative adversarial network (GAN) along with their geotechnical applications were elaborated. In addition, a thorough summary containing pubilished literatures, the corresponding reference cases, the adopted DL algorithms as well as the related geotechnical topics was compiled. Furthermore, the challenges and perspectives of future development of DL in geotechnical engineering were presented and discussed.
194 citations
TL;DR: This is a foundational study that formalises and categorises the existing usage of AR and VR in the construction industry and provides a roadmap to guide future research efforts.
182 citations
TL;DR: The feasibility of RNNs for the real-time prediction of TBM operating parameters indicates thatRNNs can afford the analysis and the forecasting of the time-continuous in-situ data collected from various construction equipments.
146 citations
TL;DR: The results indicate that the predicted settlements show great agreement with the measured settlements and the consistency of the face pressure and grout filling values calculated by the PSO-RF and the grid search method demonstrates the feasibility and applicability of proposed hybrid algorithm.
122 citations