Real-Time Risk Assessment of Tunneling-Induced Building Damage Considering Polymorphic Uncertainty

Cao, Ba Trung; Obel, Markus; Freitag, Steffen; Heußner, Lukas; Meschke, Günther; Mark, Peter

doi:10.1061/ajrua6.0001192

Cited by 11 publications

(2 citation statements)

References 48 publications

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“…Further applications of RNNs are full waveform inversion [188][189][190], high-dimensional chaotic systems [191], fluid flow [40,192], fracture propagation [116], sensor signals in non-linear dynamic systems [193,194], and settlement field predictions induced by tunneling [195], which was extended to damage prediction in affected structures [196,197]. RNNs are often combined with reduced order model encodings [198], where the dynamics are predicted on the reduced latent space, as demonstrated in [199][200][201][202][203][204][205].…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

Deep learning in computational mechanics: a review

Herrmann,

Kollmannsberger

2024

Comput Mech

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The rapid growth of deep learning research, including within the field of computational mechanics, has resulted in an extensive and diverse body of literature. To help researchers identify key concepts and promising methodologies within this field, we provide an overview of deep learning in deterministic computational mechanics. Five main categories are identified and explored: simulation substitution, simulation enhancement, discretizations as neural networks, generative approaches, and deep reinforcement learning. This review focuses on deep learning methods rather than applications for computational mechanics, thereby enabling researchers to explore this field more effectively. As such, the review is not necessarily aimed at researchers with extensive knowledge of deep learning—instead, the primary audience is researchers on the verge of entering this field or those attempting to gain an overview of deep learning in computational mechanics. The discussed concepts are, therefore, explained as simple as possible.

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Section: Recurrent Neural Networkmentioning

confidence: 99%

Deep learning in computational mechanics: a review

Herrmann,

Kollmannsberger

2024

Comput Mech

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“…With the help of computer simulation, the immersion process is calculated and the situation of sudden shell failures is simulated. For modeling and calculations, the finite method (FEM) and programs for design and geotechnical calculations [9,10]. Fragments of the calculation of non-stationary immersion of a large-sized massive reinforced concrete shell (with a diameter and depth of more than 70 m, with a wall thickness of 2.5 m and the number of volumetric finite elements up to 364 thousand elements), falling under its own weight at an angle of 12 ° -24 ° from an estimated height of up to 280 cm height onto pliable soil of different strength soil (from E= 4 MPa to E= 19 MPa).…”

Section: Protection Of An Underground Pumping Station From Sudden Imm...mentioning

confidence: 99%

Restoration of underground water-carrying structures damaged by external non-stationary impacts

Perminov

2023

E3S Web Conf.

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Object and purpose of the study: Underground aquiferous point and linear structures operated in difficult ground conditions lose their structural safety as a result of the influence of external forces and the environment. It is important to establish the features of the operation of underground aquifers and communications in an inhomogeneous enclosing soil massif, taking into account the interaction of structures with the ground in case of non-stationary force impacts and environmental impacts with an assessment of their structural safety. Methods: The use of analytical and experimental research methods to evaluate the work of structures in the soil mass, taking into account force influences and environmental influences. Results: The features of interaction of the housing of a collapsible pipe with a soil mass with a multidirectional horizontal component of a non-stationary load are established. The negative impact of the combination of the multidirectional impact of the power load and internal hydrostatic pressure on the structural safety of the housing of a prefabricated underground structure is shown. Practical significance and conclusions: The results obtained on the picture of the stress-strain state of an inhomogeneous enclosing soil massif allowed us to establish the nature and boundaries of the safe operation of structures and to make suggestions on the choice of ways to strengthen and protect underground structures, taking into account their operating conditions.

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Real-Time Simulation for Steering the Tunnel Construction Process

Cao

Heußner

Jodehl

et al. 2023

Interaction Modeling in Mechanized Tunneling

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Currently, in mechanized tunneling, the steering of tunnel boring machines (TBM) in practice is mainly decided based on engineering expert knowledge and recorded monitoring data. In this chapter, a new concept of exploiting the advantages of simulation models to support the steering phase is presented, which allows optimizing the construction process. With the aim to support the steering decision during tunnel construction by means of real-time simulations, predictive simulation models are established in the initial planning phase of a tunnel project. The models are then capable of being continuously updated with monitoring data during the construction. The chapter focuses on explaining models for real-time predictions of logistics processes and tunneling induced settlements as well as the risk of building damage in more details. Additionally, application examples, which are practical-oriented, are also presented to illustrate the applicability of the proposed concept.

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Real-Time Risk Assessment of Tunneling-Induced Building Damage Considering Polymorphic Uncertainty

Cited by 11 publications

References 48 publications

Deep learning in computational mechanics: a review

Deep learning in computational mechanics: a review

Restoration of underground water-carrying structures damaged by external non-stationary impacts

Real-Time Simulation for Steering the Tunnel Construction Process

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