A DEM-based Generic Modeling Framework for Hydrate-Bearing Sediments

Computers and Geotechnics, Год журнала: 2024, Номер 171, С. 106287 - 106287

Опубликована: Апрель 19, 2024

Язык: Английский

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A deep learning informed-mesoscale cohesive numerical model for investigating the mechanical behavior of shield tunnels with crack damage

Structures, Год журнала: 2024, Номер 66, С. 106902 - 106902

Опубликована: Июль 13, 2024

Язык: Английский

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Towards digitalized maintenance of operating tunnels: A text documents-based defect evaluation and visualization

Tunnelling and Underground Space Technology, Год журнала: 2025, Номер 157, С. 106345 - 106345

Опубликована: Янв. 2, 2025

Язык: Английский

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In-situ and experimental investigations of the failure characteristics of surrounding rock through granites with biotite interlayers in a deep TBM tunnel

Engineering Geology, Год журнала: 2024, Номер 343, С. 107816 - 107816

Опубликована: Ноя. 13, 2024

Язык: Английский

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Designing a Distributed Sensing Network for Structural Health Monitoring of Concrete Tunnels: A Case Study

Structural Control and Health Monitoring, Год журнала: 2024, Номер 2024(1)

Опубликована: Янв. 1, 2024

Structural health monitoring is essential for the lifecycle maintenance of tunnel infrastructure. Distributed fiber‐optic sensor (DFOS) technology, which capable distributed strain measurement and long‐range sensing, an ideal nondestructive testing (NDT) approach linear infrastructures. This research aims to develop a sensing network utilizing DFOS structural integrity assessment concrete immersed tunnels. The primary innovations this study lie in development general flowchart establishing obtaining reliable field data, as well its subsequent validation through detailed case study. Concentrated joint deformations typical tunnels, detectable by DFOS, are key indicators integrity. addresses crucial elements system design, including selection appropriate optical fibers or cables determination vital interrogator parameters. It also covers parameter determination, installation techniques, data collection, postanalysis. Furthermore, exemplified that illustrates successful implementation operational tunnel, reveals cyclic under impacts daily tide seasonal temperature variations. obtained from play significant role condition assessments structures. findings contribute large‐scale infrastructure conditions application monitoring.

Язык: Английский

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Compound damage detection using wavelet transform and deep neural network trained on healthy and single damage states: Validation on a laboratory-scale offshore jacket model

Structural Health Monitoring, Год журнала: 2025, Номер unknown

Опубликована: Янв. 21, 2025

Structural health monitoring is vital for the early detection of damage, enabling effective life cycle management structures. Detecting compound where multiple types damage occur simultaneously in different sections a structure, particularly challenging, especially when some damages are subtle or minor. Existing methods typically treat as distinct category, separate from single types. This paper introduces novel approach to based solely on vibration responses, combining wavelet transform with deep convolutional neural network interference (MIDCNN). In this approach, MIDCNN trained using time-frequency data healthy and states, intentionally excluding training phase. During testing, model accurately distinguishes between healthy, untrained states output probabilities meet predefined conditions. The method validated laboratory-scale offshore jacket structure. results demonstrate method’s ability extract relevant features classify structural including single, damage.

Язык: Английский

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A deep learning-based adaptive denoising approach for fine identification of rock microcracks from noisy strain data

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 148, С. 110471 - 110471

Опубликована: Март 11, 2025

Язык: Английский

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Deep convolutional transformer network for quantifying crack width in tunnel lining structures using distributed fiber optic sensing data

Smart Materials and Structures, Год журнала: 2025, Номер 34(3), С. 035046 - 035046

Опубликована: Март 1, 2025

Abstract Distributed fiber optic sensing (DFOS) technique provides distinct advantages for crack monitoring in infrastructure by measuring strain distribution. However, deriving width from measured data is challenging due to their complex nonlinear mapping relationship. To address this issue, paper proposes a deep learning (DL)-based method quantification tunnel lining structures using DFOS. First, simplified segments were cast and subjected destructive eccentric loading tests, during which distributions collected DFOS sensors. Afterward, the sequences appropriately segmented labeled with corresponding values form sample dataset. Importantly, developed novel DL framework called convolutional transformer network (DCT-Net), capable of extracting local global sensitive features quantification. The effectiveness, noise robustness generalization ability proposed DCT-Net extensively validated. Experimental results demonstrate that approach can accurately quantify widths exhibits strong generalization. In addition, outperforms current five state-of-the-art models, particularly under noisy conditions. This study will pave way future application intelligent cracks in-situ engineering projects.

Язык: Английский

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GeoLLM: A specialized large language model framework for intelligent geotechnical design

Computers and Geotechnics, Год журнала: 2024, Номер 177, С. 106849 - 106849

Опубликована: Окт. 24, 2024

Язык: Английский

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Simulation of geological uncertainty based on improved three-dimensional coupled Markov chain model

Engineering Geology, Год журнала: 2024, Номер 340, С. 107647 - 107647

Опубликована: Июль 19, 2024

Язык: Английский

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