A probabilistic framework for the resilience assessment of transport infrastructure systems via structural health monitoring and control based on a cost function approach

Domaneschi, Marco; Cucuzza, Raffaele; Martinelli, Luca; Noori, Mohammad; Marano, Giuseppe Carlo

doi:10.1080/15732479.2024.2318231

Cited by 17 publications

(2 citation statements)

References 43 publications

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“…Therefore, the health monitoring of tunnel structures using fiber optic methods, non-contact methods such as digital images, and acoustic emission methods are equally important [119,120]. In addition, effective methods for the long-term monitoring and assessment of bridges and buildings [121][122][123][124][125] can also be applied during tunnel operation to reduce the incidence of disasters during the tunnel life cycle and to safeguard the safety of personnel and equipment.…”

Section: Discussionmentioning

confidence: 99%

Review on Vibration Monitoring and Its Application during Shield Tunnel Construction Period

Yang,

Fang,

Wang

et al. 2024

Buildings

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With the rapid development of metro construction, shield machines inevitably have to traverse a variety of complex geological conditions, leading to the frequent occurrence of geological disasters, equipment failures, building vibration and other problems. Vibration, as an important feature of the shield tunneling process, has received more and more attention in recent years. This paper summarizes the relevant research progress of vibration monitoring during shield construction from 2015 to 2023. It analyzes the shield vibration generation mechanism, monitoring methods and application areas. Firstly, the shield vibration type is divided into mechanical vibration triggered by internal excitation and forced vibration triggered by external excitation, and the principles of vibration generated by shield main bearing, gearbox and disc cutter are discussed. Then, the commonly used vibration monitoring methods are outlined according to the installation location of the sensors (inside and outside of the shield). Finally, the applications of vibration signals in the diagnosis of shield faults, the identification of geologic conditions, and the evaluation of the current status of the interference with the buildings are summarized. This paper discusses the development trend of vibration monitoring during shield tunneling based on the current research situation and the current technology level, which provides valuable insights to enhance the safety and intelligence of shield construction.

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Section: Discussionmentioning

confidence: 99%

Review on Vibration Monitoring and Its Application during Shield Tunnel Construction Period

Yang,

Fang,

Wang

et al. 2024

Buildings

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“…The reason for this is that when leakage occurs in underground structures such as tunnels, the surrounding soil continues rushing into tunnels due to seepage forces, triggering catastrophic consequences such as ground subsidence. Recent research indicates the potential of deep learning techniques in identifying damages within tunnel structures [8] and enhancing the resilience of underground structures by structural health monitoring in this field [9,10]. However, to prevent the hazards, an investigation into the mechanism is also necessary.…”

Section: Introductionmentioning

confidence: 99%

Model Test and Numerical Simulation for Tunnel Leakage-Induced Seepage Erosion in Different Strata

Sun,

De Corte,

Liu

et al. 2024

Applied Sciences

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Leakage in underground structures, especially tunnels, may cause seepage erosion in the surrounding soil, which in turn leads to ground subsidence, posing a great threat to urban safety. The current literature mainly focuses on seepage erosion in the sand but lacks a systematic study on the development process of seepage erosion induced by tunnel leakage in different strata. To investigate the different seepage erosion modes induced by tunnel leakage in different stratum types, a series of reduced-scale model tests were carried out. A coupled fluid–solid numerical model was further established to analyze the fine-scale characteristics of different seepage erosion modes. The results show that (1) the soil seepage erosion modes can be divided into three categories: no soil cave, unstable soil cave, and stable soil cave; (2) the adopted coupled fluid–solid numerical model based on DEM, which takes into account the degradation of clay during seepage erosion, can effectively simulate the erosion process of soil with different seepage erosion modes; (3) the phenomena of the three erosion modes are different in the process of erosion development; and (4) the micro-mechanisms of the three seepage erosion modes are different, which are manifested in the erosion range, soil arching effect, and displacement.

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GRNN-based cascade ensemble model for non-destructive damage state identification: small data approach

Izonin,

Kazantzi,

Tkachenko

et al. 2024

Engineering with Computers

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Assessing the structural integrity of ageing structures that are affected by climate-induced stressors, challenges traditional engineering methods. The reason is that structural degradation often initiates and advances without any notable warning until visible severe damage or catastrophic failures occur. An example of this, is the conventional inspection methods for prestressed concrete bridges which fail to interpret large permanent deflections because the causes—typically tendon loss—are barely visible or measurable. In many occasions, traditional inspections fail to discern these latent defects and damage, leading to the need for expensive continuous structural health monitoring towards informed assessments to enable appropriate structural interventions. This is a capability gap that has led to fatalities and extensive losses because the operators have very little time to react. This study addresses this gap by proposing a novel machine learning approach to inform a rapid non-destructive assessment of bridge damage states based on measurable structural deflections. First, a comprehensive training dataset is assembled by simulating various plausible bridge damage scenarios associated with different degrees and patterns of tendon losses, the integrity of which is vital for the health of bridge decks. Second, a novel General Regression Neural Network (GRNN)-based cascade ensemble model, tailored for predicting three interdependent output attributes using limited datasets, is developed. The proposed cascade model is optimised by utilising the differential evolution method. Modelling and validation were conducted for a real long-span bridge. The results confirm the efficacy of the proposed model in accurately identifying bridge damage states when compared to existing methods. The model developed demonstrates exceptional prediction accuracy and reliability, underscoring its practical value in non-destructive bridge damage assessment, which can facilitate effective restoration planning.

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A probabilistic framework for the resilience assessment of transport infrastructure systems via structural health monitoring and control based on a cost function approach

Cited by 17 publications

References 43 publications

Review on Vibration Monitoring and Its Application during Shield Tunnel Construction Period

Review on Vibration Monitoring and Its Application during Shield Tunnel Construction Period

Model Test and Numerical Simulation for Tunnel Leakage-Induced Seepage Erosion in Different Strata

GRNN-based cascade ensemble model for non-destructive damage state identification: small data approach

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