Evidence map›Paper›PMID 42739978›Full record

ArticleSensors (Basel, Switzerland)2026

Structural Damage Assessment and Resilience Evolution Prediction of Immersed Tunnels During Sand Foundation Loss Using In Situ Sensing Data.

Weili Chen, Zequan Yu, Zhen Feng, Yadong Li, Baoping Chen

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Weili ChenGuangzhou Road Affairs Center, Guangzhou 510635, China.
Zequan YuGuangdong Jianke Traffic Engineering Quality Inspection Center Co., Ltd., Guangzhou 510500, China.
Zhen FengGuangzhou Road Affairs Center, Guangzhou 510635, China.
Yadong LiSchool of Civil Engineering, Guangzhou University, Guangzhou 510006, China.
Baoping ChenSchool of Architecture, Kashi University, Kashi 844000, China.

Funding

Guangdong Basic and Applied Basic Research Foundation Project 2025A1515011857
6 · The paper itself

Abstract

The loss of sand foundation often induces differential settlement in immersed tunnel segments, potentially causing structural damage and reducing structural resilience. Accurately assessing the damage characteristics and their effects on resilience during sand foundation loss is essential for ensuring tunnel safety. This study adopts a typical immersed tunnel project as a case study. Long-term structural deformation data acquired by distributed optical fiber sensing technology and sand foundation detection data are adopted to analyze the response characteristics and damage state of the tunnel. A refined three-dimensional tunnel-stratum interaction model is established and validated against monitoring data to investigate mechanical response characteristics, including deformation and bending moment distributions. A redundancy factor is proposed as a quantitative index for tunnel resilience under foundation loss, and a multi-level resilience grading framework is established accordingly. Furthermore, the evolution of tunnel resilience under various displacement recovery ratios, which represent the extent of differential settlement remediation, is investigated using the refined numerical model. Field detection results show that over 50% of the foundation area is affected by loosening or voids. These defects are highly consistent with regions of abnormal structural deformation, leading to a bending-torsional deformation mode, with a maximum joint differential settlement of 106.7 mm. Stress concentration occurs in the tunnel floor above denser sand zones, with a maximum crack width of 0.43 mm. The tunnel is classified as severely damaged (low resilience) based on the proposed standard, with a redundancy factor of 1.59. Bending-torsional deformation and stress concentration are gradually mitigated as the displacement recovery ratio increases. The redundancy factor exhibits a parabolic relationship with the recovery ratio, indicating that tunnel resilience can be restored to a relatively high level when the displacement recovery ratio exceeds 70%. The proposed redundancy factor and grading framework provide quantitative guidance for designing and optimizing resilience improvement strategies following sand foundation loss.

Indexed as

damage assessmentimmersed tunnelsin situ sensingnumerical simulationsresilience evolutionsand foundation loss

Identifiers

PMID42739978
PMCPMC13568278

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.