Bolt Anchorage Defect Identification Based on Ultrasonic Guided Wave and Deep Learning.

Hui Xing, Weiguo Di, Xiaoyun Sun, Mingming Wang, Chaobo Li

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

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

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Hui XingSchool of Electrical and Electronic Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China.

Weiguo DiSchool of Electrical and Electronic Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China.

Xiaoyun SunSchool of Electronic and Control Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China.

Mingming WangSchool of Electrical and Electronic Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China.

Chaobo LiHebei Academy of Emergency Management Sciences, Shijiazhuang 050031, China.

Funding

Hebei Natural Science Foundation F2019210243

6 · The paper itself

Abstract

As a critical supporting component in geotechnical engineering structures such as bridges, tunnels, and highways, the anchorage quality of bolts directly impacts their structural safety. The ultrasonic guided wave method is a popular method for the non-destructive testing of anchorage quality. However, noise from complex field environments, modal mixing caused by anchoring interface reflections, and dispersion effects make it challenging to directly extract defect features from guided wave signals in the time or frequency domains. To address these challenges, this study proposes a solution based on the combination of the guided wave time-frequency spectrum and the gated attention residual network (GA-ResNet). The GA-ResNet introduces a gating mechanism to balance spatial attention and channel attention, and it is used for anchoring model type recognition. Experiments were conducted on four types of anchorage models, and the time-frequency spectrum was selected to be the input feature. The results demonstrate that the GA-ResNet can effectively predict the anchorage bolt defect type and prevent potential safety accidents.

Indexed as

anchorage recognitioncontinuous wavelet transformdeep learningnon-destructive testingultrasonic guided wave

Identifiers

PMID41157485

DOI10.3390/s25206431

PMCPMC12567848

What OpenQuestion holds

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LicenceCC BY

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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.