Evidence map›Paper›PMID 40732532›Full record

ReviewSensors (Basel, Switzerland)2025

Termite Detection Techniques in Embankment Maintenance: Methods and Trends.

Xiaoke Li, Xiaofei Zhang, Shengwen Dong, Ansheng Li, Liqing Wang, Wuyi Ming

Abstract readReview
In one paragraph

Review 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

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

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

6 authors.

Xiaoke LiHenan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, China.ORCID 0000-0002-2657-1149
Xiaofei ZhangHenan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, China.
Shengwen DongKey Laboratory of Termite Control of Ministry of Water Resources, Hubei Water Resources Research Institute, Wuhan 430070, China.
Ansheng LiHydropower Equipment and Intelligent System Engineering Technology Research Centre of Henan Province, Department of Mechanical and Electronic Engineering, Henan Vocational College of Water Conservancy and Environment, Zhengzhou 450002, China.
Liqing WangHydropower Equipment and Intelligent System Engineering Technology Research Centre of Henan Province, Department of Mechanical and Electronic Engineering, Henan Vocational College of Water Conservancy and Environment, Zhengzhou 450002, China.
Wuyi MingGuangdong Provincial Key Laboratory of Digital Manufacturing Equipment, Huazhong University of Science and Technology, Dongguan 523808, China.

Funding

Key Scientific Research Projects in Higher Education Institutions in Henan Province No. 24A120008Scientific and Technological Research Project of Henan Province No. 242102220092
6 · The paper itself

Abstract

Termites pose significant threats to the structural integrity of embankments due to their nesting and tunneling behavior, which leads to internal voids, water leakage, or even dam failure. This review systematically classifies and evaluates current termite detection techniques in the context of embankment maintenance, focusing on physical sensing technologies and biological characteristic-based methods. Physical sensing methods enable non-invasive localization of subsurface anomalies, including ground-penetrating radar, acoustic detection, and electrical resistivity imaging. Biological characteristic-based methods, such as electronic noses, sniffer dogs, visual inspection, intelligent monitoring, and UAV-based image analysis, are capable of detecting volatile compounds and surface activity signs associated with termites. The review summarizes key principles, application scenarios, advantages, and limitations of each technique. It also highlights integrated multi-sensor frameworks and artificial intelligence algorithms as emerging solutions to enhance detection accuracy, adaptability, and automation. The findings suggest that future termite detection in embankments will rely on interdisciplinary integration and intelligent monitoring systems to support early warning, rapid response, and long-term structural resilience. This work provides a scientific foundation and practical reference for advancing termite management and embankment safety strategies.

Indexed as

IsopteraAlgorithmsAnimalsArtificial IntelligenceElectronic NoseRadarbiological characteristicsdetectionembankmentphysical sensingtermites

Identifiers

PMID40732532
PMCPMC12298327

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.