Whole Life Cycle Monitoring of Landslide Deep Deformation via Triboelectric Nanogenerator

Abstract The displacement monitoring of the slow landslide deep deformation is an important part of the landslide early warning system. However, the traditional energy supply mode of depth sensors limits the large‐scale landslide monitoring of the whole life cycle. Therefore, it is necessary to realize self‐powered monitoring for deep deformation at extremely low speed. Herein, an extremely low‐speed triboelectric tilt sensor (ELS‐TTS) is proposed for whole life cycle monitoring of landslide deep deformation without an energy supply. The triboelectric self‐sensing unit based on the triboelectric nanogenerator (TENG) and the instantaneous structure of gravity energy storage is designed. A prototype of ELS‐TTS with a resolution of 0.075° is fabricated and experimented. ELS‐TTS can stably monitor extremely low speeds of below 0.0257° s −1 in different environments and motion states, and the monitoring error is less than 0.13°. Furthermore, a real‐time monitoring system for deep landslides based on a multi‐level forewarning method is proposed to realize the whole life cycle monitoring of landslides and the multi‐level forewarning of dangerous states. This work further promotes the development of TENG in the fields of natural disaster prevention and structural health monitoring.

Authors

Institutions

Publication Details

Journal
Advanced Functional Materials
Published
2024-11-27
DOI
https://doi.org/10.1002/adfm.202413186
Citations
4
Primary Topic
Advanced Sensor and Energy Harvesting Materials
Type
article
Field-Weighted Citation Impact
0.74

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Whole Life Cycle Monitoring of Landslide Deep Deformation via Triboelectric Nanogenerator

Xiaojun Cheng, Tinghai Cheng, Pengfei He, Yang Yu et al.
4 citations
Advanced Functional Materials
Advanced Sensor and Energy Harvesting Materials
0.74
article

Whole Life Cycle Monitoring of Landslide Deep Deformation via Triboelectric Nanogenerator

Xiaojun Cheng, Tinghai Cheng, Pengfei He, Yang Yu, Shiming Liu, Hengyu Li, Xiaosong Zhang, X. J. Bi
article en
4 citations

Abstract

Abstract The displacement monitoring of the slow landslide deep deformation is an important part of the landslide early warning system. However, the traditional energy supply mode of depth sensors limits the large‐scale landslide monitoring of the whole life cycle. Therefore, it is necessary to realize self‐powered monitoring for deep deformation at extremely low speed. Herein, an extremely low‐speed triboelectric tilt sensor (ELS‐TTS) is proposed for whole life cycle monitoring of landslide deep deformation without an energy supply. The triboelectric self‐sensing unit based on the triboelectric nanogenerator (TENG) and the instantaneous structure of gravity energy storage is designed. A prototype of ELS‐TTS with a resolution of 0.075° is fabricated and experimented. ELS‐TTS can stably monitor extremely low speeds of below 0.0257° s −1 in different environments and motion states, and the monitoring error is less than 0.13°. Furthermore, a real‐time monitoring system for deep landslides based on a multi‐level forewarning method is proposed to realize the whole life cycle monitoring of landslides and the multi‐level forewarning of dangerous states. This work further promotes the development of TENG in the fields of natural disaster prevention and structural health monitoring.

Advanced Functional MaterialsVol. 35(2)
Chinese Academy of Sciences (CN), Guangzhou Institute of Energy Conversion (CN), Beijing Institute of Nanoenergy and Nanosystems (CN), University of Chinese Academy of Sciences (CN), Shenyang Jianzhu University (CN)
Natural Science Foundation of Beijing Municipality
Responsible consumption and production
Openalex Percentile: Top 31%
Advanced Sensor and Energy Harvesting Materials
0.74
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.