Research on a Hybrid Self-Powered Landslide Rainfall Sensor Based on Triboelectric Nanogenerators and Electromagnetic Generators DOI Creative Commons
Hao Zou, Zhi Zuo,

Xue Hong Shen

и другие.

Micromachines, Год журнала: 2025, Номер 16(6), С. 678 - 678

Опубликована: Июнь 4, 2025

Landslide monitoring is crucial for mitigating landslide disaster risks. However, the power supply methods of existing rainfall sensors often fail to meet demands practical field applications. This study proposes a hybrid self-powered sensor monitoring, integrating triboelectric nanogenerator (TENG) and an electromagnetic generator (EMG). The TENG module used while both EMG modules are synergistically utilized generation. Experimental results demonstrate that sensor’s measurement error less than 5%, it can operate stably under conditions temperature below 90 °C humidity 90%. Furthermore, exhibits generation capabilities. When connected resistors 4.3 × 108 Ω 3.6 102 Ω, respectively, they output maximum 57.5 nW 110.25 mW, respectively. Compared conventional sensors, this self-powered, allowing normal operation without external supply, making more suitable environments prone landslides.

Язык: Английский

Uncertainty analysis of 3D post-failure behavior in landslide and reinforced slope based on the SPH method and the random field theory DOI
Dianlei Feng, Lin Gan, Min Xiong

и другие.

Engineering Geology, Год журнала: 2025, Номер unknown, С. 108017 - 108017

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

2
Investigation of morphological features and mechanical behavior of jointed limestone subjected to wet-dry cycles and cyclic shear in drawdown areas of the three Gorges Reservoir DOI
Qiang Xie,

YuCheng Chen,

Zhangrui Wu

и другие.

Engineering Geology, Год журнала: 2025, Номер 350, С. 107990 - 107990

Опубликована: Март 2, 2025

Язык: Английский

Процитировано

1
Mitigating rainfall induced soil erosion through bio-approach: From laboratory test to field trail DOI
Bo Liu, Chao‐Sheng Tang,

Xiaohua Pan

и другие.

Engineering Geology, Год журнала: 2024, Номер unknown, С. 107842 - 107842

Опубликована: Дек. 1, 2024

Язык: Английский

Процитировано

4
Landslide-reinforcement method and its application based on jet grouting to improve sliding-soil strength DOI
Bolin Chen, Haiyou Peng, Wenjun Yang

и другие.

Engineering Geology, Год журнала: 2025, Номер unknown, С. 107976 - 107976

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0
Displacement prediction and failure mechanism analysis of rainfall-induced colluvial landslides DOI
Yabo Li,

Xinli Hu,

Haiyan Zhang

и другие.

Journal of Hydrology, Год журнала: 2025, Номер unknown, С. 133361 - 133361

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
A comparison of metaheuristic optimizations with automated hyperparameter tuning methods in support vector machines algorithm for predicting soil water characteristic curve DOI

Mostafa Rastgou,

Yong He,

Ruitao Lou

и другие.

Engineering Geology, Год журнала: 2025, Номер unknown, С. 108121 - 108121

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0
Validating and enhancing data-driven landslide susceptibility prediction by model explanation and MT-InSAR techniques DOI Creative Commons

Miao Yu,

Li Chen, Xuanmei Fan

и другие.

International Journal of Digital Earth, Год журнала: 2025, Номер 18(1)

Опубликована: Май 28, 2025

Язык: Английский

Процитировано

0
Thermal conductivity of bentonite-based materials: model overview and physics constrained ensemble learning predictive framework DOI

Shengkui Tian,

Qiong Wang, Wei Su

и другие.

Nuclear Engineering and Design, Год журнала: 2025, Номер 441, С. 114139 - 114139

Опубликована: Июнь 3, 2025

Язык: Английский

Процитировано

0
Research on a Hybrid Self-Powered Landslide Rainfall Sensor Based on Triboelectric Nanogenerators and Electromagnetic Generators DOI Creative Commons
Hao Zou, Zhi Zuo,

Xue Hong Shen

и другие.

Micromachines, Год журнала: 2025, Номер 16(6), С. 678 - 678

Опубликована: Июнь 4, 2025

Landslide monitoring is crucial for mitigating landslide disaster risks. However, the power supply methods of existing rainfall sensors often fail to meet demands practical field applications. This study proposes a hybrid self-powered sensor monitoring, integrating triboelectric nanogenerator (TENG) and an electromagnetic generator (EMG). The TENG module used while both EMG modules are synergistically utilized generation. Experimental results demonstrate that sensor’s measurement error less than 5%, it can operate stably under conditions temperature below 90 °C humidity 90%. Furthermore, exhibits generation capabilities. When connected resistors 4.3 × 108 Ω 3.6 102 Ω, respectively, they output maximum 57.5 nW 110.25 mW, respectively. Compared conventional sensors, this self-powered, allowing normal operation without external supply, making more suitable environments prone landslides.

Язык: Английский

Процитировано

0