Slope movement detection sensor based on acceleration using multi-point fixed inclinometer
Journal
AIP Conference Proceedings
PROCEEDINGS OF THE 2025 9TH INTERNATIONAL CONFERENCE ON STRUCTURE AND CIVIL ENGINEERING RESEARCH: Selected and Revised Papers from ICSCER2025
ISSN
0094-243X
Date Issued
2025-11-07
Author(s)
Aina Syahirah Ahmad Ishak
Ahmad Loqman Ahmad Marzuki
DOI
https://doi.org/10.1063/5.0300726
Abstract
A slope movement detection sensor is a proactive measure to monitor slope failure or landslide. Slope instability significantly contributes to hazards affecting lives and properties. Numerous instruments have been developed to monitor slope movement at the surface or deeper ground. In this study, the application of a slope movement detection sensor, namely the Multi-Point Fixed Inclinometer (MPFI), was illustrated to detect slope movement below the ground surface. The MPFI employs the MPU-9250, a Micro-Electromechanical Systems (MEMS) sensor controlled by an Arduino Mega model 2560 RE, which can detect the precursor movement of the slope prior to failure. The sensor components are enclosed with segmented casing and connected via a flexible wire. Owing to its flexibility, the connection exhibits unrestricted slope movement in all axis directions, rendering it more sensitive compared to a rigid Poly Vinyl Chloride (PVC), metal pipe or concrete encasement. Two units of prototype sensors were assessed on a small-scale slope model made of silica sand with a slope angle of 34°. The slope model is subjected to incremental surcharge load to induce slope failure, and the readings of slope movement are captured by the MPFI. Failure data depicts changes in acceleration behavior from stable to instantaneous spike when there is minute slope movement. The landslide threshold values for the triggering state were interpreted from a series of laboratory experiments when the z, x and y acceleration values were 0.1, 0.1 and 0.01 m/s2, respectively. The MPFI will be further developed for long-term monitoring with the application of the Internet-of-Thing (IoT) and early warning system.
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