SmartWood: field-based analysis of large wood movement dynamics using inertial measurement units (IMUs)

Abstract

Abstract Wood plays an important ecological role in rivers. Yet challenges arise when large wood (LW) is mobilised and transported during floods. Due to a lack of quantitative data, movement behaviour of LW during floods is still not well understood to date. A proof-of-concept study was conducted at three Swiss rivers to test state-of-the-art sensor-tagged logs, so-called “SmartWood” and collect quantitative field-scale data about LW movement behaviour. The experiments utilised innovative inertial measurement units (IMUs), which have been developed at the Laboratory of Hydraulics, Hydrology and Glaciology (VAW) at ETH Zurich and implanted into wood logs (SmartWood) at prototype scale. Each IMU comprised three individual sensors (gyroscope, accelerometer, and magnetometer) and was equipped with an on-board processor, an AA battery (4.35 V), a memory (8 MB), and a Wi-Fi transmitter (100 m) for data transfer. After successful initial verification tests of the sensors, the IMUs were installed into debranched wood logs, measuring 4.35 m in length and 0.33 m in diameter. At the time of the field experiments, each SmartWood-log weighted between 170 and 220 kg, yielding a density of roughly 500 kg∙m−3. At the Limmat, Thur, and Grosse Melchaa Rivers in Switzerland, innovative yet discontinuous data were obtained. Results revealed consistent movement dynamics across all field sites. Specifically, we observed positive yaw movement during transport of SmartWood along the left river bank and negative yaw movement along the right river bank. Furthermore, interactions of SmartWood with channel boundaries, riparian vegetation, and objects (e.g., ferry dock) were registered and quantified, even when the SmartWood-log was transported out of sight of traditional sensing methods. The conducted field experiments enabled the initial testing of SmartWood in the field and exposed critical limitations of the IMUs and software algorithms for the reconstruction and analysis of floating LW dynamics. The gained knowledge and introduced sensing method will benefit the quantitative assessment of LW dynamics in rivers to maintain safety and functionality for instream structures (e.g., considering LW movement dynamics for the robust design of LW retention and guiding structures), but also river restoration projects and numerical models that rely on quantitative field-scale data.

Keywords

• Large wood (LW)
• LW movement dynamics
• SmartWood
• Inertial measurement unit (IMU)
• Orientation estimates