The impact of climate change on tree stability is often associated with a higher risk of windthrow due to higher frequency and greater magnitude of extreme climatic conditions. Higher lateral loads due to an increase in maximum wind and rainfall reduce tree anchorage because of a decrease in soil matric suction and consequently the overall strength in the system of trunk, root, and soil. This study compared the mechanical response of trees with different root architectures using static loading tests conducted in the field and numerical analysis of laser-scanned root systems. For this case, mature trees of Khaya senegalensis (Desr.) A. Juss., Samanea saman (Jacq.) Merr., and Syzygium grande (Wight) Wight ex Walp. were tested and analyzed. The root system models consisted of root system architectures obtained using 3-D laser scanning. A parametric analysis was conducted by varying the modulus of elasticity of the soil (Es) from 2.5 to 25 MPa, and the results were compared with those of the static loading tests to obtain the overall mechanical responses of the root–soil systems. The results showed important dependencies of the mechanical responses of the root–soil system on the root architecture in withstanding the lateral load. The numerical models also allowed estimation of the effective leeward and windward anchorage zones with different soil elastic moduli and rooting architectures to define the extent of the tree root protection zones.

Climatic variations induce negative pore-water pressure or soil suction oscillations in unsaturated soil with daily and seasonal cycles. The dramatic suction changes of unsaturated soil in shallow depths can lead to engineering failures. However, suction measurements using conventional water-based tensiometers are limited to 98-kPa suction that is due to water cavitation. Therefore, they are not able to capture variations of high suction values near the ground surface. A newly proposed osmotic tensiometer was developed using a cross-linked polymer-water solution to allow the high suction measurement range and provide a reliable and consistent suction response under long-term field monitoring. The unique swelling behavior of the polymer-water solution extends the suction measurement capacity to suction values above 1.5 MPa. Laboratory verification and calibration were carried out with centrifuge (covers the lower range of suction measurement from 0.66 to 250 kPa) and WP4C potentiometer (covers the higher range of suction measurement beyond 250 kPa). The proposed osmotic tensiometer can measure rapid suction changes during drying and wetting paths. Long-term laboratory monitoring using the submerged osmotic tensiometer in a deionized water reservoir demonstrated reliable and consistent suction measurements. Osmotic pressure decay was observed after a long testing period because of the leakage of polymer particles through the high air-entry ceramic disc. A series of parametric studies was performed to explore the optimal tensiometer configuration to minimize the pressure decay. Polymer adsorption measurements from ultraviolet-visible spectrometry were used to quantify the polymer leakage. The newly proposed osmotic tensiometer is expected to capture field suction changes that are due to rainfall precipitation (wetting) and evaporation (drying) and provide long-term, accurate, and consistent suction measurements for geotechnical engineering applications.

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In unsaturated soil mechanics, many attempts have been made to estimate the SWCC based on soil texture and grain-size distribution. This paper proposes a simplified method to estimate the soil-water characteristic curve (SWCC) for both coarse and fine-grained soils using SWCC data and machine learning computer code in the Aburra Valley. Fredlund and Xing parameters has been used to estimate the SWCC correlations. Soil samples collected from field survey were subjected to laboratory testing, SWCCs were estimated using filter paper method. Each SWCC data set from Aburra Valley was fitted with Fredlund and Xing curve using multiple regression analysis, correlations were derived for those four parameters based on predictors derived from machine learning. The proposed method gives a good estimation and low residual errors of the SWCC.