Evaluation of time response of GMS for soil suction measurement

Vettorello, Danilo L.; Marinho, Fernando A. M.

doi:10.1051/matecconf/202133701014

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…[36][37][38] Thus, many efforts have been made to improve the measurement methods, aiming to reduce cost, 39 shorten measurement time, and reliably measure a large range of matric suction. [40][41][42] The high-capacity tensiometer stands out as an invaluable tool capable of directly and reliably measuring matric suction. 10,43,44 Remarkably, it facilitates matric suction measurement across a broad range, with the latest advancements enabling the measurement of matric suction up to 7 MPa.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the measurement of matric suction is typically hindered by limitations that include costly equipment, time‐consuming measurement, and sophisticated operations requiring highly skilled professionals 36–38 . Thus, many efforts have been made to improve the measurement methods, aiming to reduce cost, 39 shorten measurement time, and reliably measure a large range of matric suction 40–42 . The high‐capacity tensiometer stands out as an invaluable tool capable of directly and reliably measuring matric suction 10,43,44 .…”

Section: Introductionmentioning

confidence: 99%

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A framework for estimating the matric suction in unsaturated soils using multiple artificial intelligence techniques

Wang,

Vanapalli

2024

Num Anal Meth Geomechanics

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Implementation of the state‐of‐the‐art understanding of the mechanics of unsaturated soils into geotechnical engineering practice is partly limited due to the lack of quick, reliable, and economical techniques for matric suction measurement. Matric suction is one of the key stress state variables that significantly influences the hydro‐mechanical behavior of unsaturated soils. In this paper, to address this objective, two artificial intelligence (AI) models were developed for estimating matric suction in unsaturated soils based on the particle swarm optimization support vector regression (PSO‐SVR) and multivariate adaptive regression spline (MARS) algorithms. The results suggest that both these models can reasonably estimate matric suction. Compared to the MARS model, the PSO‐SVR model can achieve higher accuracy. Nonetheless, the MARS model facilitates the sensitivity analysis and the selection of essential inputs. A novel integrated framework is proposed and validated, leveraging the strengths, and alleviating the limitations of the PSO‐SVR and MARS algorithms for reliable and rapid estimation of matric suction in the range of 0–1500 kPa for low plastic soils (0 < Ip ≤ 7). Six inputs are required to use this model successfully; some can be measured using conventional laboratory tests, and others can be calculated from mass‐volume relationships.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A framework for estimating the matric suction in unsaturated soils using multiple artificial intelligence techniques

Wang,

Vanapalli

2024

Num Anal Meth Geomechanics

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Identification of Soil Water Potential Sensors Readings in an Irrigation Control System Using Internet-of-Things (IoT): Automatic Tensiometer and Watermark 200SS

Kovalchuk,

Voitovich,

Kovalchuk

et al. 2023

Lecture Notes on Data Engineering and Communications Technologies

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Field Performance Evaluation of Low-Cost Soil Moisture Sensors in Irrigated Orchard

Marković,

Matoša Kočar,

Barač

et al. 2024

Agriculture

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Measuring the soil water content (SWC) is a fundamental component of the sustainable management of water resources, soil preservation, and high irrigation efficiency. Non-destructive SWC measurements using soil moisture sensors (SMSs) enables timely irrigation and reduces overirrigation and water stress. Within this context, the performance of four commercial single-point soil moisture sensors (Watermark and tensiometer (Irrometer Company, Inc., Riverside, CA, USA), SM150 (Delta-T Devices, Cambridge, UK)), FieldScout TDR300 (Spectrum Technologies, Aurora, IL, USA) and one soil profile PR2 probe (Delta-T Devices, Cambridge, UK) were tested under anthropogenic eutric cambisol with a silty clay loamy texture (20, 30, and 40 cm) to evaluate accuracy and sensitivity to changes in the SWC in an irrigated apple orchard. The Watermark and tensiometer were additionally tested in the laboratory to convert soil water tension (kPa) to the volumetric soil water content (%vol.). In general, all tested SMSs responded to changes in the SWC, with sensor-to-sensor differences. The Watermark and tensiometer underestimated the SWC, while the TDR overestimated the SWC. The SM150 and PR2 showed high accuracy, i.e., SM150—RMSE-2.24 (20 cm), 2.18 (30 cm) and 2.34 (40 cm), MSE—5.02 (20 cm), 2.93 (30 cm) and 1.89 (40 cm), and PR2—RMSE-1.8 (20 cm), 1.3 (30 cm) and 1.55 (40 cm), MSE-3.23 (20 cm), 1.7 (30 cm) and 2.39 (40 cm) at all observed soil depths.

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Evaluation of time response of GMS for soil suction measurement

Cited by 3 publications

References 16 publications

A framework for estimating the matric suction in unsaturated soils using multiple artificial intelligence techniques

A framework for estimating the matric suction in unsaturated soils using multiple artificial intelligence techniques

Identification of Soil Water Potential Sensors Readings in an Irrigation Control System Using Internet-of-Things (IoT): Automatic Tensiometer and Watermark 200SS

Field Performance Evaluation of Low-Cost Soil Moisture Sensors in Irrigated Orchard

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