Modeling Excavator-Soil Interaction | SpringerLink
Abstract. This paper reviews models of how ground-engaging tools interact with soils, the rigid-body dynamics of excavating machines, and how to combine these models to estimate soil parameters or to find faults in machines from anomalous dynamic behaviour.
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Spatio-temporal dynamic of soil quality in the Central …
Request PDF | On Jul 15, 2020, Hassan Fathizad and others published Spatio-temporal dynamic of soil quality in the Central Iranian desert modeled with Machine Learning and Digital Soil Assessment ...
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Scale and uncertainty in modeled soil organic carbon
Scale and uncertainty in modeled soil organic carbon stock changes for US croplands using a process‐based model. ... PurposeIntegrating process-based models with machine learning (ML) is an ...
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Spatio-temporal dynamic of soil quality in the central Iranian …
@article{Fathizad2020SpatiotemporalDO, title={Spatio-temporal dynamic of soil quality in the central Iranian desert modeled with machine learning and digital soil assessment techniques}, author={Hassan Fathizad and Mohammad Ali Hakimzadeh Ardakani and Brandon Heung and Hamid Sodaiezadeh and Asghar Rahmani and …
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CPT-Based Soil Classification through Machine Learning …
In this paper, machine learning algorithms are used to build a model which can classify the soil using Cone Penetration Testing (CPT) data, focusing on three regions in the US (southeastern, central, and western). Random Forest, Support Vector Machine, K Nearest Neighbors, and Extreme Gradient Boosting algorithms are the four ML …
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(PDF) A Review of Machine Learning Approaches to Soil
School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada. * Correspondence: [email protected]. Abstract: Soil temperature is an essential factor ...
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Remote Sensing | Free Full-Text | Evaluation of the Effects of Soil …
To better evaluate the effects of soil layer classification on modeled diurnal LST and NSSR cycles, and more importantly, the associated SSM retrieval model of Leng et al., the soil profile has been divided into three layer zones named: upper layer (0–0.05 m), root layer (0.05–1.30 m) and bottom layer (1.30–2.50 m). The SSM is …
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Determination of bioavailable arsenic threshold and …
Therefore, the DT estimated maximum allowable total As in paddy soil of 14 mg kg −1 could confidently be used as an appropriate guideline value. We further used the purposely collected field data to predict the concentration of bioavailable As in the paddy soil with the help of random forest (RF), gradient boosting machine (GBM), and LR …
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From data to interpretable models: machine learning …
From data to interpretable models: machine learning for soil moisture forecasting. Regular Paper. Open access. Published: 31 August 2022. Volume 15, …
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Estimating Spatially Explicit Irrigation Water Use Based
And they inferred irrigation from a positive bias between the remotely sensed and the modeled soil moisture at a spatial resolution of 25 km. Zhang and Long (2021) also developed a robust ...
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Agronomy | Free Full-Text | Modeling Soil–Plant–Machine
The study of soil–plant–machine interaction (SPMI) examines the system dynamics at the interface of soil, machine, and plant materials, primarily consisting of soil–machine, soil–plant, and plant–machine interactions. A thorough understanding of the mechanisms and behaviors of SPMI systems is of paramount importance to optimal …
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Incorporating soil knowledge into …
Various machine-learning models have been extensively applied to predict soil properties using infrared spectroscopy. Beyond the interpretability and transparency of these models, there is an ongoing …
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Machine learning applications for water-induced soil …
In this study, machine learning techniques were used to predict the quantities of water-induced soil erosion rates, which is categorized as a regression problem. One of the crucial factors in soil erosion modeling is the validation of the models through field-based measurements ( Batista et al., 2019 ).
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Review of numerical methods for modeling the interaction of soil
Review of numerical methods for modeling the interaction of soil environments with the tools of soil tillage machines M N Lysych1 1 Department Forest Industry, Metrology, Standardization and Certification, Voronezh State University of Forestry and Technologies named after G.F. Morozov, 8 Timiryazeva Street, Voronezh 394087, Russian Federation …
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Improved soil carbon stock spatial prediction in a Mediterranean soil
Soil serves as a reservoir for organic carbon stock, which indicates soil quality and fertility within the terrestrial ecosystem. Therefore, it is crucial to comprehend the spatial distribution of soil organic carbon stock (SOCS) and the factors influencing it to achieve sustainable practices and ensure soil health. Thus, the present study aimed to …
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Application of machine learning algorithms to model …
This study demonstrates the ability of ML models to estimate values of soil thermal diffusivity, and it provides reference information for future thermo-related soil …
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Soil Modeling
The physical defection of soil resistance p j is shown in Fig. 8.3C.This represents an installed pile (that assumes no bending so that soil stresses and depth x i are uniformly distributed) and a thin slice of soil at some depth x i (Fig. 8.3 A) below the mud line.If the pile is loaded laterally with a deflection y i at depth x i, the soil stresses could yield the …
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Soil – Machine Interaction: Simulation and Testing
For this purpose, track –soil models and 3D tire model have been developed and implemented within the machine multi-body dynamics code (developed and owned by ). Moreover, compaction operations are usually needed to be modeled to understand soil and landfill compaction efficiency when these machines are used.
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From data to interpretable models: machine learning for soil …
Soil moisture is critical to agricultural business, ecosystem health, and certain hydrologically driven natural disasters. Monitoring data, though, is prone to instrumental noise, wide ranging extrema, and nonstationary response to rainfall where ground conditions change. Furthermore, existing soil moisture models generally forecast …
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Application of machine learning algorithms to model soil …
Soil thermal diffusivity ( k, units: m 2 /s) is defined as the ratio of thermal conductivity ( λ) to the volumetric heat capacity ( C) [ 24, 63 ], and it characterizes a soil's ability to propagate temperature changes. As illustrated in Fig. 1, methods to determine k can be sorted into three categories: direct measurement methods, analytical ...
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Solved Figure Q3(i) shows a soil compaction machine which
Figure Q3(i) shows a soil compaction machine which can be modeled as shown in Figure Q3(ii). The tractor of the machine has mass of m, which is connected to the roller by a flexible hitch. The hitch can be modeled as a spring with stiffness of k. The roller has mass of m and radius of r can roll without slipping on a horizontal plane as shown ...
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Modeling of soil movement in the screw conveyor of the …
For this study, modeling was conducted based on the operational data of EPB machine used in a project in Seattle, WA. The earth pressure balance machine used for this project was 6.44 m in diameter, equipped with a combined screw conveyor consist of two screws in series in order to control the pressure along the screw more uniformly.The …
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Soil erosion modeled with USLE, GIS, and remote sensing
The Ikkour watershed located in the Middle Atlas Mountain (Morocco) has been a subject of serious soil erosion problems. This study aimed to assess the soil erosion susceptibility in this mountainous watershed using Universal Soil Loss Equation (USLE) and spectral indices integrated with Geographic Information System (GIS) …
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Evaluation of Two SMAP Soil Moisture Retrievals Using Modeled …
A comprehensive evaluation of the performance of satellite-based soil moisture (SM) retrievals is undoubtedly very important to improve its quality and evaluate its potential application in hydrology, climate, and natural disasters (drought, flood, etc.). Since the release of the SMAP (Soil Moisture Active Passive) mission data in April 2015, the …
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Sensitivity of Modeled Soil NOx Emissions to Soil Moisture
Soil porosity is required to convert VSM to WFPS, with WFPS defined by the ratio of VSM to soil porosity (Equation 2). We use soil porosity from the Catchment model provided with SMAP Level 4 modeled data (Reichle et al., 2017), which provides soil porosity on a 9 km EASE grid which we regrid to the 0.25° grid for use within our study. …
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Digital mapping of soil erodibility factor in northwestern
Understanding the spatial distribution of soil erodibility factor (K-factor) at the district scale is essential for managing water erosion risk. In this research, we performed to predict the low and high classes of K-factor in the northwest of Iran. Based on this, soil sampling was performed at 64 points using the grid sampling method with 1 km spacing. …
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Machine learning–informed soil conditioning for …
Abstract. Effective soil conditioning is critical for mechanized shield tunneling, yet the selection of conditioning parameters remains experience-oriented. This …
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Machine Learning for Modeling Soil Organic Carbon …
Land cover change can affect soil organic carbon (SOC) concentrations in both top- and subsoils. Here, we propose to implement emerging machine learning (ML) …
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Determination of bioavailable arsenic threshold …
We further used the purposely collected field data to predict the concentration of bioavailable As in the paddy soil with the help of random forest (RF), gradient boosting machine (GBM) and LR models.
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Modeling the soil-machine response of secondary tillage: A …
In this work, we refer to this as the soil-machine response. The same fact applies to agricultural robots or machines that are supposed to work autonomously on fields. ... In case of samples a), b) and d), the modeled RC also increases with higher working speed but less steeply as for sample e). Sample c) is more difficult to interpret, …
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