Clarification of vehicle cone index with reference to mean maximum pressure

Priddy, Jody D.; Willoughby, William E.

doi:10.1016/j.jterra.2004.10.001

Cited by 38 publications

(28 citation statements)

References 2 publications

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“…Trafficability assessment is often done using field-based surveys performed by military personnel (e.g., [16,[40][41][42]). The US Army Corps of Engineers has developed standard in situ techniques for trafficability assessment, using a variety of devices including a handheld cone penetrometer (PCP) ( [41]).…”

Section: Trafficability Assessment Modelmentioning

confidence: 99%

“…The vehicle mobility model is based on the concept of the Rating Cone Index (RCI) [15,16], a measure of soil surface load-bearing capacity, and a Vehicle Cone Index (VCI) [16,17], vehicle-specific critical values of RCI that are indicative of impaired mobility. The soil moisture data assimilation system, described in detail by Flores et al [2012], uses a physics-based ecohydrology model (described below) to simulate the spatiotemporal distribution of soil moisture at the hillslope scale.…”

Section: Introductionmentioning

confidence: 99%

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Application of a hillslope-scale soil moisture data assimilation system to military trafficability assessment

Flores

Entekhabi

Bras

2014

Journal of Terramechanics

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Soil moisture is an important environmental variable that impacts military operations and weapons systems. Accurate and timely forecasts of soil moisture at appropriate spatial scales, therefore, are important for mission planning. We present an application of a \ soil moisture data assimilation system to military trafficability assessment. The data assimilation system combines hillslope-scale (e.g., 10s to 100s of m) estimates of soil moisture from a hydrologic model with synthetic L-band microwave radar observations broadly consistent with the planned NASA Soil Moisture Active-Passive (SMAP) mission. Soil moisture outputs from the data assimilation system are input to a simple index-based model for vehicle trafficability. Since the data assimilation system uses the ensemble Kalman Filter, the risks of impaired trafficability due to uncertainties in the observations and model inputs can be quantified. Assimilating the remote sensing observations leads to significantly different predictions of trafficability conditions and associated risk of impaired trafficability, compared to an approach that propagates forward uncertainties in model inputs without assimilation. Specifically, assimilating the observations is associated with an increase in the risk of "slow go" conditions in approximately two-thirds of the watershed, and an increase in the risk of "no go" conditions in approximately 40% of the watershed. Despite the simplicity of the trafficability assessment tool, results suggest that ensemble-based data assimilation can potentially improve trafficability assessment by constraining predictions to observations and facilitating quantitative assessment of the risk of impaired trafficability.Highlights: 1. We apply a soil moisture data assimilation system (DAS) to military trafficability 2. Assimilation improves prediction of impaired trafficability 3. Ensemble-based assimilation supports quantification of trafficability risk

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Section: Trafficability Assessment Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of a hillslope-scale soil moisture data assimilation system to military trafficability assessment

Flores

Entekhabi

Bras

2014

Journal of Terramechanics

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“…Methods were developed to quantify the soft soil performance of military vehicles and to characterise the terrain over which the vehicle would operate (Nuttall et al, 1966). The US Army Corps of Engineers developed the vehicle cone index (VCI) metric to quantify a ground vehicle's performance in soft soil (Priddy and Willoughby, 2006;Stevens et al, 2013). This metric describes the minimum soil strength needed for a vehicle to make a specified number of passes over terrain without becoming immobilised.…”

Section: Introductionmentioning

confidence: 99%

“…VCI can be both measured in the field and predicted based on vehicle characteristics (Priddy, 1995). VCI is heavily influenced by the vehicle's ground contact pressure (Priddy and Willoughby, 2006).…”

Section: Introductionmentioning

confidence: 99%

Full-featured ground vehicle mobility analysis using different soil moisture sources

Stevens

McKinley

Vahedifard

2017

IJVP

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Abstract:We investigated the influence of using three different soil moisture sources WindSat (a satellite), land information system (LIS) (a computer model), and in situ ground sensors in a full-featured ground vehicle mobility analysis model. Soil moisture, soil type, land use, and slope were used to estimate the mobility in dry and wet scenarios for six vehicles using the NATO Reference Mobility Model (NRMM). The dry scenario showed nearly the same results for all three soil moisture sources. In the wet scenario, modelled travel times based on LIS were about 26% faster compared to those from the in situ moisture. Modelled times based on WindSat averaged 11% slower than those from the in situ moisture. For the test case we found agreement between the results of the current full-feature model against a previously-developed model using only soil moisture time series. This demonstrates that soil moisture can be important even as one terrain factor among many.

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“…This article focuses on determining soil resistances to penetration (or soil penetrability) as primary means to model and map soil trafficability in general, and soil rutting specifically. The analytical framework to do this was derived from the Waterways Experiment Station Method of the US Army Engineers (Carter et al 2000, Saarilahti 2002Priddy and Willoughby 2006). This method uses cone penetrometers (1) to probe the resistance of soils to penetration, (2) to ascertain how many vehicles of certain type and load can pass through an area under given soil and weather conditions without exceeding a predefined rutting depth, and (3) to evaluate the effect of rutting on soil compaction and strength.…”

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confidence: 99%

Modeling and mapping soil resistance to penetration and rutting using LiDAR-derived digital elevation data

Campbell¹,

White²,

Arp³

2013

Journal of Soil and Water Conservation

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Soil resistances to penetration were probed with a hand-held soil cone penetrometer across ridge-to-depression transects for two contrasting study areas in Alberta, Canada: one in the foothills west of Calgary, and one in the boreal plain north of Peace River. The resulting cone index values (CI) were related to plot-measured values for soil moisture, density, texture, organic matter content, soil depth, elevation, slope, and a cartographic depth-to-water index (DTW) by way of multiple regression analysis. Elevation, slope, DTW, and the associated flow accumulation pattern were all derived from a light detection and ranging (LiDAR)-generated bare ground digital elevation model (DEM), at 1 m (3 ft) resolution. The field-determined CI values conformed to a previous formulation that related CI to soil texture, density, and waterfilled pore space. In terms of topographic position, CI increased with increasing DTW, in parallel with decreasing soil moisture content and increasing soil density. The resulting best-fitted regression between CI, log 10 (DTW), and elevation (or study area) was used to map CI and expected all-terrain recreational vehicle-specific rutting depths across the landscape. The maps so generated were in general agreement with the results of a 40 km (25 mi) long soil disturbance survey along a recreational vehicle trail section within the foothill area.

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Clarification of vehicle cone index with reference to mean maximum pressure

Cited by 38 publications

References 2 publications

Application of a hillslope-scale soil moisture data assimilation system to military trafficability assessment

Application of a hillslope-scale soil moisture data assimilation system to military trafficability assessment

Full-featured ground vehicle mobility analysis using different soil moisture sources

Modeling and mapping soil resistance to penetration and rutting using LiDAR-derived digital elevation data

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