Geostatistical Analysis for Spatially Referenced Roller-Integrated Compaction Measurements

Vennapusa, Pavana; White, David J.; Morris, Max D.

doi:10.1061/(asce)gt.1943-5606.0000285

Cited by 79 publications

(37 citation statements)

References 14 publications

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“…Numerous different indicators such as Compaction Meter Value (CMV), Resonance Meter Value, Continuous Compaction Value, Omega value, and machine drive power have been proposed to gauge the compaction state of the soil through the spectrum analysis of the vibration signal (White & Thompson, ; Xu & Chang, ). Meanwhile, research has been reported on the theory of the compaction process (Beainy, Commuri, & Zaman, ; Beainy, Commuri, Zaman, & Syed, ), the spatial distribution (Vennapusa, White, & Morris, ; Xu & Chang, ), the prediction model based on neural networks (Beainy, Commuri, & Zaman, ; Commuri, Mai, & Zaman, ), and the automatic control of compaction quality (Xu, Chang, & Gallivan, ). However, some researchers have questioned the accuracy of CCC‐ and IC‐measured indicators because the correlation coefficient between these indicators and compaction quality is not very high (Meehan, Cacciola, Tehrani, & Iii, ).…”

Section: Introductionmentioning

confidence: 99%

Smart bacteria‐foraging algorithm‐based customized kernel support vector regression and enhanced probabilistic neural network for compaction quality assessment and control of earth‐rock dam

et al. 2018

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Compaction quality assessment and control for an earth‐rock dam is the key measure to ensure dam safety. However, to date, the compaction quality assessment model has not been accurate enough, and no effective feedback control measures have been developed. Hybrid data mining algorithms have great potential for solving this problem. In this study, smart bacteria‐foraging algorithm‐based customized kernel support vector regression (SBFA‐CKSVR) is proposed for compaction quality assessment, whereas an enhanced probabilistic neural network (EPNN) is adopted for compaction quality control. SBFA integrates a bacteria‐foraging algorithm, chaos mapping, and adaptive and quantum computing to solve the high‐dimensional complex problem effectively. CKSVR is proposed to approximate a function in quadratic continuous integral space L2(R) where its hyperparameters are optimized by SBFA. Finally, SBFA‐CKSVR is used to establish a high‐precision compaction quality assessment model whereas the EPNN is adopted to realize the compaction quality feedback control. A three‐dimensional real‐time monitoring system for the earth‐rock dam is also developed based on SBFA‐CKSVR and EPNN. A large‐scale hydraulic engineering application proves the effectiveness and superiority of this research compared with the previous work.

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Section: Introductionmentioning

confidence: 99%

Smart bacteria‐foraging algorithm‐based customized kernel support vector regression and enhanced probabilistic neural network for compaction quality assessment and control of earth‐rock dam

et al. 2018

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“…A major advantage of the data that is recorded by CCC and IC systems is that measurements can be made much more continuously than with traditional spot-measurement tests such as the nuclear density gauge test or sand cone test. Consequently, CCC or IC measurements are sometimes considered to represent 100% coverage of the compacted area (e.g., Vennapusa et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Monitoring Field Lift Thickness Using Compaction Equipment Instrumented with Global Positioning System (GPS) Technology

Meehan

Khosravi

Cacciola

2013

Geotechnical Testing Journal

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When constructing earthen embankments, it is essential that the soil be placed and spread in uniform lifts prior to compaction. To ensure that the resulting soil lifts are evenly compacted, typical compaction specification approaches place restrictions on the thickness that is acceptable for each soil lift. In current practice, it can be extremely difficult for a field inspector to verify that lift thickness requirements are being met when soil is being placed and spread over a large area, without the use of frequent surveying (which adds both costs and delays to earthwork projects). Recent advances in compaction control include the development of continuous compaction control (CCC) and intelligent compaction (IC) systems, which provide real-time monitoring and feedback about the operation and performance of soil compaction. Typically, CCC and IC compaction equipment is outfitted with a real-time kinematic global positioning system (RTK-GPS) that monitors and records the position of the compacter as the soil lift is being compacted. This paper suggests that geotechnical engineers use field RTK-GPS measurements that are made by CCC or IC equipment to monitor and control the thickness of compacted soil lifts. Data collected from a full-scale field study is used to illustrate the practical issues with using GPS measurements for field monitoring of lift thickness during construction of a roadway embankment, such as varying roller position from lift-to-lift and the measurement uncertainty associated with RTK-GPS measurement data. The use of both simple and sophisticated spatial analysis techniques are explored for interpolating measured field elevation data onto a uniform grid for lift thickness assessment. The resulting methodology that is presented can be utilized to build spatial maps of compacted soil lift thickness, a process that can be used to great benefit by field engineers who are trying to ensure the quality of compacted soil lifts.

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“…A relationship documented by White et al (2007b) for a granular base material (classified as A-1-b) between CMV and Keros 200-mm plate diameter LWD device (E LWD-K2 ) is presented in the figure as a reference. The E LWD-K2 and E LWD-D2 measurements are strongly correlated and are close to R 2 = 1 line (see Vennapusa and White 2008b). Another regression line is plotted on Figure 5.9 for E LWD-Z2 based on a relationship documented by Vennapusa and White (2008b) between E LWD-K2 and E LWD-Z2 measurements (E LWD-K2 = 1.75 E LWD-Z2 ).…”

Section: Comparison Of Roller-integrated and In-situ Point Measurementsmentioning

confidence: 62%

“…The E LWD-K2 and E LWD-D2 measurements are strongly correlated and are close to R 2 = 1 line (see Vennapusa and White 2008b). Another regression line is plotted on Figure 5.9 for E LWD-Z2 based on a relationship documented by Vennapusa and White (2008b) between E LWD-K2 and E LWD-Z2 measurements (E LWD-K2 = 1.75 E LWD-Z2 ). Although there is scatter, the E LWD-D2 and E LWD-Z2 measurements matched the trend of existing relationships.…”

Section: Comparison Of Roller-integrated and In-situ Point Measurementsmentioning

confidence: 62%

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Investigation of roller-integrated compaction monitoring and in-situ testing technologies for characterization of pavement foundation layers

Vennapusa

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Geostatistical Analysis for Spatially Referenced Roller-Integrated Compaction Measurements

Cited by 79 publications

References 14 publications

Smart bacteria‐foraging algorithm‐based customized kernel support vector regression and enhanced probabilistic neural network for compaction quality assessment and control of earth‐rock dam

Smart bacteria‐foraging algorithm‐based customized kernel support vector regression and enhanced probabilistic neural network for compaction quality assessment and control of earth‐rock dam

Monitoring Field Lift Thickness Using Compaction Equipment Instrumented with Global Positioning System (GPS) Technology

Investigation of roller-integrated compaction monitoring and in-situ testing technologies for characterization of pavement foundation layers

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