Dynamical Response of Vibratory Rollers during the Compaction of Asphalt Pavements

Beainy, Fares; Commuri, Sesh; Zaman, Musharraf

doi:10.1061/(asce)em.1943-7889.0000730

Cited by 29 publications

(14 citation statements)

References 18 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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“…The literature on mechanical modeling of roller-compactors for numerical response simulation is quite scarce. Basically, it can be distinguished between finite element (FE) models (e.g., [3,9]) and lumped parameter models (e.g., [2,14,23]) of the dynamic interacting roller-subsurface system. The focus of most FE models is to predict the subsurface compaction and, depending on the degree of sophistication, allow only selective insight into the system response, like experimental studies.…”

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confidence: 99%

Analytical modeling of the stick-slip motion of an oscillation drum

Paulmichl

Adam

2019

Acta Mech

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In this paper, a lumped parameter model of the interacting oscillation roller-subsurface system is proposed. The main aim of the model is to predict the response acceleration of the roller drum during nearsurface compaction of non-cohesive soils. The compaction process of the soil itself is not captured, but different degrees of compaction are considered by varying the soil stiffness. The roller is represented by the oscillation drum and its viscoelastic connection to the roller frame. In the chosen modeling strategy, the curvature of the soil surface below the drum is prescribed. In this way, also the vertical drum acceleration can be computed. The discrete subsoil model consists of a vertical and a horizontal Kelvin-Voigt element. Contact between drum and soil surface is described by means of dry friction according to Coulomb's law. As such, the stick-slip motion of the drum can be simulated. In the stick phase, pure rolling between drum and soil surface is assumed. The highly nonlinear equations of motion of this three degrees-of-freedom model are derived separately for the stick and the slip phase of the motion. Selective numerical studies show that this model captures the fundamental response characteristics of the dynamic drum-subsoil interacting system observed in the field. 1 Introduction A roller, also often referred to as roller-compactor, is a heavy equipment used for near-surface compaction of soil and asphalt layers in the construction of various civil structures such as roads, airfields, dams, and track foundations. A static roller uses only its weight to compact the layer, whereas a dynamic roller enhances the efficiency of subsurface compaction through dynamic excitation of the drum. Depending on the drum excitation, two basic types of dynamic rollers do exist, i.e., vibratory rollers and oscillation rollers. They differ in design, mode of operation, and how the medium to be compacted is loaded. In a vibrating drum, a single unbalance mass, which is attached concentrically to the drum axis, generates a rapidly alternating upwarddownward motion of the drum. The subgrade is compacted by the dynamic pressure applied by the drum. An oscillation drum is equipped with two offset eccentric masses, which rotate synchronously in the same direction. The resulting alternating high-frequency forward-backward motion of the drum, superposed with the translational roller motion, imposes dynamic shear forces that increase the subsurface density. Continuous Compaction Control (CCC) ([1,11]) has become the standard technology for controlling subsurface compaction by vibratory rollers. This control technique is based on the dynamic response of the interacting drum-subsurface system recorded during the roller pass, and thus, allows an instant continuous assessment of the degree of compaction. For oscillation rollers, however, no mature CCC system has been

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“…Series of FEM applications were applied to consider heterogeneity of asphalt mix type materials on meso scale (Dai et al 2006;Hahn et al 2010;Chareyre et al 2011;Fakhari Tehrani et al 2013;Huang et al 2011;Ameri et al 2011;Xia, Pan 2011;González et al 2007). Mixt discrete model presenting asphalt layer by the packing particles described as the system of the FEM was considered by Mo et al (2008).…”

Section: Introductionmentioning

confidence: 99%

Numerical Analysis of Asphalt Mixture and Comparison With Physical Marshall Test

Rimša

Kačianauskas

Sivilevičius

2014

Journal of Civil Engineering and Management

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Abstract. This paper addresses simulation of asphalt on the macroscopic scale, using the Discrete Element Model (DEM). Asphalt mixture is considered as a particulate solid composed of relatively stiff deformable layered spherical particles embedded into a homogenised matrix. The discrete model presents a 3D network of one-dimensional elements connecting particle's centres and comprising mutual interaction of particles. The contribution of the film layer and the interface thickness for normal stiffness is described analytically and illustrated by simulation results. Development of the normal interaction model of layered particles contacting via interface on the meso scale is the key point of the current development. The model applied aiming to simulate deformation of asphalt mixture is able to capture properties of the weaker matrix interface while layered particle comprises of bitumen film in meso scale. The developed technique is applied for simulation of Marshall test, i.e. diametric compression of cylindrical specimen, widely used for characterisation of asphalt mixtures. Finally, suitability of the model is illustrated by comparison with the available experimental results.

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Dynamical Response of Vibratory Rollers during the Compaction of Asphalt Pavements

Cited by 29 publications

References 18 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

Analytical modeling of the stick-slip motion of an oscillation drum

Numerical Analysis of Asphalt Mixture and Comparison With Physical Marshall Test

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