A two-dimensional entropy-based method for detecting the degree of segregation in asphalt mixture

Hu, Tao; Yuan, Jiahu; Zhou, Xiao; Liu, Lu; Ran, Maoping

doi:10.1016/j.conbuildmat.2022.128450

Cited by 8 publications

(4 citation statements)

References 33 publications

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“…Due to the different mean values of the particle mass ratio being measured, using the standard deviation of the deviation degree for further comparison is not very accurate. [25] Therefore, the coefficient of variation was introduced to characterize the degree of variation of the particle distribution. The coefficient of variation for large particles C x can be expressed as the ratio of the unbiased standard deviation of the deviation degree 𝜀 xi and the mean value εx : By following the above steps, the coefficient of variation for the middle particle and small particle C y and C z can be obtained.…”

Section: Determination Of Coefficient Of Variation Of Particle Deviationmentioning

confidence: 99%

“…g j (X) = y j (X) − U j for y j > U j g j (X) = 0 for L j ≤ y j ≤ U j g j (X) = L j − y j (X) for y j < L j (24) where X represents design variables in optimization space; g j is the constraint function of the j-th iteration; y j is the optimal response of the j-th iteration; and U j and L j are reactions constrained by upper and lower limits, respectively. This forms a constraint system, which can be solved as an unconstrained problem by the penalty function method, as shown in Equation (25). The goal of the penalty function is to provide a mountain to climb when the optimization starts at an undesired position.…”

Section: Optimization Based On Hc Algorithmmentioning

confidence: 99%

{C_x}

can be expressed as the ratio of the unbiased standard deviation of the deviation degree

{\varepsilon _{x{\mathrm{i}}}}

and the mean value

{\bar \varepsilon _x}

\begin{equation}\;{C_x} = \frac{{\sqrt {\mathop \sum \nolimits_{{\mathrm{i}} = 1}^n \frac{{{{({\varepsilon _{x{\mathrm{i}}}} - {{\bar \varepsilon }_{x{\mathrm{i}}}})}^2}}}{{n - 1}}} }}{{{{\bar \varepsilon }_x}}}\;\end{equation}

…”

Section: Evaluation Indicators Of Asphalt Mixtures Uniformitymentioning

confidence: 99%

See 2 more Smart Citations

Optimization of Particle Distribution for Asphalt Mixtures in Screw Conveyer of Paver Based on Discrete Element Method and Response Surface Methodology

Zhang³

et al. 2023

Advcd Theory and Sims

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To improve the uniformity of asphalt mixtures, optimizing the particle distribution is essential, and the screw conveyor of paver plays a critical role in this process. In this regard, a parameter optimization strategy based on the discrete element method and response surface methodology (RSM) is proposed. First, a discrete element model is developed for spiral material separation and asphalt mixture particles. Next, uniformity evaluation indicators are used to analyze the impact of individual parameters on particle uniformity. Then, simulation experiments are conducted under varying material level coefficients and rotational speeds, and a regression model is developed to analyze the impact of coupling factors using RSM. Afterward, different algorithms are employed to optimize the parameters of the model, and the results show that the particle swarm optimization algorithm is better than the traditional hill climbing algorithm. Last, field tests are conducted to verify the feasibility of the proposed optimization strategy. The optimal theoretical parameter of the material level coefficient is 0.95, and the rotational speed is 53 rpm. Overall, the proposed strategy offers an effective and accurate model for simulating the process of particle separation, which can lead to improved uniformity of asphalt mixtures.

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Section: Determination Of Coefficient Of Variation Of Particle Deviationmentioning

confidence: 99%

Section: Optimization Based On Hc Algorithmmentioning

confidence: 99%

{C_x}

can be expressed as the ratio of the unbiased standard deviation of the deviation degree

{\varepsilon _{x{\mathrm{i}}}}

and the mean value

{\bar \varepsilon _x}

\begin{equation}\;{C_x} = \frac{{\sqrt {\mathop \sum \nolimits_{{\mathrm{i}} = 1}^n \frac{{{{({\varepsilon _{x{\mathrm{i}}}} - {{\bar \varepsilon }_{x{\mathrm{i}}}})}^2}}}{{n - 1}}} }}{{{{\bar \varepsilon }_x}}}\;\end{equation}

…”

Section: Evaluation Indicators Of Asphalt Mixtures Uniformitymentioning

confidence: 99%

See 1 more Smart Citation

Optimization of Particle Distribution for Asphalt Mixtures in Screw Conveyer of Paver Based on Discrete Element Method and Response Surface Methodology

Zhang³

et al. 2023

Advcd Theory and Sims

View full text Add to dashboard Cite

show abstract

“…Zhang Z, et al [13] and Hu T, et al [14] use the support of the Canny edge detection method to solve image processing problems such as the detection of asphalt pavement thickness [14] and also the detection of quality of crosssectional fiber image [13].…”

Section: Introductionmentioning

confidence: 99%

Canny and Morphological Approaches to Calculating Area and Perimeter of Two-Dimensional Geometry

Mentari

Syaifudin

Funabiki

et al. 2022

Jartel

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Calculating area and perimeter in real-world conditions has its challenges. The actual conditions include applications in the medical field to measure the presence of tumors or the condition of human organs and applications in geography to measure specific areas on a map; applications in architecture often calculate the area and perimeter of buildings, interior design, exterior design, and other uses. Technology can make it easier with automatic calculations. Mathematical methods and computer vision techniques are required to create automated systems. The Canny method is usually used, which is good enough for detecting edges but not sufficient for measuring irregular geometric shapes. This paper aims to calculate the area and perimeter of a geometric shape using the Canny method and geometry. Data samples in various forms are used in this study. Calculating area and perimeter using the Canny method involves obtaining the length (X,Y) of the RGB image converted to HSV. Edge detection values are used to calculate the area and perimeter of objects. The morphological method uses binary image input as input data. Then proceed to the convolution process with structuring and calculating the area and circumference of objects. Based on the research results, calculating the area and circumference of objects is more effective using morphological methods. However, the level of accuracy is affected by the selection of structuring elements (strels) which must be optimal and global.

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Precise Z-Block positioning and dimension measurement using improved Canny edge detection and sub-pixel contour fitting

Xiong,

Wang,

Yin

et al. 2024

J Supercomput

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A two-dimensional entropy-based method for detecting the degree of segregation in asphalt mixture

Cited by 8 publications

References 33 publications

Optimization of Particle Distribution for Asphalt Mixtures in Screw Conveyer of Paver Based on Discrete Element Method and Response Surface Methodology

Optimization of Particle Distribution for Asphalt Mixtures in Screw Conveyer of Paver Based on Discrete Element Method and Response Surface Methodology

Canny and Morphological Approaches to Calculating Area and Perimeter of Two-Dimensional Geometry

Precise Z-Block positioning and dimension measurement using improved Canny edge detection and sub-pixel contour fitting

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