Paver automation for road surfacing

Tihonov, A.; Velichkin, Vladimir

doi:10.1088/1755-1315/90/1/012148

Cited by 2 publications

(1 citation statement)

References 5 publications

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“…Asphalt mixtures can be installed using excavators and graders, but the most dominant and high-quality method is by using a paver. The main parts of the paver are paving material tank, conveyor belt, augers, tamper and screed [7]. Pavers differ from each other in working widths, tank capacities, conveyor belt speeds, the speed of the paver itself, engine power, width, length, height, weight, etc.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Fuzzy MCDM Model Based on D and Z Numbers for Paver Selection: IMF D-SWARA—Fuzzy ARAS-Z Model

Jovanović

Zavadskas

Stević

et al. 2023

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One of the most important challenges when building road infrastructure is the selection of appropriate mechanization, on which the efficiency of construction and the life of exploitation depends largely. As construction machinery, pavers occupy a significant place in civil engineering projects, so their selection, depending on a road category, is a very important activity. The objective of this paper is to develop an intelligent Fuzzy MCDM (Multi-Criteria Decision-Making) model, which consists of the integration of D and Z numbers for the selection of construction machinery. The IMF D-SWARA (Improved Fuzzy D Step-Wise Weight Assessment Ratio Analysis) method was used to determine weighting coefficients. A novel Fuzzy ARAS-Z (Additive Ratio Assessment) method has been developed to determine an adequate paver for a lower category of roads (asphalt width up to 5 m), which represents an important contribution and novelty of the paper. A total of 10 alternatives were evaluated based on 16 criteria which were classified into 4 main groups. The results have shown that the alternative A8—SUPER 1300-3 represents a paver with the best characteristics for the considered set of parameters. After that, verification tests were calculated, and they include a comparative analysis with four other MCDM methods based on Z numbers, a change in the normalization procedure, and the impact of changing the size of an initial fuzzy matrix. The tests showed the stability of the developed model with negligible deviations.

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Section: Introductionmentioning

confidence: 99%