期刊
INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING
卷 24, 期 2, 页码 -出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/10298436.2021.2021406
关键词
Multi-criteria decision-making; pervious concrete; mix proportions; target properties; TOPSIS; VIKOR
The aim of this study was to evaluate the performance of control and sand-modified pervious concrete mixtures and recommend the best performing products. A hybrid multi-criteria decision-making tool was developed to analyze the characteristics of 36 mixtures and optimize the selection process. The results showed that certain ratios need to be restricted in order to achieve optimal performance.
The major objective of this study was to evaluate the performance characteristics of control and sand-modified pervious concrete (PC) mixtures through the development of a hybrid multi-criteria decision-making (MCDM) tool to rank the materials and recommend the most optimal performing products. A full-factorial experimentation was adopted to analyse porosity, density, permeability, and compressive strength for 36 PC mixtures. A hybrid MCDM tool involving analytical hierarchy process, Entropy, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methods was used to optimize the multi-response problem into a single output. Based on the results, the water-to-cement ratio may be restricted to a maximum of 0.30 and 0.33 for aggregates varying from 9.5 to 6.7 mm and 6.7 to 4.75 mm, respectively. Likewise, the cement-to-aggregate ratio may be restricted to a maximum of 1:3.75 and 1:4.25 for aggregate size in the range 9.5-6.7 mm and 6.7-4.75 mm, respectively. Further, the ranks for various PC mixtures obtained from TOPSIS and VIKOR were identical/ similar for about 80% of the mixtures. The proposed MCDM tools are envisioned to help the decision-makers in selecting the most optimum PC mix based on performance and site-specific requirements, a priori pavement design and construction.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据