Research on the Similarity of Highway Construction Projects Based on EWM-GRA

Bo Yu (), Liudan Jiao, Yu Zhang and Xiaosen Huo
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Bo Yu: Chongqing Jiaotong University
Liudan Jiao: Chongqing Jiaotong University
Yu Zhang: Chongqing Jiaotong University
Xiaosen Huo: Chongqing Jiaotong University

A chapter in Proceedings of the 27th International Symposium on Advancement of Construction Management and Real Estate, 2023, pp 602-614 from Springer

Abstract: Abstract At present, the selection of construction solutions for highway construction projects mainly relies on personnel experience for the preparation of construction solutions for target projects, so to make use of the experience of previous highway projects, this paper establishes a decision model for construction solutions of roadbed projects based on case inference techniques. First, several characteristic attributes are extracted from the previous literature and data. The item attributes are extracted from the selected historical items, and the data is dimensionless. Moreover, the characteristic weights are assigned to the attributes using the entropy weight method. Based on this, the search for similar items is completed using gray correlation analysis and the items with the highest similarity are selected. In the last step, experts refer to the projects with the highest similarity after conducting the screening to prepare the roadbed construction plan for the new project. Finally, the model was applied to the reconstruction project of the National Highway G350 Huangshui-Huashi Ling section road in Shizhu County. Nine historical projects in the southwest area were selected for screening. The final results showed that the selected historical projects were mainly similar to the construction schemes of the target projects, which proved the model's effectiveness and effectively improved the efficiency of the roadbed construction scheme preparation.

Keywords: Roadbed construction program; CBR; Entropy power method; Grey correlation analysis (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-981-99-3626-7_47

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