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A platform for research: civil engineering, architecture and urbanism
Building a rough sets-based prediction model for classifying large-scale construction projects based on sustainable success index
To address requirements and specifications of construction project, academics need to build a project classification model. In recent years, project success concept, particularly on large-scale construction projects, has been a controversial issue, especially in developing countries. Hence, in this paper, after introducing a sustainable success index (SSI), a novel method called “rough set approach” had been adopted to induce decision rules and to classify construction projects. The paper aims to discuss these issues.
At first, 20 effective success factors and 15 success criteria based on three pillars of sustainability of economy, society and environment had been categorized. The research data used for analysis had been collected from 26 large-scale construction projects in Iran and five other countries. After collecting data collection, observations had been analyzed and 51 decision rules were generated, and the projects were classified. Eventually, in order to evaluate the performance of the generated rules, confusion matrix was applied, and the model was validated.
The results of the present study show that rough set theory (RST) can be an effective and valuable tool for building expert systems. Practical applications of these results along with limitations and future research are described.
Perhaps for the first time, in the present study, a number of large-scale construction projects are classified based on SSI. Applying RST for building rule-based system and classifying projects in construction project area are novel attempts undertaken in this paper. The rules induced in this study can be applied to develop a sustainable success prediction model in the future studies.
Building a rough sets-based prediction model for classifying large-scale construction projects based on sustainable success index
To address requirements and specifications of construction project, academics need to build a project classification model. In recent years, project success concept, particularly on large-scale construction projects, has been a controversial issue, especially in developing countries. Hence, in this paper, after introducing a sustainable success index (SSI), a novel method called “rough set approach” had been adopted to induce decision rules and to classify construction projects. The paper aims to discuss these issues.
At first, 20 effective success factors and 15 success criteria based on three pillars of sustainability of economy, society and environment had been categorized. The research data used for analysis had been collected from 26 large-scale construction projects in Iran and five other countries. After collecting data collection, observations had been analyzed and 51 decision rules were generated, and the projects were classified. Eventually, in order to evaluate the performance of the generated rules, confusion matrix was applied, and the model was validated.
The results of the present study show that rough set theory (RST) can be an effective and valuable tool for building expert systems. Practical applications of these results along with limitations and future research are described.
Perhaps for the first time, in the present study, a number of large-scale construction projects are classified based on SSI. Applying RST for building rule-based system and classifying projects in construction project area are novel attempts undertaken in this paper. The rules induced in this study can be applied to develop a sustainable success prediction model in the future studies.
Building a rough sets-based prediction model for classifying large-scale construction projects based on sustainable success index
Rough sets-based prediction model
Akbari, Saeed (author) / Khanzadi, Mostafa (author) / Gholamian, Mohammad Reza (author)
Engineering, Construction and Architectural Management ; 25 ; 534-558
2018-05-21
25 pages
Article (Journal)
Electronic Resource
English
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