Identification of Environmental Pollutants in Construction Site Monitoring Using Association Rule Mining and Ontology-Based Reasoning

Abstract

Pollutants from construction activities of building projects can have serious negative impacts on the natural environment and human health. Carrying out monitoring of environmental pollutants during the construction period can effectively mitigate environmental problems caused by construction activities and achieve sustainable development of the construction industry. However, the current environmental monitoring method relying only on various sensors is relatively singlar which is unable to cope with a complex on-site environment We propose a mechanism for environmental pollutants identification combining association rule mining and ontology-based reasoning and using random forest algorithm to improve the accuracy of identification. Firstly, the ontology model of environmental pollutants monitoring indicator in the construction site is built in order to integrate and share the relative knowledge. Secondly, the improved Apriori algorithm with added subjective and objective constraints is used for association rule mining among environmental pollutants monitoring indicators, and the random forest algorithm is applied to further filter the strong association rules. Finally, the ontology database and rule database are loaded into a Jena reasoning machine for inference to establish an identification mechanism of environmental pollutants. The results of running on a real estate development project in Jiangning District, Nanjing, prove that this identification mechanism can effectively tap the potential knowledge in the field of environmental pollutants monitoring, explore the relationship between environmental pollutants monitoring indicators and then overcome the shortcomings of traditional monitoring methods that only rely on sensors to provide new ideas and methods for making intelligent decisions on environmental pollutants in a construction site.