Domain Analysis to assess the sustainability of construction projects using Semantic Technologies

Cristiano Geraldo Teixeira  Silva; Marcello Peixoto Bax

doi:10.36311/1981-1640.2022.v16.e02144

Autores

Cristiano Geraldo Teixeira Silva FUMEC https://orcid.org/0000-0001-5010-3081
Marcello Peixoto Bax

DOI:

https://doi.org/10.36311/1981-1640.2022.v16.e02144

Palavras-chave:

Semantic technologies, Ontology, Sustainable constructive projects, LEED

Resumo

A avaliação de projetos de construção visa medir o quão sustentáveis são os ambientes construídos e isso é importante para salvar o meio ambiente de uma deterioração crescente. Porém, a maioria das tarefas de avaliação são realizadas em ferramentas diferentes, em todas as fases da construção e até manualmente, consumindo tempo e aumentando a possibilidade de erros. O problema de pesquisa é verificar como o uso de tecnologias semânticas representaria uma alternativa vantajosa, em relação ao estado da arte, para analisar, sistematizar e organizar informações do domínio dos projetos de construção, acelerando o processo de avaliação. São apresentados os conceitos por trás de um experimento que emprega tecnologias semânticas e uma ontologia de domínio para integrar dados de projetos BIM (Building Information Modeling) e outros dados tabulares externos ao projeto para automatizar as tarefas de certificação LEED (Leadership in Energy and Environmental Design). Para produzir o protótipo deste experimento, dados de um projeto BIM e dados tabulados abertos da cidade de Belo Horizonte são semanticamente anotados e um gráfico de conhecimento é gerado em formato RDF. Depois de integrados ao gráfico, os dados são usados para avaliar os critérios de certificação por meio de consultas. Os resultados sugerem que a análise de domínio associada às tecnologias semânticas promove a extensibilidade conceitual dos elementos construtivos, possibilitando sua integração com bases de conhecimento externas. Outra vantagem é a organização conceitual dos dados com o objetivo de melhorar as tarefas de recuperação de informações relevantes.

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