Image Based Inspection and Monitoring of Buildings

Mishra, Eshanta; Anwar, Naveed; Izhar, Muhammad Amir; Supprasert, Sumet

doi:10.1007/978-3-030-48465-1_18

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…In [42], authors apply a multi-label classification task for material recognition of building facades using GIS data and street views and setup three image classification models, namely Vision Transformer, Swin Transformer V2 and ConvNeXt. Regarding image-based inspection and monitoring of buildings, [43] combine binary thresholding of images with a DT to detect cracks in concrete surfaces, map the results to the Building Information Modeling (BIM) model, to update and visualize the current building state. [44] attempt to estimate construction waste generation using multiple linear regression, DT, Gray Model and NN with study area focusing on the Greater Bay Area in China, one of the world's most dynamic regions in terms of construction activity.…”

Section: Applied Machine Learning In the Fields Of Circular Economymentioning

confidence: 99%

Challenges and Opportunities of Artificial Intelligence and Machine Learning in Circular Economy

Despotovic,

Glatschke

2024

Preprint

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The inherent "take-make-waste" of the current linear economy is a major contributor to exceeding planetary boundaries. The transition to a circular economy (CE) and the associated challenges and opportunities require fast, innovative solutions. Artificial Intelligence (AI) and Machine Learning (ML) can play a key role in the transition to a CE paradigm by overcoming the challenges of increasing material extraction and use and creating a far more environmentally sustainable future. The objective of this article is to provide a status quo on the use of AI and ML in the transition to CE and to discuss the potential and challenges in this regard. The literature survey on Google Scholar using targeted queries with predefined keywords and search operators revealed that the number of experimental scientific contributions to AI and ML in the CE has increased significantly in recent years. As the number of research articles increased, so did the number of ML methods and algorithms covered in experimental CE publications. In addition, we found that there are 84% more AI and ML-affiliated research articles on CE in Google Scholar since 2020, compared to the total number of entries, and 55% more articles since 2023, compared to the respective articles up to 2023. The status quo of the scientific contributions shows that AI and ML are seen as extremely useful tools for the CE and their use is steadily increasing.

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Section: Applied Machine Learning In the Fields Of Circular Economymentioning

confidence: 99%

Challenges and Opportunities of Artificial Intelligence and Machine Learning in Circular Economy

Despotovic,

Glatschke

2024

Preprint

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“…Os drones colhem, por sua vez, capturam imagens de diversos locais, as quais podem ser utilizadas para construir modelos 3D, posteriormente unidos aos modelos BIM, proporcionando uma análise mais aprimorada (MISHRA et al 2021).…”

Section: Ferramentas Tecnológicasunclassified

Análise bibliométrica da pesquisa sobre levantamento de quantitativos de obras em execução utilizando BIM

Fernandes,

Nielsen,

Miceli Junior

et al. 2023

Viii Simpósio Brasileiro De Qualidade Do Projeto No Ambiente Construído (Sbqp)

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O artigo tem como objetivo identificar as principais tendências de pesquisa relacionadas ao Building Information Modeling (BIM) e sua relação com o levantamento de quantitativos de serviços executados durante a obra, visando uma melhor qualidade nos projetos, por meio de uma revisão bibliográfica sobre os termos e uma análise bibliométrica. A metodologia consistiu em buscar na base de dados Scopus publicações dos últimos cinco anos que relacionassem os termos "Building Information Modeling" e "Quantity Take-Off". Utilizou-se o programa VOSViewer para construir e visualizar mapas bibliométricos. Os resultados obtidos indicam que a utilização do BIM para realizar o levantamento de quantitativos de serviços nas obras vem sendo estudada, principalmente em relação à automação e a integração dos processos, e que há uma tendência crescente no uso de novas tecnologias para levantamento de dados em campo. Este estudo mostra que existe uma tendência crescente de pesquisas relacionadas ao levantamento de quantitativos de serviços nas obras com uso do BIM e a análise das ferramentas disponíveis para auxiliar e otimizar o processo de medição de obra.

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Machine Learning Techniques for Structural Health Monitoring of Concrete Structures: A Systematic Review

Padmapoorani¹,

Senthilkumar²,

Mohanraj³

2023

Iran J Sci Technol Trans Civ Eng

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Image Based Inspection and Monitoring of Buildings

Cited by 3 publications

References 3 publications

Challenges and Opportunities of Artificial Intelligence and Machine Learning in Circular Economy

Challenges and Opportunities of Artificial Intelligence and Machine Learning in Circular Economy

Análise bibliométrica da pesquisa sobre levantamento de quantitativos de obras em execução utilizando BIM

Machine Learning Techniques for Structural Health Monitoring of Concrete Structures: A Systematic Review

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