Exploiting Deep Learning Algorithm to Understand Buildings’ Façade Characteristics

Rampini, Luca; Khodabakhshian, Ania; Cecconi, Fulvio Re

doi:10.1007/978-3-030-82199-9_25

Cited by 2 publications

(1 citation statement)

References 66 publications

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“…Currently, DL techniques are used for two tasks: Computer Vision, referred to actions that identify human faces and objects in digital photographs or videos; and NLP, considered as the machine's ability to recognize, analyze, comprehend, and/or generate data in written and spoken human communications. According to(Martinez et al 2019) Computer Vision (CV) applications in construction covers four main topics: Construction safety and personnel monitoring(Seo et al 2015), Resource tracking and activity monitoring(Yang et al 2015), Surveying and as-is modeling(Ptrucean et al 2015, Rampini et al 2022, and Inspection and condition monitoring(Spencer et al 2019). On the other hand, NLP techniques are mainly used for compliance checking(Zhang & El-Gohary 2017) and risk prediction(Li et al 2021).…”

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confidence: 99%

Artificial intelligence feasibility in construction industry

Luca¹,

Ania²,

Fulvio³

2022

Proceedings of the 2022 European Conference on Computing in Construction

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Artificial Intelligence (AI) has a great impact on increasing productivity and economic growth in many sectors. However, in the construction industry, AI is still limited to research and few pilot projects. This study aims to depict the current rate of AI adoption in the industry and understand the obstacles that are hindering the required changes in the companies' business models. The data are collected through a tailored questionnaire sent to experts and practitioners in the field. The results show that labourskilled shortage, data quality, cost-benefit and lack of case studies and standards have been identified as major issues. The findings help to understand the needs of construction practitioners and propose possible solutions.

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confidence: 99%

Artificial intelligence feasibility in construction industry

Luca¹,

Ania²,

Fulvio³

2022

Proceedings of the 2022 European Conference on Computing in Construction

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Artificial intelligence in construction asset management: a review of present status, challenges and future opportunities

Rampini¹,

Cecconi²

2022

ITcon

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The built environment is responsible for roughly 40% of global greenhouse emissions, making the sector a crucial factor for climate change and sustainability. Meanwhile, other sectors (like manufacturing) adopted Artificial Intelligence (AI) to solve complex, non-linear problems to reduce waste, inefficiency, and pollution. Therefore, many research efforts in the Architecture, Engineering, and Construction community have recently tried introducing AI into building asset management (AM) processes. Since AM encompasses a broad set of disciplines, an overview of several AI applications, current research gaps, and trends is needed. In this context, this study conducted the first state-of-the-art research on AI for building asset management. A total of 578 papers were analyzed with bibliometric tools to identify prominent institutions, topics, and journals. The quantitative analysis helped determine the most researched areas of AM and which AI techniques are applied. The areas were furtherly investigated by reading in-depth the 83 most relevant studies selected by screening the articles’ abstracts identified in the bibliometric analysis. The results reveal many applications for Energy Management, Condition assessment, Risk management, and Project management areas. Finally, the literature review identified three main trends that can be a reference point for future studies made by practitioners or researchers: Digital Twin, Generative Adversarial Networks (with synthetic images) for data augmentation, and Deep Reinforcement Learning.

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Exploiting Deep Learning Algorithm to Understand Buildings’ Façade Characteristics

Cited by 2 publications

References 66 publications

Artificial intelligence feasibility in construction industry

Artificial intelligence feasibility in construction industry

Artificial intelligence in construction asset management: a review of present status, challenges and future opportunities

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