Applications of Deep Learning in Intelligent Construction

Zhang, Yang; Yuen, Ka‐Veng

doi:10.1007/978-3-030-81716-9_11

Cited by 7 publications

(2 citation statements)

References 56 publications

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“…Arti cial intelligence technologies such as deep learning and computer vision are widely used in the construction industry [15][16][17], especially in the areas of schedule management [18-21], quality management [22][23][24][25][26][27][28], safety management [29][30][31][32][33][34][35][36][37][38], cost management [39,40], and assisted construction [41][42][43].…”

Section: Deep Learning and Computer Vision In Constructionmentioning

confidence: 99%

Computer vision-based monitoring method of non-wearing helmet events using face recognition

Liao,

Chen,

Liu

et al. 2023

Preprint

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Wearing helmets is crucial for ensuring the safety of workers in the construction industry because this is the first line of avoiding over 70% of production safety accidents. However, many workers are not willing to wear helmets due to discomfort and reduced work efficiency. To this end, this paper proposes a computer vision-based monitoring method using face recognition to detect and prevent non-wearing helmet events on construction sites. Compared to existing surveillance or monitoring systems, the proposed method has three significant advantages. Firstly, by using a unique structure, the proposed method can achieve up to 97.7% accuracy in detecting workers not wearing helmets. Secondly, the proposed method enables real-time detection, allowing it to prevent dangerous behaviors by stopping them in advance. Finally, the proposed method has been successfully deployed on over 20 real construction sites, and it has detected more than 18,000 related events.

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Section: Deep Learning and Computer Vision In Constructionmentioning

confidence: 99%

Computer vision-based monitoring method of non-wearing helmet events using face recognition

Liao,

Chen,

Liu

et al. 2023

Preprint

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“…These structural health monitoring systems continuously collect various types of sensing data from bridges and provide data support for artificial intelligence-based bridge damage identification. In recent years, with the rapid development of artificial intelligence, it has been widely used in various engineering fields, such as intelligent transportation, [9][10][11] smart construction sites, [12][13][14] and structural damage monitoring. [14][15][16][17] Artificial intelligence (AI) is a branch of computer science that aims to simulate human intelligence with machines.…”

Section: Introductionmentioning

confidence: 99%

Review of artificial intelligence-based bridge damage detection

Zhang

Yuen

2022

Advances in Mechanical Engineering

Self Cite

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Bridges are often located in harsh environments and are thus extremely susceptible to damage. If the initial damage is not detected in time, it can develop further causing safety hazards. Therefore, accurate detection of bridge damage is an important topic. In recent years, artificial intelligence technology has been developed rapidly, especially machine learning algorithms, which have shown amazing results in various fields while it also received attention in bridge inspection. This paper summarizes the progress of bridge damage detection research related to artificial intelligence techniques between 2015 and 2021. For structural health monitoring, sensing data is the basis for various data processing methods. The strength and weakness of the sensing data itself directly affect the effectiveness of subsequent processing methods. As a result, this paper classifies bridge damage detection studies into six categories from the types of sensing data: visual image, point cloud, infrared thermal imaging, ground-penetrating radar, vibration response, and other types of data. These six types of damage detection methods were reviewed and summarized respectively. Finally, challenges and future trends were discussed.

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