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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Deep learning-based classification of work-related physical load levels in construction
Kanghyeok Yang, Changbum R. Ahn, Hyun-Soo Kim
Advanced Engineering Informatics (2020) Vol. 45, pp. 101104-101104
Open Access | Times Cited: 55

Showing 1-25 of 55 citing articles:

Deep learning in the construction industry: A review of present status and future innovations
Taofeek Akinosho, Lukumon O. Oyedele, Muhammad Bilal, et al.
Journal of Building Engineering (2020) Vol. 32, pp. 101827-101827
Open Access | Times Cited: 375
Classification and analysis of deep learning applications in construction: A systematic literature review
Rana Khallaf, Mohamed Khallaf
Automation in Construction (2021) Vol. 129, pp. 103760-103760
Closed Access | Times Cited: 122
Forecasting building energy consumption: Adaptive long-short term memory neural networks driven by genetic algorithm
Xiaojun Luo, Lukumon O. Oyedele
Advanced Engineering Informatics (2021) Vol. 50, pp. 101357-101357
Closed Access | Times Cited: 119
Deep learning-based construction equipment operators’ mental fatigue classification using wearable EEG sensor data
Imran Mehmood, Heng Li, Yazan Qarout, et al.
Advanced Engineering Informatics (2023) Vol. 56, pp. 101978-101978
Open Access | Times Cited: 52
A science mapping-based review of work-related musculoskeletal disorders among construction workers
Maxwell Fordjour Antwi‐Afari, Heng Li, Alan H. S. Chan, et al.
Journal of Safety Research (2023) Vol. 85, pp. 114-128
Closed Access | Times Cited: 42
The role of machine learning in the primary prevention of work-related musculoskeletal disorders: A scoping review
Victor C.H. Chan, Gwyneth B. Ross, Allison L. Clouthier, et al.
Applied Ergonomics (2021) Vol. 98, pp. 103574-103574
Open Access | Times Cited: 80
Deep learning-based networks for automated recognition and classification of awkward working postures in construction using wearable insole sensor data
Maxwell Fordjour Antwi‐Afari, Yazan Qarout, Randa Herzallah, et al.
Automation in Construction (2022) Vol. 136, pp. 104181-104181
Open Access | Times Cited: 68
Detecting excessive load-carrying tasks using a deep learning network with a Gramian Angular Field
Hoonyong Lee, Kanghyeok Yang, Namgyun Kim, et al.
Automation in Construction (2020) Vol. 120, pp. 103390-103390
Closed Access | Times Cited: 58
Exposures to select risk factors can be estimated from a continuous stream of inertial sensor measurements during a variety of lifting-lowering tasks
Sol Lim
Ergonomics (2024) Vol. 67, Iss. 11, pp. 1596-1611
Closed Access | Times Cited: 6
Environmental Risk Assessment and Management in Industry 4.0: A Review of Technologies and Trends
Janaína Lemos, Pedro Dinis Gaspar, Tânia M. Lima
Machines (2022) Vol. 10, Iss. 8, pp. 702-702
Open Access | Times Cited: 24
An Automated Recognition of Work Activity in Industrial Manufacturing Using Convolutional Neural Networks
Justyna Patalas‐Maliszewska, Daniel Halikowski, Robertas Damaševičius
Electronics (2021) Vol. 10, Iss. 23, pp. 2946-2946
Open Access | Times Cited: 27
Enhancing Workplace Safety through Personalized Environmental Risk Assessment: An AI-Driven Approach in Industry 5.0
Janaína Lemos, Vanessa Borba de Souza, Frederico Soares Falcetta, et al.
Computers (2024) Vol. 13, Iss. 5, pp. 120-120
Open Access | Times Cited: 4
Automated fall risk classification for construction workers using wearable devices, BIM, and optimized hybrid deep learning
Min‐Yuan Cheng, Deyla V.N. Soegiono, Akhmad F.K. Khitam
Automation in Construction (2025) Vol. 172, pp. 106072-106072
Closed Access
Proof-of-concept system evaluation of Ergomechanic for non-invasive estimation of upper-body posture and body load exposure in the workplace
Simon M. Harrison, Raymond C.Z. Cohen, John F. O’Hanlon, et al.
International Journal of Occupational Safety and Ergonomics (2025), pp. 1-13
Closed Access
Wearable sensors for classification of load-handling tasks with machine learning algorithms in occupational safety and health: a systematic literature review
Markus Peters, Wolfgang Potthast, Sascha Wischniewski, et al.
Ergonomics (2025), pp. 1-21
Open Access
Object Detection and Distance Measurement Algorithm for Collision Avoidance of Precast Concrete Installation during Crane Lifting Process
Yik Pong Yong, Seo Joon Lee, Young Chang, et al.
Buildings (2023) Vol. 13, Iss. 10, pp. 2551-2551
Open Access | Times Cited: 9
A Feature-Level Fusion-Based Multimodal Analysis of Recognition and Classification of Awkward Working Postures in Construction
Xiaer Xiahou, Zirui Li, Jikang Xia, et al.
Journal of Construction Engineering and Management (2023) Vol. 149, Iss. 12
Closed Access | Times Cited: 9
Monitoring and evaluating the status and behaviour of construction workers using wearable sensing technologies
Mingzhu Wang, Jiayu Chen, Ma Jun
Automation in Construction (2024) Vol. 165, pp. 105555-105555
Closed Access | Times Cited: 3
Assessing exposure to slip, trip, and fall hazards based on abnormal gait patterns predicted from confidence interval estimation
Hoonyong Lee, Gaang Lee, Sang Hyun Lee, et al.
Automation in Construction (2022) Vol. 139, pp. 104253-104253
Closed Access | Times Cited: 15
Current Status and Future Directions of Deep Learning Applications for Safety Management in Construction
Hieu Pham, Mahdi Rafieizonooz, SangUk Han, et al.
Sustainability (2021) Vol. 13, Iss. 24, pp. 13579-13579
Open Access | Times Cited: 20
Integration of deep learning and extended reality technologies in construction engineering and management: a mixed review method
Yee Sye Lee, Ali Rashidi, Amin Talei, et al.
Construction Innovation (2022) Vol. 22, Iss. 3, pp. 671-701
Open Access | Times Cited: 13
Study on abnormal gait and fall warning method using wearable sensors and a human musculoskeletal model
Xiaohui Jia, Jiarui Li, Jinyue Liu, et al.
Measurement Science and Technology (2023) Vol. 34, Iss. 6, pp. 065104-065104
Closed Access | Times Cited: 7
Investigation of the relationship between Ironworker’s gait stability and different types of load carrying using wearable sensors
Jihwan Yoon, Bogyeong Lee, Jaeyoul Chun, et al.
Advanced Engineering Informatics (2022) Vol. 51, pp. 101521-101521
Closed Access | Times Cited: 12
Multiple inertial measurement unit combination and location for recognizing general, fatigue, and simulated-fatigue gait
Yun‐Ju Lee, Mingyi Wei, Yu‐Jung Chen
Gait & Posture (2022) Vol. 96, pp. 330-337
Closed Access | Times Cited: 12
Visual analysis of machine learning methods in the field of ergonomics — Based on Cite Space V
Mengjiao Zhang, Hongxia Li, Shuicheng Tian
International Journal of Industrial Ergonomics (2022) Vol. 93, pp. 103395-103395
Closed Access | Times Cited: 11
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