Machine Learning-Driven Visual Risk Assessments for Safety and Compliance in Project Management

Authors

  • Emily Johnson Assistant Professor, Department of Project Management, Stanford University, Stanford, USA Author

Keywords:

Machine Learning, Visual Risk Assessments, Project Management, Computer Vision, Automation, Construction Safety

Abstract

The integration of machine learning and computer vision into project management represents a significant advancement in safety and compliance efforts. This paper explores how these technologies can automate visual risk assessments, enabling project managers to identify safety issues proactively and ensure compliance with regulations. By analyzing visual data collected from job sites, machine learning algorithms can detect potential hazards and compliance gaps in real time, significantly reducing the likelihood of accidents and costly delays. The research provides insights into various machine learning techniques applicable to visual risk assessments, highlights case studies demonstrating their effectiveness, and discusses the implications for project management practices. The findings suggest that adopting machine learning-driven visual assessments can lead to safer and more efficient project outcomes.

Downloads

Download data is not yet available.

References

Gayam, Swaroop Reddy. "Deep Learning for Predictive Maintenance: Advanced Techniques for Fault Detection, Prognostics, and Maintenance Scheduling in Industrial Systems." Journal of Deep Learning in Genomic Data Analysis 2.1 (2022): 53-85.

George, Jabin Geevarghese. "Augmenting Enterprise Systems and Financial Processes for transforming Architecture for a Major Genomics Industry Leader." Journal of Deep Learning in Genomic Data Analysis 2.1 (2022): 242-285.

Yellepeddi, Sai Manoj, et al. "AI-Powered Intrusion Detection Systems: Real-World Performance Analysis." Journal of AI-Assisted Scientific Discovery 4.1 (2024): 279-289.

Nimmagadda, Venkata Siva Prakash. "Artificial Intelligence for Supply Chain Visibility and Transparency in Retail: Advanced Techniques, Models, and Real-World Case Studies." Journal of Machine Learning in Pharmaceutical Research 3.1 (2023): 87-120.

Putha, Sudharshan. "AI-Driven Predictive Maintenance for Smart Manufacturing: Enhancing Equipment Reliability and Reducing Downtime." Journal of Deep Learning in Genomic Data Analysis 2.1 (2022): 160-203.

Sahu, Mohit Kumar. "Advanced AI Techniques for Predictive Maintenance in Autonomous Vehicles: Enhancing Reliability and Safety." Journal of AI in Healthcare and Medicine 2.1 (2022): 263-304.

Kondapaka, Krishna Kanth. "AI-Driven Predictive Maintenance for Insured Assets: Advanced Techniques, Applications, and Real-World Case Studies." Journal of AI in Healthcare and Medicine 1.2 (2021): 146-187.

Kasaraneni, Ramana Kumar. "AI-Enhanced Telematics Systems for Fleet Management: Optimizing Route Planning and Resource Allocation." Journal of AI in Healthcare and Medicine 1.2 (2021): 187-222.

Pattyam, Sandeep Pushyamitra. "Artificial Intelligence in Cybersecurity: Advanced Methods for Threat Detection, Risk Assessment, and Incident Response." Journal of AI in Healthcare and Medicine 1.2 (2021): 83-108.

Alluri, Venkat Rama Raju, et al. "Automated Testing Strategies for Microservices: A DevOps Approach." Distributed Learning and Broad Applications in Scientific Research 4 (2018): 101-121.

Machireddy, Jeshwanth Reddy. "Assessing the Impact of Medicare Broker Commissions on Enrollment Trends and Consumer Costs: A Data-Driven Analysis." Journal of AI in Healthcare and Medicine 2.1 (2022): 501-518.

Downloads

Published

18-11-2023

How to Cite

[1]
E. Johnson, “Machine Learning-Driven Visual Risk Assessments for Safety and Compliance in Project Management”, Distrib Learn Broad Appl Sci Res, vol. 9, pp. 423–428, Nov. 2023, Accessed: Nov. 23, 2024. [Online]. Available: https://dlabi.org/index.php/journal/article/view/153

Similar Articles

21-30 of 171

You may also start an advanced similarity search for this article.