• Mr.S.P.Sankar
• U VINITH

Addressing safety incidents remains a persistent challenge in construction, requiring constant attention from project management teams. Despite prioritizing accident prevention, the dynamic nature of projects and varying management expertise contribute to ongoing safety mishaps, disrupting schedules and inflating costs, often with tragic consequences. To manage this, it's crucial to identify and reduce risk factors throughout the project lifecycle, considering their complex interplay. A nuanced understanding is required, navigating uncertainty to anticipate and address potential risks at every stage. Integrating insights from academia and experience, leveraging advanced methodologies and technologies, is becoming increasingly important for effective safety management. Traditionally, safety risk analysis has been limited, but a novel approach combining cascading effects and machine learning offers a more thorough and objective understanding. This approach recognizes the ripple effects of incidents and employs machine learning to extract insights from vast data sets, enabling a more accurate depiction of safety landscapes and informing better risk mitigation strategies. Overall, this combined approach represents a significant advancement, promising safer environments and more successful project outcomes through enhanced risk identification, assessment, and mitigation.

Risk factors, Heterogeneous data Machine learning, Cascading effect

"Integrative Analysis of Safety Risks in Construction Projects: Merging Cascading Effects and Machine Learning Methodologies", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.o23-o45, May-2024, Available :http://www.jetir.org/papers/JETIR2405F02.pdf

"Integrative Analysis of Safety Risks in Construction Projects: Merging Cascading Effects and Machine Learning Methodologies", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppo23-o45, May-2024, Available at : http://www.jetir.org/papers/JETIR2405F02.pdf