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@article{182701,
        author = {Tejas Admane and Dr. Sachin Admane},
        title = {Evaluating Construction Site Safety: Emergency Preparedness Using Hierarchical Fuzzy Inference Systems},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {2879-2890},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182701},
        abstract = {Emergency preparedness is a critical component of safety management in the construction industry, where dynamic site conditions and complex organisational structures present persistent risks. This study proposes a hierarchical fuzzy logic framework to assess emergency preparedness on construction sites, integrating both subjective perceptions and objective indicators gathered through a structured survey of 500 construction professionals. Survey responses, collected across a diverse range of roles and experience levels, were mapped onto fuzzy membership functions and analysed using a hierarchical inference system that groups preparedness factors into logical subsystems. The results indicate that while awareness, training, and resource provision are relatively strong across the sector, significant gaps remain in communication, management support, and the mitigation of barriers to effective emergency response. Correlation analysis further highlights the interdependent nature of preparedness dimensions. The hierarchical fuzzy logic approach demonstrated effectiveness in accommodating the uncertainty and subjectivity inherent in safety assessments, offering a flexible and interpretable framework for ongoing evaluation. The findings underscore the need for holistic, systems-oriented strategies to enhance emergency preparedness in construction, with particular emphasis on strengthening leadership, communication, and practical implementation of safety culture. Recommendations for future research include the incorporation of objective performance data and adaptive modelling techniques to further refine preparedness assessment.},
        keywords = {},
        month = {July},
        }