Fire risk assessment and management practices for university laboratories involving fire based on the FAHP-FCE model

University laboratories involving fire are indispensable infrastructures for engineering education and scientific research. These laboratories are typically characterized by high-temperature operations, open-flame processes, combustible materials, high-power electrical equipment, and complex experimental procedures, resulting in significantly higher fire risks compared to conventional laboratory settings. Existing fire risk assessment methods for university laboratories often rely heavily on historical accident data, fail to adequately consider emerging and compound risk scenarios, and have limited adaptability to diverse laboratory functions and operational modes. To address these challenges, this study develops a systematic, generalizable, and practical fire risk assessment approach specifically designed for university laboratories facing fire hazards. By integrating the fuzzy analytic hierarchy process (FAHP) with the fuzzy comprehensive evaluation (FCE) method, key fire risk factors are systematically identified, and their relative importance is quantitatively determined, thereby providing a scientific basis for fire risk classification, graded control, and safety management decision-making in university laboratories.

This study establishes a comprehensive fire risk assessment framework for university laboratories by synthesizing relevant national policies, safety regulations, technical standards, representative accident analyses, and expert insights to construct a fire risk evaluation indicator system. The proposed system consists of four primary indicators, namely direct disaster-causing factors, vulnerability of the exposed body, prevention and management capability, and special scenario risks, and is further detailed into 26 secondary indicators. These indicators encompass essential risk elements, including ignition source control, equipment condition, experimental environment characteristics, human factors, management mechanisms, and scenario-specific hazards. The FAHP is employed to determine the weights of each indicator. Expert judgment data are collected through structured questionnaires administered to specialists with extensive experience in laboratory safety management, and fuzzy complementary judgment matrices are constructed to address uncertainty and subjectivity in expert assessments. The FCE method is then applied to quantitatively evaluate laboratory fire risks. Based on expert scoring, membership functions and fuzzy relation matrices for each indicator are established. The study then conducts a weighted fuzzy synthesis operation to obtain comprehensive membership vectors and performs defuzzification using the weighted average method to determine the final fire risk level and overall risk score. The Disaster Prevention and Mitigation Laboratory of Qingdao University of Technology is selected as a case study to validate the feasibility and applicability of the proposed model.

The weighting results indicate that direct disaster-causing factors and prevention and management capabilities are critical in determining overall fire risk, highlighting the importance of ignition source control, electrical safety, equipment condition, and management enforcement on laboratory fire safety. The evaluation results reveal that the case laboratory achieves an overall score of 91.38, corresponding to a fire risk level classified as “no risk.” This assessment aligns closely with the laboratory’s actual safety performance, reflecting a high level of safety in fire compartmentation design, firefighting facility configuration, personnel access control, and implementation of safety responsibility systems. However, some indicators, such as fire risk awareness among faculty and students and the closed-loop management of hazard identification and rectification, received relatively lower scores, suggesting a need for improvement in personnel awareness and enhanced safety management practices.

The FAHP-FCE model effectively differentiates among various fire risk levels and accurately identifies critical risk factors. It demonstrates strong scientific validity, practical operability, and the potential for wide application, providing robust technical support for graded fire risk control and safety management in university laboratories. The findings of this study not only contribute to improving fire risk prevention and control in university laboratories but also serve as valuable references for advancing safety governance in research facilities at higher education institutions.