Fire risk assessment method for ancient buildings in the palace museum based on spatio-temporal data

doi:10.7523/j.ucas.2024.075

Abstract

Abstract: Fire poses the greatest threat to the cultural heritage of timber buildings. The fire risk assessment of ancient buildings in the Palace Museum, the world's largest remaining ancient palace complex, aids in the management, protection, and fire prevention efforts of the heritage site. This study suggests creating a dataset of fire risk factors, taking into account the hazard of causal factors, the sensitivity of disaster-bearing bodies, and loss control, reflecting the temporal and spatial characteristics of the Palace Museum's ancient buildings. Utilizing the entropy-weighted TOPSIS algorithm, a fire risk index calculation model was developed to determine the relative fire risk among ancient buildings. This assessment was performed using the monthly spatio-temporal factor data of the Palace Museum from 2014 to 2019. The findings reveal that the comprehensive fire risk index of the Palace Museum ranges from 0.23 to 0.58, with the central axis area exhibiting the highest fire risk, especially in July and August. Hazard and sensitivity indices are increased in the central axis, northwestern, and northeastern zones, whereas the control index is higher in the central and southern parts of the central axis area as well as in the northwestern and northeastern zones. These areas are thus more susceptible to fire hazards, more impacted by fire disturbances, and possess a stronger response and coping capacity post-disturbance. In conjunction with GIS, the comprehensive time-series fire risk map of single ancient buildings in the Palace Museum offers valuable insights for relevant departments to develop fire management plans and allocate resources for ancient buildings.

Key words: ancient buildings, fire, risk assessment, entropy, GIS

CLC Number:

K909

LEI Yating, SHEN Zhanfeng, YANG Xinwei, WANG Bo, LIU Jinzhou, KOU Wenqi, ZHANG Chi. Fire risk assessment method for ancient buildings in the palace museum based on spatio-temporal data[J]. Journal of University of Chinese Academy of Sciences, DOI: 10.7523/j.ucas.2024.075.

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