Fire incidents in operating
highway tunnels can cause sustained deterioration of the tunnel lining
structure. To accurately predict the evolving risk associated with lining
degradation after such hazards, a dynamic Bayesian network (DBN) model is
developed to assess the long-term
structural safety of highway tunnel linings. First, based on the
characteristics of fire-induced damage, a safety
evaluation index system is established, yielding a corresponding weight table.
Then, a DBN-based evaluation model is constructed, and
the risk grades and classification values of postfire lining safety are
proposed. Prior probabilities are estimated from field detection results, while
the transfer probabilities of each evaluation index are derived assuming an
exponential distribution for tunnel service life. The model is applied to the
Shiyashan tunnel. Results show extensive concrete spalling near the center of
the fire zone, with a maximum depth of 40 mm and a concrete strength decay rate of 0.10 － 0.12. The safety risk value of the lining increases with service
time, reaching 0.81 at 35 years. The progressive deterioration of concrete and
development of cracks induced by the fire continuously impact structural
safety, resulting in a rising risk value over time.
ZHANG Yani
,
LIU Xiabing
,
ZHANG Yanlong
,
CHEN Jinyu
,
ZHOU Yu
. Long-Term Safety Status Assessment of Operational Highway Tunnel Lining After Breaking Fire Hazard Based on Dynamic Bayesian Network[J]. Tunnel Construction, 2025
, 45(6)
: 1210
-1218
.
DOI: 10.3973/j.issn.2096-4498.2025.06.016
