TY - JOUR
T1 - Dynamic Bayesian network for durability of reinforced concrete structures in long-term environmental exposures
AU - Guo, Hongyuan
AU - Dong, You
N1 - Funding Information:
Funding: The study has been supported by Research Grants Council of the Hong Kong Special Administrative Region, China (No. T22-502/18-R and No. PolyU 15219819) and Natural Science Foundation of China (Grant No. 52078448).
Publisher Copyright:
© 2022
PY - 2022/12
Y1 - 2022/12
N2 - Reinforced concrete (RC) structures under the marine environment may be subjected to chloride-induced corrosion of reinforcement, which significantly impacts the structural serviceability and reliability and further affects the sustainability and development of society. However, most of the existing durability assessment methods for RC structures only address their static and deterministic durability prediction and assessment at the design stage given the constant environment, ignoring the influences of stochastic environmental effects, uncertainties in structural properties, and inspection results. To this end, this paper proposes a dynamic Bayesian network (DBN) based durability assessment framework combined with a deterioration model that considers random changes in environmental parameters, convective chloride ion transport, and corrosion-induced cracking of concrete. In this framework, two-dimensional chloride transport and its influences on the durability deterioration assessment are concerned and achieved using the finite difference method. Besides, to reduce the deviations in probabilistic evaluation, the good-lattice-point-set-partially stratified-sampling (GLP-PSS) method is employed to establish a DBN framework. The proposed DBN framework is used for sensitivity analysis through a real-world example to examine the effects of the environmental model, chloride transport mode, and inspection results of concrete crack on durability assessment.
AB - Reinforced concrete (RC) structures under the marine environment may be subjected to chloride-induced corrosion of reinforcement, which significantly impacts the structural serviceability and reliability and further affects the sustainability and development of society. However, most of the existing durability assessment methods for RC structures only address their static and deterministic durability prediction and assessment at the design stage given the constant environment, ignoring the influences of stochastic environmental effects, uncertainties in structural properties, and inspection results. To this end, this paper proposes a dynamic Bayesian network (DBN) based durability assessment framework combined with a deterioration model that considers random changes in environmental parameters, convective chloride ion transport, and corrosion-induced cracking of concrete. In this framework, two-dimensional chloride transport and its influences on the durability deterioration assessment are concerned and achieved using the finite difference method. Besides, to reduce the deviations in probabilistic evaluation, the good-lattice-point-set-partially stratified-sampling (GLP-PSS) method is employed to establish a DBN framework. The proposed DBN framework is used for sensitivity analysis through a real-world example to examine the effects of the environmental model, chloride transport mode, and inspection results of concrete crack on durability assessment.
KW - Durability assessment
KW - Dynamic Bayesian Network
KW - Environmental actions
KW - Reinforced concrete (RC) structures
UR - http://www.scopus.com/inward/record.url?scp=85139351858&partnerID=8YFLogxK
U2 - 10.1016/j.engfailanal.2022.106821
DO - 10.1016/j.engfailanal.2022.106821
M3 - Journal article
AN - SCOPUS:85139351858
SN - 1350-6307
VL - 142
JO - Engineering Failure Analysis
JF - Engineering Failure Analysis
M1 - 106821
ER -