Stochastic Analysis of Network-Level Bridge Maintenance Needs Using Latin Hypercube Sampling

Abstract

The deterioration of bridge infrastructure along with diminishing funding resources necessitates reliable planning for future budget needs to maintain bridges at an acceptable level of performance. Although existing methodologies do consider uncertainties in individual bridge deterioration, there is a gap in the development of a network-level budget planning framework incorporating such stochastic phenomena. In this paper, a network-level needs-prediction simulation framework is proposed, that constructs confidence intervals for the output within a specific precision and significance level. Additionally, as the vast network size sets a demand for high computational effort, the Latin hypercube sampling technique is introduced to reduce the inherent simulation variance and decrease the number of replications needed. Ultimately, the applicability of the proposed methodology is demonstrated using a case study pertaining to a network of structures comprising bridges and culverts within the Austin District of the Texas DOT (TxDOT). The results confirm the capability of the proposed methodology in providing meaningful budget confidence interval estimates at the network level by using a significantly reduced number of computational resources.