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Ageing infrastructure is becoming an increasing challenge as a result of deterioration and greater loading demands. Modern cities were built on top of complex underground infrastructure networks many of which are still in-service beyond their design life. The safety assessment of underground structures is of utmost importance to avoid catastrophic failures and develop cost-effective renewal and rehabilitation strategies. However, the lack of design documentation and absence of data on the level of structural deterioration make determination of current structural capacity a challenge. This paper presents a probabilistic based assessment framework for underground utility service pits using Bayesian updating technique, which is used to refine the probabilistic distribution of material properties from the prior distribution constructed using published data. A case study of an underground pit located in Central Melbourne is provided. Extensive experimental testing was conducted to characterise the material properties and a full-scale masonry wall was tested to understand the failure mode due to earth pressure and traffic load. The test data was used in strength prediction models to achieve a more accurate estimate for wall capacity. Further, the strength degradation models were integrated to develop the time-dependent material models, which were eventually used to compute reliability index.

Structural assessment of underground utility services pit using Bayesian inference