Trans-Dimensional Statistical Inversion for Estimating Earth Pressures on Underground Structures

Identification of earth pressures acting on in-service underground structures is critical for their health monitoring and performance prediction. Given that the extensive deployment of sensors on poorly performing structures to measure pressure incurs high costs and presents technical challenges, the inversion of these pressures from easily observed deformation data has become increasingly desirable. However, traditional pressure inversion methods require subjective assumptions about the complexity of the pressure, necessitating extensive engineering judgment that may not be confidently applied in practice. To address this challenge, this paper proposes a trans-dimensional Bayesian method for pressure inversion. This method simultaneously incorporates pressure complexity and quantities into the inversion by parameterizing a set of previously unknown parameters, where the number of parameters itself is unknown. A recorded case study is presented for illustration and verification. It is found that the proposed method yields good inversion results on the pressures on a diaphragm wall, whereas traditional methods lead to poor inversion results due to inadequate assumptions. These outcomes highlight the advancements of the proposed method. Lastly, deficiencies and future extensions are discussed in the conclusion.