Tunneling operations in modern construction demand meticulous evaluation of their impact on nearby structures. A primary concern is the potential for soil subsidence, which could damage adjacent buildings. Complicating matters is the challenge of accurately modeling such settlement and the consequent damage, a critical process for informed decision-making during construction projects. By employing Bayesian updating, we refine our models by acquiring posterior distributions for key parameters. We put forth an analytical method for profiling ground settlement and follow this by calculating the strain on an equivalent beam, which serves as a proxy for building damage. This results in a distribution of strain values that allows for an assessment of how varying certain length parameters affects the probability of maintaining a safe distance between the tunneling activities and the surrounding buildings. With this probabilistic approach, one can propose a recommended safety distance as a guideline for construction practices.

Reliable predictions of time-dependent diaphragm wall deflections in deep excavations in soft soils are crucial for managing potential damage to the surrounding environment. Bayesian updating offers a rational method for refining these predictions by using monitoring data. The inconsistency in monitoring data necessitates an examination of the impact of using different datasets on Bayesian updating. This paper presents a Bayesian updating of time-dependent deflections of diaphragm walls in deep excavations in soft soils using different datasets. The soft soil creep model is utilized to simulate the time-dependent behavior of soil. Bi-directional longshort memory neural networks are employed as surrogate models. Different updating strategies with varying numbers of data in the datasets are adopted for Bayesian updating and illustrated with the Taipei National Enterprise Center project. The results show that incorporating more monitoring data in the datasets for Bayesian updating does not guarantee better predictions unless the consistency of the monitoring data used is ensured. Additionally, the Bayesian updating process more accurately predicts short-term deflections than long-term ones, likely due to the higher consistency in short-term construction processes. It is advisable to review the construction processes to ensure the consistency of the monitoring data before selecting the appropriate dataset.

This paper introduces a novel framework for developing reliable probabilistic predictive corrosion growth models for buried steel pipelines using pipeline inspection data. The framework adopts a power -law function of time model formulation, accounting for nonconstant damage growth rates, and considers the correlation between defect depth and length growth models. The proposed framework explicitly incorporates local influential soil properties in the model formulation; thus, it requires no segmentation and homogenous defect growth assumption and provides defect -specific growth models. The framework is applicable regardless of the availability of matched or non -matched defect data. For corrosion initiation time estimation, two different approaches are proposed: one is to use a Poisson process to account for defect occurrence, which can also predict newly generated defects since the last inspection, and the other is to use multivariate linear regression of soil and pipe properties. The statistics of unknown model parameters are assessed using a Bayesian updating framework in which the model error can be incorporated. The proposed framework is applied using two different sets of data: one set of inline inspection (ILI) data and one set of field excavation data. A case study is conducted, where timedependent system reliability of an in-service pipeline is assessed considering small leak and burst failure modes using the developed defect growth models. The impact of the growth model accuracy on the probability of failure is investigated, and the importance analysis is performed to identify the most influential random variables to the probability of failure.

Applications of Bayesian updating commonly treat soil parameters as random variables. A significant issue with this is that soil parameters are highly subjective. Therefore, using traditional parameter-based models, Bayesian analysis starts from a subjective prior and it is unclear how this may influence the overall results of a study. In this paper, Bayesian updating is combined with a data-driven method, known as CRACA (i.e., CReep And Consolidation Analysis), for predicting the settlement of embankments on soft soil. Importantly, the method directly ingests measured oedometer data and, therefore, avoids the subjectivity involved in parameter selection. Because parameters are not used, scaling factors are introduced that account uncertainty associated with the laboratory measurements and the automated interpretation process. These factors have an initial value of unity (returning the prior) and are updated in a Bayesian framework as settlement monitoring data are revealed over time to improve future forecasts. The model was applied to an embankment case history and was shown to result in a rapid improvement in the accuracy and a narrowing of the 95% confidence interval as settlement monitoring data are revealed to the model.

Recently, the application of Bayesian updating to predict excavation-induced deformation has proven successful and improved prediction accuracy significantly. However, updating the ground settlement profile, which is crucial for determining potential damage to nearby infrastructures, has received limited attention. To address this, this paper proposes a physics-guided simplified model combined with a Bayesian updating framework to accurately predict the ground settlement profile. The advantage of this model is that it eliminates the need for complex finite element modeling and makes the updating framework user-friendly. Furthermore, the model is physically interpretable, which can provide valuable references for construction adjustments. The effectiveness of the proposed method is demonstrated through two field case studies, showing that it can yield satisfactory predictions for the settlement profile. (c) 2024 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

Numerical models play a crucial role in the study and understanding of cultural heritage structures, serving as valuable tools for predicting their behavior under diverse and prospective scenarios. They are however affected by various uncertainties, which impact can be mitigated through the calibration of model parameters. For heritage structures, where testing is usually restricted to the use of non-destructive techniques, and often unable to directly assess the inherent heterogeneity of the materials, a calibration approach can prove particularly useful to obtain a working model. This work applies a Bayesian model updating procedure to material-related uncertainties affecting a recently developed finite element model of the Leaning Tower of Pisa also comprising the underlying soil layers. The procedure takes advantage of literature modal data of the Tower and uses a general Polynomial Chaos Expansion-based surrogation of the model to evaluate sensitivity and ease the computational burden that comes with the probabilistic framing of the updating problem. The results represent the first probabilistic model-based assessment of material uncertainties in a three-dimensional finite element model of the Leaning Tower of Pisa. They shed some light into the value of specific modal information, while the use of analytical surrogation paves the way for the future design of a real-time updating procedure for monitoring and damage detection.