Learning loads on in‐service underground infrastructure with a trans‐dimensional Bayesian inversion method

Earth Pressure and Internal Forces of Tunnel Lining in Jet Grouting Reinforced Mud Stratum

Statistical mediation analysis is used to investigate intermediate variables in the relation between independent and dependent variables. Causal interpretation of mediation analyses is challenging because randomization of subjects to levels of the independent variable does not rule out the possibility of unmeasured confounders of the mediator to outcome relation. Furthermore, commonly used frequentist methods for mediation analysis compute the probability of the data given the null hypothesis, which is not the probability of a hypothesis given the data as in Bayesian analysis. Under certain assumptions, applying the potential outcomes framework to mediation analysis allows for the computation of causal effects, and statistical mediation in the Bayesian framework gives indirect effects probabilistic interpretations. This tutorial combines causal inference and Bayesian methods for mediation analysis so the indirect and direct effects have both causal and probabilistic interpretations. Steps in Bayesian causal mediation analysis are shown in the application to an empirical example.

A global sensitivity analysis and Bayesian inference framework was developed for improving the parameterization and predictability of a monthly time step process‐based biogeochemistry model. Using a Latin Hypercube sampler and an existing Terrestrial Ecosystem Model (TEM), a set of 500,000 Monte Carlo ensemble simulations was conducted for a black spruce forest ecosystem. A global sensitivity analysis was then conducted to identify the key model parameters and examine the interaction structures among TEM parameters. Bayesian inference analysis was also performed using these ensemble simulations and eddy flux data of carbon, latent heat flux, and MODIS gross primary production (GPP) to reduce the uncertainty of parameter estimation and prediction of TEM. We found that (1) the simulated carbon fluxes are mostly affected by parameters of the maximum rate of photosynthesis (CMAX), the half‐saturation constant for CO2 uptake by plants (kc), the half‐saturation constant for Photosynthetically Active Radiation used by plants (ki), and the change in autotrophic respiration due to 10°C temperature increase (RHQ10); (2) the effect of parameters on seasonal carbon dynamics varies from one parameter to another during a year; (3) to well constrain the uncertainties of TEM predictions and parameters using the Bayesian inference technique, at least two different fluxes of NEP, GPP, and ecosystem respiration (RESP) are required; and (4) different assumptions of the error structures of the flux data used in the Bayesian inference analysis result in different uncertainty bounds of the posterior parameters and model predictions. We further found that, using the Bayesian framework and eddy flux and satellite data, the uncertainty of simulated carbon fluxes has been remarkably reduced. The developed global sensitivity analysis and Bayesian framework could further be used to analyze and improve the predictability and parameterization of relatively coarse time step biogeochemistry models when the eddy flux and satellite data are available for other terrestrial ecosystems.

Effect of nearby ground structures on the seismic response of underground structures in saturated sand

Worldwide we see that the construction industry is expanding, requiring new directions, new perspectives that can help reduce time, cost, and make transportation easy, safe, and affordable. For decades now, most of the large cities have completed their surface infrastructure. It has become urgent to address their issues for overpopulated cities where nowadays all infrastructure is overwhelmed, these issues must be addressed, solved and have vision to build underground infrastructure. Developed countries are focused on expanding their infrastructure for road systems, subway network, railway, storm, and sanitary systems. The emergency for underground infrastructure development requires more large-scale projects to be built and it is becoming more crucial building tunnels/underground structures for the future than ever before. Engineering focus, scientific searches are looking to develop their ideas for designing and delivering project underground, but government, agencies and engineers are concerned about the safety, durability, functionality, and the lifetime of this structures planned to be functional for decades. To address all this concerns this study provides information of how to identify the risk on tunnels and underground structures by capturing data from the beginning phases of construction, to analyze, evaluate and produce bulletins and engineering reports through convergences and monitoring. Convergences are the key factor on development of infrastructure underground as it is the only way to explore and analyze the rock mass disturbance during excavation. Convergences and monitoring in infrastructure are the safety coefficient for building underground, preventing accidents, and assessing real risks associated with tunnel/mine works and ensuring progress of the construction in underground structures. This study delves into the engineering role of convergence monitoring, during construction activities on project excavated using New Austrian Tunnelling method and Sequential Excavation Method. The primary objective of convergence monitoring is to gather critical information on ground movements and disturbances, thereby enhancing safety measures during tunnel construction. The monitoring process serves as an early warning system offering evidence of the real risks associated with underground infrastructure, bringing results and engineering data to be used for the design as key coefficient for structural design, type of material, type and strength of the concrete, rebars, concrete mix design. By using the convergence and monitoring system on underground infrastructure this study represents information that can contribute to risk assessment, structural analysis, and the lifetime of a project.

Process monitoring plays an important role in ensuring the safety and stable operation of equipment in a large-scale process. This paper proposes a novel data-driven process monitoring framework, termed the ensemble adaptive sparse Bayesian transfer learning machine (EAdspB-TLM), for nonlinear fault diagnosis. The proposed framework has the following advantages: Firstly, the probabilistic relevance vector machine (PrRVM) under Bayesian framework is re-derived so that it can be used to forecast the plant operating conditions. Secondly, we extend the PrRVM method and assimilate transfer learning into the sparse Bayesian learning framework to provide it with the transferring ability. Thirdly, the source domain (SD) data are re-enabled to alleviate the issue of insufficient training data. Finally, the proposed EAdspB-TLM framework was effectively applied to monitor a real wastewater treatment process (WWTP) and a Tennessee Eastman chemical process (TECP). The results further demonstrate that the proposed method is feasible.

This paper reports the structural health monitoring benchmark study results for the Canton Tower using Bayesian methods. In this study, output-only modal identification and finite element model updating are considered using a given set of structural acceleration measurements and the corresponding ambient conditions of 24 hours. In the first stage, the Bayesian spectral density approach is used for output-only modal identification with the acceleration time histories as the excitation to the tower is unknown. The modal parameters and the associated uncertainty can be estimated through Bayesian inference. Uncertainty quantification is important for determination of statistically significant change of the modal parameters and for weighting assignment in the subsequent stage of model updating. In the second stage, a Bayesian model updating approach is utilized to update the finite element model of the tower. The uncertain stiffness parameters can be obtained by minimizing an objective function that is a weighted sum of the square of the differences (residuals) between the identified modal parameters and the corresponding values of the model. The weightings distinguish the contribution of different residuals with different uncertain levels. They are obtained using the Bayesian spectral density approach in the first stage. Again, uncertainty of the stiffness parameters can be quantified with Bayesian inference. Finally, this Bayesian framework is applied to the 24-hour field measurements to investigate the variation of the modal and stiffness parameters under changing ambient conditions. Results show that the Bayesian framework successfully achieves the goal of the first task of this benchmark study.

Urban stormwater management increasingly relies on decentralized infiltration systems to mitigate the adverse effects of soil sealing. At the catchment scale, the hydrological impacts of such practices however remain poorly understood, especially in shallow aquifers settings where interactions between infiltrated volumes, groundwater and underground infrastructures can occur. This study examines the hydrological response of a small shallow groundwater urban area to the implementation of stormwater infiltration, based on numerical modeling. The influence of climate, soil conditions, hydrogeology, urban context, and infiltration strategy is assessed. Results show that the concentration of runoff within infiltration systems causes a large increase of groundwater recharge, often leading to water‐table rise. Where initial groundwater depth or low subsurface transmissivity is low, seepage into sewer pipes and underground drainage structures increases and potentially undermines the runoff volume reduction benefits of infiltration systems. Occasionally, an increase in transpiration from green spaces may also occur. For remaining settings, the additional recharge is mainly dissipated by downstream groundwater flow, without generating notable interactions with the surface or underground structures. Overall, this study illustrates the variety of stormwater infiltration outcomes and demonstrates the inability of this strategy, alone, to restore pre‐development hydrological conditions.

Logit, probit and complementary log-log models are the most widely used models when binary dependent variables are available. Conventionally, these models have been frequentists. This paper aims to demonstrate how such models can be implemented relatively quickly and easily from a Bayesian framework using Gibbs sampling Markov chain Monte Carlo simulation methods in WinBUGS. We focus on the modeling and prediction of Down syndrome (DS) and Mental retardation (MR) data from an observational study at Kuwait Medical Genetic Center over a 30-year time period between 1979 and 2009. Modeling algorithms were used in two distinct ways; firstly, using three different methods at the disease level, including logistic, probit and cloglog models, and, secondly, using bivariate logistic regression to study the association between the two diseases in question. The models are compared in terms of their predictive ability via R2, adjusted R2, root mean square error (RMSE) and Bayesian Deviance Information Criterion (DIC). In the univariate analysis, the logistic model performed best, with R2 (0.1145), adjusted R2 (0.114), RMSE (0.3074) and DIC (7435.98) for DS, and R2 (0.0626), adjusted R2 (0.0621), RMSE (0.4676) and DIC (23120) for MR. In the bivariate case, results revealed that 7 and 8 out of the 10 selected covariates were significantly associated with DS and MR respectively, whilst none were associated with the interaction between the two outcomes. Bayesian methods are more flexible in handling complex non-standard models as well as they allow model fit and complexity to be assessed straightforwardly for non-nested hierarchical models.

With the development of subway construction in China, the scale of exploitation and utilization of underground space is becoming more and more larger. The dynamic characteristics of soil-above ground structure must be changed with the existence of underground structure, and therefore, the seismic ability of above ground structures will be influenced. The calculation models for soil-underground frame structure-above ground structure interaction system are established based on finite element method. The mutual influencing on seismic response of underground structure and above ground structure are calculated and analyzed. The analysis showed that above ground frame structure has little influence on the seismic response of underground structure, while the underground structure has great influence on the seismic of above ground structure. The results in the paper can provide references for the seismic design of underground and above ground structures.

An efficient Bayesian inversion method for seepage parameters using a data-driven error model and an ensemble of surrogates considering the interactions between prediction performance indicators

<p><span>In March 2011, large amount of radionuclides were released into the atmosphere throughout the Fukushima Daiichi nuclear disaster. This massive and very complex release, characterized by several peaks and wide temporal variability, lasted for more than three weeks and is subject to large uncertainties. The assessment of the radiological consequences </span><span>due to the exposure during the emergency phase </span><span>is highly dependent on </span><span>the challenging estimate of the source term.</span><span><br><br>Inverse modelling techniques have proven to be efficient in assessing the source term of radionuclides. Through Bayesian inverse methods, distributions of the variables describing the release such as the duration and the magnitude as well as the observation error can be drawn in order to get a complete characterization of the source.</span></p><p><span><br>For complex situations involving releases from several reactors, the temporal evolution of the release may be as difficult to reconstruct as its magnitude. The source term or function of the release is described in the inverse problem as a vector of release rates. Thus, the temporal evolution of the release appears in the definition of the time steps where the release rate is considered constant. The search for the release variability therefore corresponds to the search for the number and length of these successive time steps.</span></p><p><span>In this study, we propose to tackle the Bayesian inference problem through sampling Monte Carlo Markov Chains methods (MCMC), and more precisely the Reversible-Jump MCMC algorithm.<br>The Reversible-Jump MCMC method is a transdimensional algorithm which allows to reconstruct the time evolution of the release and its magnitude in the same procedure.<br></span></p><p><span>Furthermore, to better quantify uncertainty </span><span>linked to the reconstructed source term</span><span>, different approaches are proposed and applied. First, we discuss how to choose the likelihood and propose several distributions. Then, different approaches to model the likelihood covariance matrix are defined.</span></p><p><span><br>These different methods are applied to characterize the </span><sup><span>137</span></sup><span>Cs Fukushima source term. We present </span><span><em>a posteriori</em></span><span> distributions enable to assess the source term and the temporal evolution of the release, to quantify the uncertainties associated to the observations and the modelling of the problem.</span></p>

Soil-structure interaction of integral abutments

Guages used in testing materials

How do tall buildings affect seismic earth pressures on their basement walls?