RELIABILITY ANALYSIS OF RHS STEEL TRUSSES JOINTS BASED ON THE P-BOXES APPROACH

Reliability is one of the main indicators of structural elements mechanical safety. The choice of stochastic models is an important task in reliability analysis for describing the variability of random variables with aleatory and epistemic uncertainty. The article proposes a method for the reliability analysis of RHS (rectangular hollow sections) steel truss joints based on p-boxes approach. The p-boxes consist of two boundary distribution functions that create an area of possible distribution functions of a random variable. The using of p-boxes make possible to model random variables without making unreasonable assumptions about the exact cumulative distribution functions (CDF) or the exact values of the CDF parameters. The developed approach allows to give an interval estimate of the non-failure probability of the truss joints, which is necessary for a comprehensive (system) reliability analysis of the entire truss.

Uncertainty and Sensitivity Analysis of Significant Parameters for Superlarge Diameter Shield Excavation

Excavation of a superlarge diameter tunnel by tunnel boring machine (TBM) is different from that of a shield tunnel with normal dimension, in which the control system of the superlarge TBM is very complicated and difficult to operate. Hence, it is very important to focus on the control and management of significant parameters to ensure excavation stability under uncertainty. In this paper, we (i) utilize a BIM-based big data platform (BIM-BDP) to manage the essential construction data of tunnel project in digital format; (ii) adopt the global sensitivity analysis (SA) to recognize significant parameters for shield excavation based on polynomial chaos expansion (PCE)–extended Fourier amplitude sensitivity test (eFAST) model; and (iii) employ the uncertainty analysis (UA) to discover the correlation between significant parameters from the data of the BIM-BDP. This research contributes to (i) the body of knowledge of proposing a more appropriate research methodology that can cope with aleatory and epistemic uncertainty and support uncertainty and sensitivity analysis (UA/SA) processes based on data from BIM-BDP and (ii) the state of practice by providing a data-driven surrogate model to simulate system behaviors of shield excavation with high reliability and to reduce dependency on domain experts. Here, we pay close attention to the most influential parameters that require priority parameter control, which can help administrators optimize the management of shield parameters during tunnel excavation.

Exposure forecasting for seismic risk estimation: Application to Costa Rica

This study proposes a framework to forecast the spatial distribution of population and residential buildings for the assessment of future disaster risk. The approach accounts for the number, location, and characteristics of future assets considering sources of aleatory and epistemic uncertainty in several time-dependent variables. The value of the methodology is demonstrated at the urban scale using an earthquake scenario for the Great Metropolitan Area of Costa Rica. Hundreds of trajectories representing future urban growth were generated using geographically weighted regression and multiple-agent systems. These were converted into exposure models featuring the spatial correlation of urban expansion and the densification of the built environment. The forecasted earthquake losses indicate a mean increase in the absolute human and economic losses by 2030. However, the trajectory of relative risk is reducing, suggesting that the long-term enforcement of seismic regulations and urban planning are effectively lowering seismic risk in the case of Costa Rica.

Uncertainty Evaluation of Water Inrush in Karst Tunnels Based on Epistemic Uncertainty with Possibility Theory

In the risk assessment of water inrush in karst tunnel, it is most important to provide an available theoretical model for qualifying the epistemic uncertainties due to a lack of knowledge and information. Firstly, a mechanical model dependent on geology is introduced associating with four parameters, i.e., the elastic modulus E, the Poisson ratio μ, the water differential pressure q, and the tunnel radius a. Then, a mathematical model representing epistemic uncertainty is represented with probability theory and possibility theory. The methodology was computerized to calculate the distribution of the margin and uncertainty and then to determine the ratio of “margin/uncertainty.” Analyses involving possibility theory and possibility theory are illustrated with the same engineering example used in the presentation indicated above to illustrate the use of probability to represent aleatory and epistemic uncertainty in QMU analyses. The comparison between the uses of possibility theory and probability theory for the representation of aleatory and epistemic uncertainty indicates that the possibility is not only has a better mathematical structure than probability theory but also has some challenges.

Illustrative Analysis of Probabilistic Sea Level Rise Hazard

Sea level rise results from several contributing physical processes, including ocean thermal expansion and glacier and ice sheet mass loss. Future projections of sea level remain highly uncertain due to several sources of aleatory and epistemic uncertainty. Quantifying different sources of sea level rise involves considering possible pathways of future radiative forcing and integrating models of different sea level rise processes. The probabilistic hazard analysis strategy has been proposed for combining sea level rise prediction models and climate forcing scenarios to examine sea level rise prediction uncertainty and the sources of this uncertainty. In this study we carry out an illustrative probabilistic sea level rise hazard analysis using ensembles of sea level rise predictions and emissions scenarios from the literature. This illustrative analysis allows us to estimate the probability that sea level rise will exceed a specified threshold at a given location and time and highlights how sea level rise uncertainty is sensitive to scenario inputs and sea level rise projection modeling choices. Probabilistic hazard is depicted for Earth using sea level rise hazard maps. We also demonstrate how hazard deaggregation can help us quantify the relative contributions of sea level rise sources, prediction models, and climate forcing scenarios to sea level rise hazard. The ice sheet contribution to sea level rise has a large impact on probabilistic projection of sea level rise due to the disagreements between current ice sheet models related to differences in modeling ice sheet instability.