Probabilistic Assessment Framework Research Articles

Reliability of an engineered slope considering the Regression Kriging (RK)-based conditional random field

Many attempts have been made to apply random field theory to the slope reliability analysis in recent decades. However, there are only a few studies that consider real landslide cases by incorporating actual soil data in the probabilistic slope stability analysis with spatially variable soils. In this paper, an engineered slope located in Hong Kong was investigated using the probabilistic approach considering the Regression Kriging (RK)-based conditional random field. The slope had been assessed and considered to be safe by classical deterministic slope stability analyses but failed eventually. In this study, both deterministic slope stability analyses and probabilistic slope stability analyses were conducted, and the comparison was made between the probabilistic approach adopting RK-based conditional random field and that adopting Ordinary Kriging (OK)-based approach. The results show that the deterministic factor of safety (FS) for a slope may not be an adequate indicator of the safety margin. In particular, a slope with a higher deterministic FS may not always represent a lower probability of failure under the framework of probabilistic assessment, where the spatial variability of soil properties is explicitly considered. Besides, the critical portion of the slope could not be found using the OK-based approach that considers a constant trend structure.

Probabilistic performance assessment of complex energy process systems – The case of a self-sustained sanitation system

A probabilistic modelling approach was developed and applied to investigate the energy and environmental performance of an innovative sanitation system, the “Nano-membrane Toilet” (NMT). The system treats human excreta via an advanced energy and water recovery island with the aim of addressing current and future sanitation demands. Due to the complex design and inherent characteristics of the system’s input material, there are a number of stochastic variables which may significantly affect the system’s performance. The non-intrusive probabilistic approach adopted in this study combines a finite number of deterministic thermodynamic process simulations with an artificial neural network (ANN) approximation model and Monte Carlo simulations (MCS) to assess the effect of system uncertainties on the predicted performance of the NMT system. The joint probability distributions of the process performance indicators suggest a Stirling Engine (SE) power output in the range of 61.5–73 W with a high confidence interval (CI) of 95%. In addition, there is high probability (with 95% CI) that the NMT system can achieve positive net power output between 15.8 and 35 W. A sensitivity study reveals the system power performance is mostly affected by SE heater temperature. Investigation into the environmental performance of the NMT design, including water recovery and CO2/NOx emissions, suggests significant environmental benefits compared to conventional systems. Results of the probabilistic analysis can better inform future improvements on the system design and operational strategy and this probabilistic assessment framework can also be applied to similar complex engineering systems.

Contribution of secondary frames to the mitigation of collapse in steel buildings subjected to extreme loads

This paper examines the contribution of secondary (or gravity) frames to the mitigation of collapse in steel buildings subjected to extreme loading conditions arising from multiple hazards. By considering sidesway and vertical mechanisms as representative of most building collapse modes, this study evaluates the effects of various secondary-frame parameters on the overall building collapse capacity. Given that the beam-to-column joint response typically dominates the behaviour of secondary frames, particular emphasis is given to key connection response characteristics such as stiffness, strength and ductility. The paper starts with a brief description of the probabilistic assessment framework that serves as the basis for the study followed by the assessment of the influence of partially restrained (PR) gravity frames on the collapse capacities of steel structures by means of generalised structural sub-assemblages. Secondary frames are found to provide up to a threefold increase in the sidesway collapse resistance depending on the magnitude of second-order effects. Similarly, the vertical (progressive) collapse capacity is greatly affected by the connection depth versus beam length ratio and the secondary connection strength. A brief discussion on directions for the provision of simplified approaches for the design of secondary frames is presented and illustrated through two case studies involving steel frame buildings subjected to earthquake and blast scenarios, as well as localised fire and blast hazards. This study offers a fundamental step towards the formulation of design and assessment strategies that incorporate secondary systems into a multi-hazard structural evaluation framework.

Probabilistic modeling of eruptive activity at Etna volcano using InSAR surface displacements and ATSR thermal radiance

Satellite monitoring offers a powerful means to regularly characterize the mechanical and thermal states of active volcanoes. Satellite‐measured surface inflation and radiant heat flux reflect the pressurization and discharge, respectively, of a magmatic system, suggesting that studying these parameters together may help in better understanding future eruptive activity. We incorporate InSAR surface displacement data and ATSR thermal radiance data into a probabilistic model for activity at Etna volcano in 1996–2000, when surface deformation and thermal radiance appeared to be related in a periodic fashion. The probabilistic approach addresses both the magnitude and timing of eruptive events, based upon a simple physical model of eruptions as renewal processes. We anticipate that this approach could serve as a framework for probabilistic assessment in eruption scenarios with persistent activity and multiple monitoring datastreams.