An integrated risk sensing system for geo-structural safety

Over the last decades, geo-structures are experiencing a rapid development in China. The potential risks inherent in the huge amount of construction and asset operation projects in China were well managed in the major project, i.e. the project of Shanghai Yangtze tunnel in 2002. Since then, risk assessment of geo-structures has been gradually developed from a qualitative manner to a quantitative manner. However, the current practices of risk management have been paid considerable attention to the assessment, but little on risk control. As a result, the responses to risks occurrences after a comprehensive assessment are basically too late. In this paper, a smart system for risk sensing incorporating the wireless sensor network (WSN) on-site visualization techniques and the resilience-based repair strategy was proposed. The merit of this system is the real-time monitoring for geo-structural performance and dynamic pre-warning for safety of on-site workers. The sectional convergence, joint opening, and seepage of segmental lining of shield tunnel were monitored by the micro-electro-mechanical systems (MEMS) based sensors. The light emitting diode (LED) coupling with the above WSN system was used to indicate different risk levels on site. By sensing the risks and telling the risks in real time, the geo-risks could be controlled and the safety of geo-structures could be assured to a certain degree. Finally, a resilience-based analysis model was proposed for designing the repair strategy by using the measured data from the WSN system. The application and efficiency of this system have been validated by two cases including Shanghai metro tunnel and underwater road tunnel

A Novel Causal Risk-Based Decision-Making Methodology: The Case of Coronavirus

Either in the form of nature's wrath or a pandemic, catastrophes cause major destructions in societies, thus requiring policy and decisionmakers to take urgent action by evaluating a host of interdependent parameters, and possible scenarios. The primary purpose of this article is to propose a novel risk-based, decision-making methodology capable of unveiling causal relationships between pairs of variables. Motivated by the ongoing global emergency of the coronavirus pandemic, the article elaborates on this powerful quantitative framework drawing on data from the United States at the county level aiming at assisting policy and decision makers in taking timely action amid this emergency. This methodology offers a basis for identifying potential scenarios and consequences of the ongoing 2020 pandemic by drawing on weather variables to examine the causal impact of changing weather on the trend of daily coronavirus cases

An Efficient Approach To Probabilistic Uncertainty Analysis In Simulation-Based Multidisciplinary Design

In this paper, computationally efficient techniques for propagating the effect of uncertainty are developed to accommodate generic probabilistic representations of uncertain parameters and error estimation models in a multidisciplinary design system. To improve the computational efficiency of probabilistic uncertainty propagation in the context of highly coupled analyses, the first order sensitivity analysis and the moment matching method are employed. This is implemented by two techniques, namely, the system uncertainty analysis method (SUAM) and the concurrent subsystem uncertainty analysis method (CSSUAM). Depending on the number of variables and the number of disciplines involved, the effectiveness of these techniques varies. A mathematical example and an electronic packaging problem are used to verify the effectiveness of these approaches