Decision-making under risk assessment involves dealing with the matter of uncertainty, especially in projects such as tunnel construction. Risk control should include not only measures to reduce the possible consequence of incident, but also exploration measures (information collecting measures) to reduce the uncertainty of the incident. The classical risk assessment model in engineering is R = P × C which only takes account of the assessment and decision-making of possible consequences. It cannot provide theoretical guidance for taking exploration measures. The paper presents an advanced methodology to assess the effectiveness of exploration measures in decision-making. The methodology classifies risk into two attributes: hazard (expected value) and uncertainty (entropy). On this basis, a generalized model of decision-making under risk assessment is proposed. This model extends the use of the classical assessment model to a more general case. The reason for taking exploration measures and assessment of such measures' effectiveness could be explained well by this developed model. This model can also serve as a descriptive model for many risk problems and provide a decision-making basis for a variety of risk types. Moreover, the assessment process and calculation method are applied with some case studies.

Thin-walled tubes were subjected to fully reversed strain-controlled axial (A), torsional (T), in-phase (I) and out-of-phase (O) loading. A computer controlled MTS axial-torsion servocontrolled testing machine and strain measurement on the uniform section of the specimen were used in all tests. Each of four specimens was subjected to combinations of A, T, and I segments, of two hundred cycles each, followed by an O-segment. In between every segment, axial, and torsional check-tests were performed to obtain information on hardening and rate dependence. During cycling the normalized stress amplitude was observed to increase nearly threefold. Even if cyclic saturation was reached during a segment, a change in loading direction caused further hardening followed by subsequent softening which rapidly subsided. Only the O-segment showed hardening exclusively. After the A, T, and I segments, a “cross-effect” was observed which disappeared after the final O-segment. Compared to the considerable hardening, rate dependence as measured by relaxation and strain-rate change tests varied little during cycling.

Abstract:The impact of uncertainty on risk assessment and decision-making is increasingly being prioritized, especially for large geotechnical projects such as tunnels, where uncertainty is often the main source of risk. Epistemic uncertainty, which can be reduced, is the focus of attention. In this study, the existing entropy-risk decision model is first discussed and analyzed, and its deficiencies are improved upon and overcome. Then, this study addresses the fact that existing studies only consider parameter uncertainty and ignore the influence of the model uncertainty. Here, focus is on the issue of model uncertainty and differences in risk consciousness with different decision-makers. The utility theory is introduced in the model. Finally, a risk decision model is proposed based on the sensitivity analysis and the tolerance cost, which can improve decision-making efficiency. This research can provide guidance or reference for the evaluation and decision-making of complex systems engineering problems, and indicate a direction for further research of risk assessment and decision-making issues.

An improved attribute recognition method is reviewed and discussed to evaluate the risk of water inrush in karst tunnels. Due to the complex geology and hydrogeology, the methodology discusses the uncertainties related to the evaluation index and attribute measure. The uncertainties can be described by probability distributions. The values of evaluation index and attribute measure were employed through random numbers generated by Monte Carlo simulations and an attribute measure belt was chosen instead of the linearity attribute measure function. Considering the uncertainties of evaluation index and attribute measure, the probability distributions of four risk grades are calculated using random numbers generated by Monte Carlo simulation. According to the probability distribution, the risk level can be analyzed under different confidence coefficients. The method improvement is more accurate and feasible compared with the results derived from the attribute recognition model. Finally, the improved attribute recognition method was applied and verified in Longmenshan tunnel in China.

The precipitation behavior of AA2618 was studied by a multitude of characterization techniques: microhardness testing, lattice parameter measurement through X-ray diffraction (XRD), differential scanning calorimetry (DSC), transmission electron microscopy (TEM), and atom probe field ion microscopy (APFIM). The matrix lattice parameter increased during the first 20 hours of natural aging, due to the formation of Cu clusters and decreased over the next 24 hours, due to the formation of Mg-rich clusters. Prior natural aging weakened subsequent artificial aging hardening at 180°C, 200°C, and 230°C, due to the cluster reversion that delayed the precipitation of strengthening phases. The matrix lattice parameter exhibited erratic changes during artificial aging that corresponded to the formation and partial dissolution of Guinier-Preston-Bagaryatsky (GPB) zones, the transformation of GPB zones to GPB2 zones, and the precipitation of S¢. The structural changes during the artificial aging of AA2618 occur in this sequence: supersaturated solid solution fi clusters + GPB fi GPB + GPB2 fi GPB2 + S¢ fi S¢+ S fi S.