道桥相关外文翻译--隧道施工风险敏感型决策支持系统(编辑修改稿)

道桥相关外文翻译--隧道施工风险敏感型决策支持系统(编辑修改稿)内容摘要：

geologic uncertainty, they cannot entirely eliminate this uncertainty from tunnel construction planning. Geologic variability Most tunnels traverse a variety of geologic conditions, the locations and extents of which are impossible to define in advance with certainty. For most tunneling projects with significant geologic variability, the selected tunneling methods must be adaptable to all anticipated geologic conditions without seriously interrupting excavation progress. These adaptable tunneling methods enpass the modification of excavation methods(.,heading and bench, and multiple drift), round length, drill patterns, and details of support. Thus, tunneling decisions are dynamic in nature. Uncertainty in tunneling productivity Another risk in tunneling decisions results from uncertainty in the productivity of tunneling processes. This uncertainty stems from the variation of construction equipment performance, the variation of worker outputs, and unexpected events such as accidents during construction. This uncertainty exists even if geologic conditions are known. Thus, its impact on tunneling decisions must be addressed explicitly. Risk sensitivity Individual valuation of benefits and costs for decisions involving risk (., tunneling decisions) is often nonlinear because these decisions are not based on the maximization of expected moary value. In other words, when making decisions under uncertainty a decision maker is typically sensitive to risk, either risk averse or risk preferring. An individual’s risk sensitivity (risk preference) is influenced by several factors, especially that person’s current asset position. Typically, as a person’s position increase, the less riskaverse their behavior toward the same risk. A contractor’s risk aversion and its degree of risk exposure can have a major influence on construction decisions and the necessary amount of risk premium or contingency embedded in a contractor’s price in order to undertake the work. A more riskaverse contractor adopts a more conservative plan and includes a higher allowance as contingencies in his bid than a less riskaverse contractor does (Ioannou 1988). Thus, it is necessary to incorporate risk sensitivity into tunneling decisions. By considering all above factors, tunneling decisions can be considered a risksensitive dynamic probabilistic decision process, which can be structures by the risksensitive decision support system (Likhitruangsilp 2020). Risksensitive decision support system The risksensitive decision support system consists of three interrelated models: the probabilistic geologic prediction model, the probabilistic tunnel cost estimating model, and the risksensitive dynamic decision model. Probabilistic geologic prediction model The probabilistic geologic prediction model uses all available geologic information to characterize geologic uncertainty and variability along the tunnel profile in the probabilistic form of ground class transitions. The model is based on discretestate, continuousspace Markov processes of important geologic parameters (., rock fracture). These geologic Markov models are created from regional data (., geologic maps) and updated by locationspecific data (., borehole tests) using direct assessment or Bayesian updating (Ioannou 1984). The model has been programmed in MATLAB. Its input includes the length of tunnel, the extent of each stage (., round length), geologic parameters and their states, and the definition of ground classes. Based on this input, the model calculates the posterior state probabilities of geologic parameters and ground classes at different locations along the tunnel. Both state probabilities are subsequently used to determine the ground class transition probability matrices of the tunnel geology by applying the concept of posite ground class transitions (Likhitruangsilp 2020). The resulting transition probability matrices bee the input for the risksensitive dynamic decision model. Probabilistic tunnel cost model The probabilistic tunnel cost estimating model performs stochastic evaluation of tunneling time and cost performance for different binations of excavation and support methods with different ground classes (tunneling alternatives). The model includes the cost estimating submodel and the probabilistic scheduling submodel. The cost estimating submodel, created in a puter spreadsheet, organizes tunneling cost items, performs quantify takeoff putations, and calculates fixed costs and variable costs associated with each alternative. In addition to normal tunneling costs,it also considers risks of selecting a wrong excavation method during construction. Its input includes a work breakdown structure (WBS) designed specifically for tunneling projects。 specifications of excavation methods and support systems。 crew positions for all tunneling operations。 and material, equipment, and labor cost data. The final outputs from the cost estimating submodel are fixed costs and variable costs for diff。