道橋相關(guān)外文翻譯--隧道施工風(fēng)險(xiǎn)敏感型決策支持系統(tǒng)-在線瀏覽
2024-07-23 15:22本頁(yè)面
【正文】 ations 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。 crew positions for all tunneling operations。 time equations for activities in the activity works。 and formulas for calculating tunneling unit costs (＄ /m).The probabilistic scheduling works are analyzed using Monte Carlo final outputs include tunneling unit cost distributions for different alternatives, which provide inputs for the risksensitive dynamic decision model. A detailed description of this model can be found in Likhitruangsilp and Ioannou (2020). Risksensitive dynamic decision model The risksensitive dynamic decision model, the core of the proposed system, is formulated as a risksensitive stochastic dynamic programming model. Its input includes the ground class transition probability matrix for each tunneling stage determined by the tunneling unit cost distributions for different alternatives simulated by the probabilistic tunnel cost estimating model. The model also requires the decision maker’s (., contractor’s) risk aversion coefficient (γ), which is the parameter of the exponential utility function used to encode the decision maker’s degree of risk preference. A positive γ means that the decision maker is risk averse, whereas a negative γ means that the decision maker is risk preferring. The risksensitive dynamic decision model, programmed in MATLAB, performs decision and risk analysis to determine the optimal tunneling policies and riskadjusted tunneling costs of the project, both of which are functions of available information and the decision maker’s degree of risk sensitivity. Application The Hanging Lake Tunnel, a highway tunneling project in Colorado, is used to demonstrate the application of the proposed system. This rock tunneling project involved the construction of a pair of twolane highway tunnels: the eastbound and the westbound tunnels. Here, we focus on the part of the westbound tunnel excavated by multipledrift and blast methods. Based on several rock mass classification systems, the geologic conditions were classified into three ground classes: GC1 (best), GC2(medium), and GC3(worst). Three excavation methods (EM1,EM2,EM3) AND initial support systems (SS1,SS2,SS3) were designed corresponding to the three ground classes. For example, EM2 and SS2 are the most economical and structurally adequate excavation of six headings (drifts) and rock reinforcement systems consisting of dowels,spiles,and shotcrete,as shown in Figure 1. Descriptions of the ground class classification and the specifications of excavation and support methods can be found in Scotese and Ackerman (1992), and Essex et al. (1993). Thus, there are nine possible tunneling alternatives (., 3 excavation and support methods 3 ground classes). For example,alternative (EM2,GC3) represents the decision to use EM2 for a particular round, and the prevailing ground class after blasting is GC3(., structurally inadequate). Probabilistic geologic prediction model The probabilistic geologic prediction model for the Hanging Lake Tunnel was developed based on three important geologic parameters: rock quality designation (RQD), fracture frequency, and weathering and alteration. The bination of these geologic parameter states are classified into three ground classes corresponding to the classification described by Essex et al.(1993). The parameters for each geologic Markov model were estimated by analyzing data from the logs of boreholes (Leeds,Hill and Jewett, Inc. 1981).The posterior state probabilities at the observation points were subjectively encoded based on a variety of assessments by geology experts, including Leeds,Hill and Jewett, Inc. (1981), and Scotese and Ackerman (1992). These probabilities were used to determine the posterior state probabilities for nonobservation points at intervals of m (12 ft) along the tunnel. The ground class transition probability matrix between any two stages was then determined based on the concept of posite ground class transitions. An example of the model output is the ground class transition probability matrix between locations m(2,448 ft) and m(2,460ft): For example, given that the tunnel geology class 1 at location m, the probabilities that it will make a transition to ground class 1 (remain the same), ground class 2, and ground class 3 at location m are , , and percent, respectively (., the first row of the above matrix). Probabilistic tunnel cost estimating model According to available info
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