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Design of a hydrosystem infrastructure, by nature, is an optimization prob­lem consisting of an analysis of the hydraulic performance of the structure to convey flow across or through the structure and a determination of the most economical design alternative. The objective function is to minimize the sum of capital investment cost, the expected flood damage […]

The evolution of hydrosystem design methods can be roughly classified into four stages: (1) historical event-based design, (2) return-period design, (3) conven­tional risk-based design, and (4) optimal risk-based design with consideration given to a variety of uncertainties. Historical event-based design. In the design of hydrosystem infrastructures and the establishment of land-use management practices to prevent […]

The basic concept of risk-based design is shown schematically in Fig. 8.9. The risk function accounting for the uncertainties of various factors can be obtained using the reliability computation procedures described in earlier chapters. Al­ternatively, the risk function can account for the potential undesirable conse­quences associated with the failure of hydrosystem infrastructures. For sim­plicity, only […]

Reliability analysis methods can be applied to design hydrosystem infrastruc­tures with or without considering risk costs. Risk costs are those cost items incurred owing to the unexpected failure of structures, and they can be broadly classified into tangible and intangible costs. Tangible costs are those measurable in terms of monetary unit, which include damage to […]

In Sec. 6.3.4 it was shown that the implementation of scheduled maintenance can increase the mean time to failure (MTTF) of a system having an increasing hazard function. Increasing maintenance frequency would result in a decrease in repair frequency and vice versa. Suppose that an engineer is considering implementing a regular scheduled maintenance for a […]

Consider the design of a hydrosystem consisting of n subsystems that are ar­ranged in series so that the failure of one subsystem will cause the failure of the entire system. In this case, reliability of the hydrosystem can be improved by installing standby units in each subsystem (see Fig. 8.7). Figure 8.7 consists of a […]

As described in Chap. 5, reliability of a multicomponent system depends on the component reliability, number of redundant components, and the arrangement of the components. With everything else remaining fixed, the system reliability gets higher as the number of redundant components increases. However, noth­ing is free—achieving higher system reliability has to be paid for by […]

Having constructed an optimization model, one must choose an optimization technique to solve the model. The way that one solves the problem depends largely on the form of the objective function and constraints, the nature and number of variables, the kind of computational facility available, and personal taste and experiences. Mays and Tung (1992) provide […]

The optimization model defined by Eqs. (8.1a-c) is for a single-objective problem. On the other hand, multiobjective programming deals with problems in­volving several noncommensurable and often conflicting objectives simultane­ously. Among the objective functions involved, no single one has an importance that is overwhelmingly dominant over all others. Under this circumstance, the ideological theme of optimality […]

Optimization models possess algorithms to compare the measures of effective­ness of a system and attempt to yield the optimal solution having the most de­sirable value of the adopted measures. In other words, an optimization model applies an optimum-seeking algorithm, which enables the search of all alterna­tive solutions to select the best one. The general class […]