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Retrofitting Transportation Network Using a Fuzzy Random Multiobjective Bilevel Model to Hedge against Seismic Risk

DOI: 10.1155/2014/505890

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This paper focuses on the problem of hedging against seismic risk through the retrofit of transportation systems in large-scale construction projects (LSCP). A fuzzy random multiobjective bilevel programming model is formulated with the objectives of the retrofit costs and the benefits on two separate levels. After establishing the model, a fuzzy random variable transformation approach and fuzzy variable approximation decomposition are used to deal with the uncertainty. An approximation decomposition-based multi-objective AGLNPSO is developed to solve the model. The results of a case study validate the efficiency of the proposed approach. 1. Introduction Transportation networks play a very important role in both urban and rural areas, as well as in industrial sites such as large-scale construction sites. Liu et al. [1] stated that transportation networks are critical infrastructure and their smooth operation is important for maintaining the normal functions of society. However, disasters, especially earthquakes, cause not only tremendous economic losses and social chaos but also enormous damage to infrastructure (e.g., 2008 Wenchuan Earthquake, 2010 Chile Earthquake, and 2011 Japan Earthquake). Thus, as Liu et al. [1] pointed out, seismic risk control should also consider the effect that damaged or destroyed transportation networks have on the effectiveness of postdisaster rescue and repair activities and the associated socioeconomic losses. Under a seismic risk threat, retrofit decisions are considered to be an effective protective measure and can have a significant impact on these systems [1–3]. Therefore, promoting retrofit decisions for transportation networks is necessary to hedge against seismic risk. The research in this area has mainly focused on the retrofitting of bridges for transportation networks [4–6]. Werner et al. [2] extended seismic retrofits to highway systems. Afterwards, Liu et al. [1] established a two-stage stochastic programming model for retrofit decisions for transportation network protection. This previous research, however, has primarily focused on urban transportation, but it is essential that transportation networks in large-scale construction projects (LSCP) also be considered. As a critical infrastructure, the smooth operation of these networks is important for maintaining the normal progress of these projects. Therefore, it is necessary to control the seismic risk for LSCP transportation networks to mitigate losses. When considering LSCP transportation network retrofits, there are significant challenges. First, these


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