ali reza shooreshi; hassan zoghi
Abstract
Among the network of urban roads, the network of emergency roads plays an important role in providing relief during an earthquake, especially in the crisis response phase. It is very important to maintain the function of the urban roads network in the first few hours after earthquake. Protecting and ...
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Among the network of urban roads, the network of emergency roads plays an important role in providing relief during an earthquake, especially in the crisis response phase. It is very important to maintain the function of the urban roads network in the first few hours after earthquake. Protecting and strengthening vulnerable parts of the network before the crisis (especially bridges) plays a significant effect in reducing damages and injuries. However, retrofitting all vulnerable components is practically impossible due to budget constraints. The existence of this limitation requires identifying the vulnerable components accurately. Therefore, retrofitting options are prioritized first, and the most suitable ones are finally selected. In this research, after identifying the bridges that need to be retrofitted on the emergency roads network through a five-step methodology, we also considered the financial limitations and budget allocation options, and prioritized retrofitting options based on the network of layers created in the Geographic Information systems environment (GIS) under the title of input. Examining all possible situations for the stability of bridges after a specific earthquake, designing the emergency road network for all these situations, examining different options for retrofitting bridges, evaluating the effect of this retrofitting on the length of the emergency network, and finally, the prioritization of retrofitting options according to their impact during the emergency network, are the main steps of the proposed method in this study. The efficiency of the above method was evaluated after applying it on a part of the emergency roads network of Tehran as a real network with large scale.
