High-rise building fire rescue equipment deployment based on case reasoning and multi-objective optimization
In order to enhance the efficiency of emergency response in high-rise building fires and re-duce potential casualties and economic losses caused by accidents.Firstly,this study established a typi-cal case database of high-rise building fires and constructed a fire rescue equipment demand prediction model to solve the demand for equipment at the incident site.Secondly,optimization objectives for fire-fighting and rescue equipment deployment were proposed from three aspects:efficiency,rationality,and reliability.Finally,the corresponding objective functions were constructed.Finally,a multi-objective op-timization model with the minimum emergency rescue points and the most reliable deployment routes was constructed under the premise of minimizing deployment time.The effectiveness of the optimized solution was validated using a case study approach.In the case study,the 21 emergency rescue points in the main urban area of Xi'an city were taken as the research objects.The demand prediction model was used to solve the fire rescue equipment requirements of the target accident case.Based on the re-search area and the overview of emergency rescue points,ArcGIS network analysis was used to calcu-late the shortest deployment time from each emergency rescue point to the incident location.And then the multi-objective optimization model was utilized to select the optimal deployment plan.The results show that the optimal deployment plan has a deployment time of 7′34″,a total of 7 emergency rescue points and a reliability rate of 64%.which verifies the effectiveness and feasibility of the model and so-lution method.The optimal deployment plan path is visualized in ArcGIS.The research findings can provide reliable solutions for addressing emergency needs in areas affected by urban high-rise building fire affected areas,and offer guidance for achieving efficient emergency resue in such accidents.
high-rise building fireemergency rescueequipment deploymentArcGISdemand forecas-tingmulti-objective optimization
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