Fast prediction for building fire spread based on machine learning
In order to quickly rehearse or reproduce the fire scene,the key influencing factors of building fire were studied by using fire dynamics software(FDS)and machine learning technology,so as to adjust the fire rescue strategy in real-time,and finally provide a space composition scheme conducive to fire engineering for architectural design.In this paper,a single-room fire smoke overflow was taken as a case,and 5 kinds of algorithm models were used to perform machine learning training and efficiency evaluation on 11 spatial composition parameters and fire conditions,a total of 7 776 sets of fire simulation result data.The experimental results show that the machine learning algorithm is suitable for parameter learning and evaluation prediction of discrete types such as building space.It can intuitively give the weight of each parameter,mine the key information in the fire dynamics system,and realize the visualization of fire data.The Random Forests(RF)algorithm has the highest prediction efficiency,and its best prediction accuracy can reach 91.82％.
building firesrapid fire predictionmachine learningweight analysisspatial composition
万方数据
维普
