Research On Disaster Rescue Of Sea Bridge Based On Deep Learning

Sea-crossing Bridges,with their unique advantages of novel structure and large span,have attracted increasing attention from many experts and scholars in the bridge field.With the continuous enhancement of comprehensive national strength and the construction of socialist safety and civilization,emergency management and deep learning have become a hot topic in society.Although the application of emergency management and deep learning started relatively late in China,with the increase of domestic emergencies in recent years,people gradually began to pay attention to emergency management.Bridge as an important hub of transportation,however,is an important part of the lifeline engineering,after an earthquake or typhoon emergency relief,post-disaster reconstruction has a very important position,under the action of earthquake or typhoon damage occurs,will hinder the progress of the earthquake relief work,so as to give people life and property caused by direct or indirect losses.Therefore,the research on disaster relief of sea bridge based on deep learning has become increasingly urgent,mainly including the following aspects:(1)Taking the image of bridge concrete crack as the engineering background,this paper collects relevant concrete crack images by UAV,and uses Matlab software to program.First,cracks are identified by using thresholding based segmentation method.Secondly,a new model named Cr-CNN is built on the basis of Convolutional neural network(CNN).The identification accuracy of concrete crack image is improved by using two algorithms to identify concrete crack image simultaneously.(2)By adopting genetic algorithm and particle swarm optimization algorithm(particle swarm optimization algorithm adds adaptive inertia weight decision and task grouping decision respectively for comparison,and genetic algorithm introduces crossover,neighborhood competition,mutation,self-learning and other operations),the material scheduling optimization model of UAV automatic rescue process is introduced in this paper.(3)Through the Long Short-Term Memory Network(LSTM)algorithm and NAR neural network,it is realized:the first data is entered,the training samples are generated,the training network is established,and then the simulation test is carried out.Finally,the performance evaluation analysis(such as error analysis)is carried out to verify the accuracy of the model.The typhoon data,for example,Central pressure and wind speed and center position(divided into longitude and latitude),are predicted by the long and short memory network and NAR neural network to realize the three-dimensional prediction of typhoon data.(4)Based on big data and deep learning algorithm,an information sharing platform is established.JDK:1.7 version is adopted,MySQL is used as the database,EasyUI framework is used in the front end,Eclipse is configured with Tomcat,and Navicat is linked to MySQL database.It is an effective way to develop the emergency management system of the Cross-sea Bridge to enhance public safety awareness and share mutual rescue capabilities.