Novel Computational Intelligence Methods For Dam Health Monitoring And Damage Identification

With the development of economy,a great deal large civil engineering projects,which are long used with great impact,are constructed in China.The wreckage of these important projects will cause serious loss of property and life.Therefore,many already constructed large engineering projects and infrastructures need effective measures to detect and evaluate their condition of safety,and then the damage can be repaired and controlled.Many new constructed structures,such as dam,bridge and ocean platform,are equipped with healthy monitoring system,which are applied to monitor the structural safety condition and provide direct effective methods to study damage evolve process during enlistment.A basic presume in health monitoring system is that these systems not measure structural abnormity directly,rather measure the responses under the operational and environmental loads or the responses to input of actors embedded in the sense system.The data collected by sensors are more or less correlated to the existence and position of structural abnormity.Data processing procedures are integrant to a health monitoring system;they translate the data collected by sensors into information of structural condition.Computational intelligence is a powerful tool for safety prediction modeling and inverse analysis,and many achievements have been obtained in this domain,but there are still some drawbacks in the traditional computational intelligence.Nowadays,computational intelligence is still in the rapid developing stage,several novel computational intelligent methods are introduced into the domain of safety prediction and inverse analysis of structures like dams,and some meaningful work is developed.Artificial bee colony algorithms(ABC),particle swarm optimization(PSO) and differential evolution(DE) are three novel intelligent optimization algorithms with tremendous developing potential.Compared to traditional genetic algorithms,the advantages of these optimization algorithms are easier to implementation and better convergence performance.Aim at the problem of "incline to be the same" phenomena when used to few parameters optimization caused by single search pattern,ABC are improved by introduce cultural frame,annealing operator and simplex operators,and cultural annealing ABC and hybrid simplex ABC are proposed.The convergence speed of the improved algorithms is accelerated,meanwhile the stagnation phenomena is reduced because of enriched search patterns.DE and PSO have good convergence performance whey they are applied to problems with many dimensions,and they are used to damage detection problems.Several immune properties are introduced into PSO,and an immunity enhanced PSO is proposed for damage detection problems.Static and dynamic inverse analysis indicates that the proposed algorithms are very efficient for inverse problem,so novel approaches are provided for structural parameter identification,which can be used to response prediction modeling and damage evaluation.Compare to BP neural networks,radial basis function neural networks(RBF) not only has biological foundation but also mathematical foundation,meanwhile it has simpler structure, faster training speed and higher accuracy of simulation which is because the hidden nodes have local tuned properties.An ant colony clustering RBF model for completed inverse analysis problems is proposed.The disadvantages of intelligent optimization based inverse need long time caused by repeating iteration and traditional neural network need long training time,easy trapped into local optimum and low inversion accuracy,are avoided in the new model.It can be applied to large-scale nonlinear inverse problems,such as inverse analysis of three dimensional rockfill dams.K-means clustering algorithms have the disadvantages of easily trapped into local optimal and dependence on initial clustering centers.However,ant colony clustering can avoid these disadvantages,more reasonable radial basis function centers and more satisfactory RBF model can be obtained.Support vector machines(SVM) are novel technology for data mine,and are novel tools for machine learning recurs to optimization methods.Several optimization algorithms are used to model parameter selection.Application example illustrated that SVM model have the advantage of high prediction accuracy,less over fitting and is an efficient prediction modeling method.Optimized sensor network configuration can minimize the sensor number needed and save investment,meanwhile can provide a robust system with high accuracy.Optimal sensor placement problem in health monitoring and testing is studied.A partheno-genetic algorithm (PGA) is used to solve this problem.The drawback of traditional genetic algorithm for this problem is avoided.In order to improve the performance of PGA,adaptive simulated annealing PGA and virus coevolution PGA are proposed.The efficiency of the proposed optimal sensor placement algorithms are illustrated by several numerical examples.