Research On Methods For Dam Safety Monitoring Based On Intelligence Computation

Based on the development character of dam safety monitoring and the wide applicationof intelligence computation, with the theories and methods of computational intelligence, thenew modeling methods of forward and inverse analysis and the computation of optimalselection degree of measuring points' location were investigated in this paper. The majorcontributions are summarized as follows:A novel machine learning method called support vector machine (SVM) based onstatistical learning theory was introduced into the field of dam safety monitoring, and theSVM model of dam safety monitoring was established to forecast the dam deformation.Through the comparison with traditional statistical regression model and neural networkmodel, results indicate that SVM-based model possesses not only higher precision offorecasting, but also better generalization ability in long-interval forecasting. In view of thelow training speed of the standard SVM algorithm which is hard to solve large scale dataproblem, a least squares support vector machine (LSSVM) was put forward for improving thedisadvantage of standard SVM algorithm. The training speed and the ability to solve largescale data problem were much improved by using LSSVM. Appropriate parameters were verycrucial to learning and generalization ability of LSSVM, so we introduced a novel heuristicand stochastic global optimization algorithm-Harmony Search (HS) algorithm to realize theadaptive selection of LSSVM parameters, and brought forward a novel fusion monitoringmodel-harmony search least squares support vector machine (HSLSSVM). Simulation showsthat HSLSSVM-based monitoring model of dam safety has the advantages of simplemodeling technique, adaptive technique, higher learning speed, higher forecasting precision,better generalization ability etc al.Two novel intelligence inversion algorithms of dam parameters were modified and used.They were particle swarm optimization (PSO) and artificial fish swarm algorithm (AFSA).For PSO, a new non-linear attenuation strategy of inertia weight was established to effectivelyharmonize the ability of global convergence and local convergence; simultaneity simulatedannealing algorithm was introduced to PSO, so that the ability of getting away from localminimum was reinforced and the search efficiency of algorithm was improved. To theimproving of AFSA, we firstly applied the Logistic mapping system provided withcompletely chaotic characteristic to replace the random number system of AFSA, and then anew strategy of dynamically adjusting visual was reconstructed, so the improved CAFSApossessed not only quicker speed of getting to global optimum, but also the stronger ability ofsearching local optimum. Results of computation example show that ISAPSO and ICAFSA have the predominant integer performance for dam parameters' inversion.On the basis of deep analysis of the relationship among effect-variable, parameterssensitivity and optimal layout of measuring points, the concept and computing way of theoptimal selection degree of measuring points' location was built, based on the way, we couldlay measuring points in the projects of constructing dams and selection measuring data toforward and inverse analysis for the projects of existing dams. Through the structure analysesand formula deductions of neural network and support vector machine, the normal formulaeof parameters sensitivity were obtained by network's output variables derivative about inputvariables. After the key parameters which control the strain and stress of dam were foundthough orthogonal test method, the sensitivity distribution of key parameters in gravity damand embankment dam was obtained by using FEM-NN method, then the optimal selectiondegree of measuring points' location in dam was computed based on the proposedcomputation method. Finally, the result indicates that the proposed method of optimalselection degree of measuring points' location in dam is reasonable and practicable.