Study On Risk Prediction And Network Planning Optimization For Construction Item Based On Intelligent Optimization Algorithms

This thesis gives a theoretical discussion to Artificial Neural Network, GeneticAlgorithms and other intelligent optimization algorithms based on the research andanalysis of the uncertain factors and uncertain information disposition method whichinfluence the construction item's risk management. Devises and introduces anArtificial Neural Network Algorithms grounded on the Genetic Algorithms to fullyuse the advantages of themselves. Then combines with the advantages anddisadvantages of both Genetic Algorithms and Simulation Anneal algorithms, putsforward the Hybrid Genetic Algorithms using the Simulation Anneal mechanism.Between the line, according to the connection of risk factors and risk result, themapped relax of them is built up by advancing the Artificial Neural Network, thusoffering a advanced prediction method. The multi-objective optimal modelconsidering the maximal pure value and quality of construction project is made badeon a deep study on Network Planning Optimization. Design the program achievementof Network Planning multi-objective optimization based on Hybrid GeneticAlgorithms Considering risk value of times and cost predicted by above improvedANN, this multi-objective optimal value must be corrected in order that the correctedvalue is much more consonant with complicated construction fact than the optimalvalue.The rough sets theory is introduced in the problem of how the Network Planningoptimization can be achieved, a series of decision rules is obtained according with thereal measure of construction enterprise. Thus guiding the construction managementpersons in the spot to manage so as to realize the ultimate object and offering a newmethod for the full intellective construction item management.