Whole Life Cycle Cost Estimate Method Based On Intelligent Integration

The current estimated project cost investment, decision-making, control theory method with linear, certainty, simplicity, hysteresis defects. It led to investment in large estimation errors(estimates are not accurate) and the control reliability unstable (three super problem). Triggered a series of real quality, duration, cost overruns and other issues,The use of smart integration technology, complex system dynamics, and overall cost of integrating theory and other methods to search for effective cost estimation and control methods,This is a way to solve the problem.In this paper, Based on full life-cycle cost (WLC) and significant theory (CS), To discuss and search for the high-precision estimation and control model under different complex circumstances, expect to improve the level of cost estimation and control,Fundamental solution to these problems which caused by improper estimation and control method. Research object are actual completed quantities, As an example of highway calculated, In-depth analysis to study the characteristics of highway engineering, using the rough set to extract engineering features from an objective, and to identify similar projects; based on traditional rough set, given the strong resistance to noise variable precision rough set model (VPRS), further excavation of similar projects.with Artificial neural networks and rough set machine learning, Through the actual analysis, proof of Rough Set - Neural networks and rough set machine learning estimation method is feasible and effective. using two basic methods in the field of intelligent computing, integration of artificial neural networks and genetic algorithms, establish intelligent integration of computing cost estimates of genetic neural network model,though the simulation tests, verify its stability and effectiveness, based on depth study of neural networks, establish radial basis (RBF) neural network model, for its structural characteristics, use particle swarm (PSO) algorithm to optimize it, completed the integration of intelligent computation cost estimation, the simulation shows that this method in the whole life significant cost (WLCS) estimation method is feasible. Integration of chaotic dynamical systems and neural networks, combine this two advantages of intelligent algorithms.Based on EVM,use chaotic neural network to estimate the significant project(ACWP,BCWP) in dynamicly, So as to estimating accurately before in the event of deviation. analyze the causes and give the control measures. Simulation results show that the method is effective and feasible.