Cost Prediction of Existing Building Renovation Based on PCA-BP Neural Network
Due to construction process information has been missed and damaged for existing build-ings,as well as the lack of basic cost estimation standard data such as cost quota data.It is difficult to quickly and accurately calculate the cost of renovation project during the decision-making stage.To adress this issure,a method of predicting the cost of existing buildings renovation based on principal component analysis(PCA)and BP neural network is proposed.According to the case and literature ayalysis,10 influencing factors that affect the cost of existing building renovation were identified and extracted.By utilizing principal component analysis,the extracted 10 factors were reduced dimension-ality and 3 new comprehensive variables were identified.BP neural network was used to train,validate and test 280 existing building renovation costs in groups.The PCA-BP neural network model is based on reduction and elimination of correlation dates among indicators.The results show that the PCA-BP neural network model improves higher processing and prediction efficiency based on dimen-sional reduction and elimation of correlation data between indicators.It achieves good results in root square error and correlation.The comprehensive error between the measured data and the predicted data was 2.66%.This paper provides a new method for rapidly estimation of renovation of existing construction projects.