Research On Cost Estimation Method And Application Of Public Buildings Based On Whole Life Cycle

With the rapid development of China’s construction industry and the increased attention of society to public services,the number and area of public building projects are increasing,but public buildings generally suffer from high operational energy consumption,low operational efficiency and high whole life cycle costs.To this end,this thesis introduces the whole life cycle cost theory into the investment estimation of public buildings,considers both the initial construction cost and future cost of the project at the project decision-making stage,and carries out engineering design and investment optimisation according to the principle of the lowest whole life cycle cost,which is of great significance to the whole life cycle cost control of public buildings.In this thesis,based on life cycle cost theory and the characteristics of public buildings,the life cycle cost is divided into initial construction cost and future operation and maintenance cost,and the literature research method is used to summarize the life cycle cost estimation method into two parts:initial construction cost and future cost.Secondly,considering the collection of historical data in different stages of the whole life cycle of public buildings,different ways are adopted to estimate the initial construction cost and future operation cost.The optimal neural network is introduced to establish the initial construction cost estimation model,and the unascertained mathematical theory is introduced to establish the future cost estimation model.Finally,taking university buildings as an example,the prediction effect of the model is verified.The main work of this thesis is summarized as follows:(1)For the initial construction cost estimation,based on existing literature studies and public building cases,this thesis preliminatively obtains the characteristic parameters that affect the project cost,uses the rough set theory to reduce the redundant indicators,and proposes to optimize the BP neural network based on combining improved beetle antennas search and improved sine cosine double optimization algorithm(BAS-SCA)to establish the initial construction cost estimation model.The estimation results show that compared with the performance of BP neural network estimation model improved by other intelligent algorithms,the coefficient of determination(R~2)of the initial cost estimation method used in this thesis reaches 0.9994,and the average absolute percentage error MAPE is 0.81%,which can be seen.It has better performance in stability and prediction accuracy.(2)For future cost estimation,this thesis proposes two future cost estimation models according to whether similar projects exist in the proposed public buildings.In the case of similar engineering,the unascertained mathematical point estimation model based on small sample data is established,and in the case of no similar engineering data,the unascertained mathematical point estimation model based on expert prediction is established.The estimation results show that the confidence degree of the point estimation model for the operation and maintenance costs of public buildings is above 93%.By comparing the results of the point estimation model and the expert prediction model,it is proved that the accuracy of the expert prediction also meets the requirements of the investment estimation accuracy.Moreover,the results of these two estimation methods can solve the problem that the accuracy of estimation cannot be quantified due to the lack of subjective knowledge of traditional estimation methods to a certain extent.Moreover,the advantages and disadvantages of two future cost estimation models are compared,and their respective application scopes are given.(3)A whole life cycle cost estimation model was established to calculate the whole life cycle costs of the project to be estimated.The estimation results show that the O&M costs of the public building case used in this thesis account for 21.25%of the whole life cycle costs,which is a relatively large percentage.This demonstrates the need for whole life cycle cost estimation for public buildings at the project decision stage.