This paper makes a comprehensive analysis on the characteristics and influencing factors of regional allocation of new construction land use indicators,and determines the primary indicators from social,economic,extern...This paper makes a comprehensive analysis on the characteristics and influencing factors of regional allocation of new construction land use indicators,and determines the primary indicators from social,economic,external and internal factors.Using Delphi method and correlation analysis,this paper selects indicators and establishes evaluation indicator system.Using entropy method and AHP,this paper determines the weight of indicators,rationally allocates new construction land,and uses the actual data in Huai'an City for case studies,so as to provide a reference for land use planning.In addition,this paper makes a comparative analysis on the land use internal factors as important factors,in order to make the weight assigning more in line with the actual situation of construction land in Huai'an.展开更多
Construction Industry operates relying on various key economic indicators.One of these indicators is material prices.On the other hand,cost is a key concern in all operations of the construction industry.In the uncert...Construction Industry operates relying on various key economic indicators.One of these indicators is material prices.On the other hand,cost is a key concern in all operations of the construction industry.In the uncertain conditions,reliable cost forecasts become an important source of information.Material cost is one of the key components of the overall cost of construction.In addition,cost overrun is a common problem in the construction industry,where nine out of ten construction projects face cost overrun.In order to carry out a successful cost management strategy and prevent cost overruns,it is very important to find reliable methods for the estimation of construction material prices.Material prices have a time dependent nature.In order to increase the foreseeability of the costs of construction materials,this study focuses on estimation of construction material indices through time series analysis.Two different types of analysis are implemented for estimation of the future values of construction material indices.The first method implemented was Autoregressive Integrated Moving Average(ARIMA),which is known to be successful in estimation of time series having a linear nature.The second method implementedwas Non-LinearAutoregressive Neural Network(NARNET)which is known to be successful in modeling and estimating of series with non-linear components.The results have shown that depending on the nature of the series,both these methods can successfully and accurately estimate the future values of the indices.In addition,we found out that Optimal NARNET architectures which provide better accuracy in estimation of the series can be identified/discovered as result of grid search on NARNET hyperparameters.展开更多
文摘This paper makes a comprehensive analysis on the characteristics and influencing factors of regional allocation of new construction land use indicators,and determines the primary indicators from social,economic,external and internal factors.Using Delphi method and correlation analysis,this paper selects indicators and establishes evaluation indicator system.Using entropy method and AHP,this paper determines the weight of indicators,rationally allocates new construction land,and uses the actual data in Huai'an City for case studies,so as to provide a reference for land use planning.In addition,this paper makes a comparative analysis on the land use internal factors as important factors,in order to make the weight assigning more in line with the actual situation of construction land in Huai'an.
基金supported by the Energy Cloud R&D Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science,ICT(2019M3F2A1073164)MSGSU BAP(2021-25).
文摘Construction Industry operates relying on various key economic indicators.One of these indicators is material prices.On the other hand,cost is a key concern in all operations of the construction industry.In the uncertain conditions,reliable cost forecasts become an important source of information.Material cost is one of the key components of the overall cost of construction.In addition,cost overrun is a common problem in the construction industry,where nine out of ten construction projects face cost overrun.In order to carry out a successful cost management strategy and prevent cost overruns,it is very important to find reliable methods for the estimation of construction material prices.Material prices have a time dependent nature.In order to increase the foreseeability of the costs of construction materials,this study focuses on estimation of construction material indices through time series analysis.Two different types of analysis are implemented for estimation of the future values of construction material indices.The first method implemented was Autoregressive Integrated Moving Average(ARIMA),which is known to be successful in estimation of time series having a linear nature.The second method implementedwas Non-LinearAutoregressive Neural Network(NARNET)which is known to be successful in modeling and estimating of series with non-linear components.The results have shown that depending on the nature of the series,both these methods can successfully and accurately estimate the future values of the indices.In addition,we found out that Optimal NARNET architectures which provide better accuracy in estimation of the series can be identified/discovered as result of grid search on NARNET hyperparameters.
