“…The model had one hidden layer with seven neurons. The results obtained from the trained models indicated that neural networks are reasonably succeeded in predicting the early stage cost estimation of buildings 11 KICEM Journal of Construction Engineering and Project Management using basic information of the projects and without the need for a more detailed design [13].…”
Section: Diversity Of Variables For Effective Factors Ismentioning
confidence: 98%
“…Despite the large number of researchers who applied neural network approach in various fields of engineering, the studies and researches on utilizing neural networks to estimate the cost of construction projects at various stages of the work are very limited [13].…”
Section: Diversity Of Variables For Effective Factors Ismentioning
confidence: 99%
“…Training and cross validation sets are used in learning the model through utilizing training set in modifying the network weights to minimize the network error, and monitoring this error by cross validation set during the training process. However, test set does not enter in the training process and it hasn"t any effect on the training process, where it is used for measuring the generalization ability of the network, and evaluated network performance [13].…”
Section: Figure I Model Impementation Steps Flowchartmentioning
confidence: 99%
“…Floor area parameter, Number of stories, Slab type, Foundation type, Number of elevators, Type of project, Type of project and External finishing parameter were adopted by several researchers [5]; [12]; [13]; [2]; [4]; [14]; [15].…”
Section: Parametric Cost Factorsmentioning
confidence: 99%
“…However, the authors reckon that the researches and studies on utilizing neural networks to estimate the cost of construction projects at various stages of the work are very limited [12] , [13].…”
The purpose of this paper is to develop a model for forecasting early design construction cost of building projects using Artificial Neural Network (ANN). Eighty questionnaires distributed among construction organizations were utilized to identify significant parameters for the building project costs. 169 case studies of building projects were collected from the construction industry in Gaza Strip. The case studies were used to develop ANN model. Eleven significant parameters were considered as independent input variables affected on "project cost". The neural network model reasonably succeeded in estimating building projects cost without the need for more detailed drawings. The average percentage error of tested dataset for the adapted model was largely acceptable (less than 6%). Sensitivity analysis showed that the area of typical floor and number of floors are the most influential parameters in building cost.
“…The model had one hidden layer with seven neurons. The results obtained from the trained models indicated that neural networks are reasonably succeeded in predicting the early stage cost estimation of buildings 11 KICEM Journal of Construction Engineering and Project Management using basic information of the projects and without the need for a more detailed design [13].…”
Section: Diversity Of Variables For Effective Factors Ismentioning
confidence: 98%
“…Despite the large number of researchers who applied neural network approach in various fields of engineering, the studies and researches on utilizing neural networks to estimate the cost of construction projects at various stages of the work are very limited [13].…”
Section: Diversity Of Variables For Effective Factors Ismentioning
confidence: 99%
“…Training and cross validation sets are used in learning the model through utilizing training set in modifying the network weights to minimize the network error, and monitoring this error by cross validation set during the training process. However, test set does not enter in the training process and it hasn"t any effect on the training process, where it is used for measuring the generalization ability of the network, and evaluated network performance [13].…”
Section: Figure I Model Impementation Steps Flowchartmentioning
confidence: 99%
“…Floor area parameter, Number of stories, Slab type, Foundation type, Number of elevators, Type of project, Type of project and External finishing parameter were adopted by several researchers [5]; [12]; [13]; [2]; [4]; [14]; [15].…”
Section: Parametric Cost Factorsmentioning
confidence: 99%
“…However, the authors reckon that the researches and studies on utilizing neural networks to estimate the cost of construction projects at various stages of the work are very limited [12] , [13].…”
The purpose of this paper is to develop a model for forecasting early design construction cost of building projects using Artificial Neural Network (ANN). Eighty questionnaires distributed among construction organizations were utilized to identify significant parameters for the building project costs. 169 case studies of building projects were collected from the construction industry in Gaza Strip. The case studies were used to develop ANN model. Eleven significant parameters were considered as independent input variables affected on "project cost". The neural network model reasonably succeeded in estimating building projects cost without the need for more detailed drawings. The average percentage error of tested dataset for the adapted model was largely acceptable (less than 6%). Sensitivity analysis showed that the area of typical floor and number of floors are the most influential parameters in building cost.
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