A neural network model for building construction projects cost estimating

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

تقدير تكلفة مشاريع المباني الانشائية في قطاع غزة باستخدام الشبكات العصبية الاصطناعية

Early stage cost estimate plays a significant role in the success of any construction project. All parties involved in the construction of a project; owners, contractors, and donors are in need of reliable information about the cost in the early stages of the project, where very limited drawings and details are available during this stage. This research aims at developing a model to estimate the cost of building construction projects with a high degree of accuracy and without the need for detailed information or drawings by using Artificial Neural Network (ANN). ANN is new approach that is used in cost estimation, which is able to learn from experience and examples and deal with non-linear problems. It can perform tasks involving incomplete data sets, fuzzy or incomplete information and for highly complex problems. In order to build this model, quantitative and qualitative techniques were utilized to identify the significant parameters for the building project costs including skeleton and finishing phases. A database of 169 building projects was collected from the construction industry in Gaza Strip. The ANN model considered eleven significant parameters as independent input variables affected on one dependent output variable "project cost". Neurosolution software was used to train the models. The results of the trained models indicated that neural network reasonably succeeded in estimating the cost of building projects without the need for more detailed drawings. The average error of test dataset for the adapted model was largely acceptable (less than 6%). The performed sensitivity analysis showed that the area of typical floor and number of floors are the most influential parameters in building cost. One of the main recommendations of this research is to join the developed model with cost index to give an accurate estimate in any time. In addition, it encourages all parties involved in construction industry to pay more attention for developing ANN in cost estimation by archiving all projects data, and conducting more studies and workshops to obtain maximum advantage of this new approach and join more outputs in a model