The Prospective of Artificial Neural Network (ANN’s) Model Application to Ameliorate Management of Post Disaster Engineering Projects
Description
In this paper, We have studied a post disaster for emergency reconstruction projects. The software used is SPSS which provide an optimized method to find the optimum number of nodes in the hidden layer using training process according to the theory of ANN. The set of the input data is processed using assumed model parameters and the results outputs are found and compared with the real output , which then adjust the assumed parameters to minimize the sum of square error, this is the algorithm that the ANN use to adjust the parameters to the best values. The data was divided into two sets the training set 20 projects, to build the model (estimating the parameters), then the rest 10 project will used for verification, which proved that the output are reliable. About the originality of this paper, it was first mentioned in the abstract, besides, it is the first time that we invent an application using both ANN’s and Java script to predict deviation in cost and time before even starting the project. Findings were clearly shown in results and discussions. The used approach in analyzing data was the standard statistics and ANN’s approach that was first time used in such studies in Iraq. About the significant points, I shortly briefed that because the focus was on ANN’s first. Then these points should be made even clearer; that there is little use of ANN and delay factors, and that our main findings are related to post-emergency construction projects (like the other paper)