Construction project delay is a global phenomenon. The delay risk being regarded as a main challenge that is tackled via the firms of construction. It possessed an inverse effect upon the performance of the project resulting in cost overruns and productivity reduction. In Iraq, most construction projects surpassed their prearranged time and were delayed, resulting in a loss of productivity and income. The objective of this paper was to predict the cost and delay of construction projects to illustrate their risks effects by using of artificial neural networks with the particle swarm optimization method (ANN-PSO). Thereby, risk factors were identified and analysed using Probability and Impact Analysis which were embraced as the model inputs. In comparison, the outputs for the models were represented by the ratio of the contractor's profit to project costs and the delay in construction projects. An ANN model was additionally evolved with a backpropagation (BP) optimization method to assess the exhibition of the ANN-PSO model. To evaluate the accuracy of the results of the ANN-PSO model, coefficient of correlation (R), determination coefficient (R2), and root mean squared error (RMSE) was utilized as performance evaluation of the models. The ANN-PSO model showed a significant performance in the delay prediction. The performance evaluation for the cost and delay prediction were (R=0.929, R2=0.863, RMSE=0.044), and (R=0.998, R2=0.996, RMSE=0.094), respectively. The model of ANN-PSO has a virtuous performance in the delay prediction better than the cost. However, the ANN-BP model showed better performance than ANN-PSO in term of cost prediction.
Combining Stream Mining and Neural Networks for Short Term Delay Prediction
