Abstract
The construction duration of residential projects, especially in building processes, significantly impact the business of a construction company. The balance between the planned cost, direct cost, and overheads directly depend on the precision of the implementation phase of the project. The application of the artificial neural network (ANN) to predict the duration of implementation of a residential construction project from the pre-design stage to completion is comprehensively discussed in this research. The study applies the back-propagation (BP) network made of nodes for error evaluation of the training states. Further, the proposed system illustrated that the artificial neural network (ANN) satisfy the three crucial criteria (cost, quality, and time) used for the evaluation of projects. The ANN provided accurate data for the training and estimation of, the duration of a residential construction project with adequate resources of implementation. The performance of the results for the ANN at 105 iteration shows that the prediction was 99.841% accurate for the overall system. The best fit occurred at the 99th epoch with an MSE of 0.10286.
Highlights
The success of contract construction projects is a belt on the realization of the project within the plant time for the agreed cost
This research dwells on the inaugural use of neural networks in the construction industry, but for this research, we create a comprehensive artificial neural network (ANN) model using three vital AI aspects to compute the time for completion of a residential project
Our research considers the performance of ANN in the training state, regression and error histogram [4]
Summary
The success of contract construction projects is a belt on the realization of the project within the plant time for the agreed cost. The conceptual estimates function to evaluate the validity of the potential project, i.e., decide on the viability of the project., Considering that the formation of the preliminary parameters is created by the contractor using sparse information, making the use of modern prediction technique very significant such as the utilization of artificial neural networks [6]. This research creates, with the help of an artificial neural network, a design for the conceptual estimation of the length of a residential construction project This proposed integrated model predicts the time to complete the project. The preliminary estimates of the parameters of the project were based on the bill of quantities, which is the most vital information present for the potential project as defined in the completed project database and tender documentation
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