Artificial Neural Network for Green Building Cost Prediction

Domaine:

environment and energy

Type de record:

paper

Créateur:

DepCom

Éditeur:

Way

Hôte:

The increased cost of production is one of the challenges to green building development. The purpose of this study was to develop a cost-predictive model to enhance decision-making for green building development among stakeholders in South-Western Nigeria. Tertiary Education Trust Fund (TETFund) projects executed from 2011 to 2018 constitute the study population. Secondary data, on design parameters and elemental cost details, were collected for the cost-predictive model using the Artificial Neural Network (ANN). The results showed that the ANN model predicts the cost of a green building project with 99% accuracy. The study concludes that the Artificial Neural Network model is a veritable tool to effectively manage the cost of the TETFund green building project’s development with up to 99% accuracy. The study recommends the ANN model for cost prediction in making an informed decision for green building development. The institutions should use the ANN model to forecast proposed green building costs. ANN is useful for benchmarking approvals by the TETFund for green TEIs.

Visit

doi.org

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