Early stage cost prediction model for Indian building construction projects using artificial neural networks

Purpose – This paper aims to enhance early-stage cost estimation in construction projects, a critical factor in project feasibility, funding, resource allocation and scheduling. Traditional cost estimation approaches suffer from limitations such as the absence of structured methodologies, assumptions of linear cost relationships, prolonged processes and expert judgment variations. To address these challenges, this study proposes a reliable cost prediction model based on artificial neural networks (ANNs) for building construction projects in India. Design/methodology/approach – To develop cost prediction model, this study collected data from 377 building construction projects in India, encompassing 17 essential cost parameters. The methodology involves data preprocessing, constructing features and fine-tuning ANN hyperparameters meticulously to achieve optimal performance. Findings – The research showcases effectiveness of cost prediction model, evident in significantly reduced mean square error values. ANN-based prediction model excels in handling nonlinear cost dependencies and diverse project complexities, making it a valuable tool for early-stage cost estimation. Research limitations/implications – ANN-based cost prediction model is primarily designed for predicting costs associated with structural works of building projects. Practical implications – The proposed solution offers stakeholders a robust data-driven decision-making tool during initial phases of construction projects. This can lead to more successful and economically viable outcomes. Originality/value – This research examines the drawbacks of traditional cost estimation methods by presenting a data-driven approach leveraging machine learning. It significantly improves precision of early cost forecasts in construction projects while offering practical value to industry.