Cost prediction of road construction projects in Pakistan using machine learning

The road construction sector is integral to economic growth in developing countries. However, road construction projects in Pakistan frequently experience budget overruns and delays, largely due to inaccurate cost prediction practices. These traditional techniques rely on expert judgement and analogies to past projects, which are often imprecise and subject to human biases. This study aims to enhance cost prediction process for road construction projects in Pakistan by developing a machine learning-based prediction model. To this end, a dataset of road construction projects is prepared using information from Planning Commission Forms I, obtained from several departments of the government of Pakistan. The dataset consists of 86 samples and includes macro-level features such as project duration, road dimensions, and road type. An Artificial Neural Network model is trained using this dataset to predict project costs. The model showed promising results, achieving MAPE and R2 scores of 49.6% and 0.89, respectively, and making better predictions than human estimates on over-budget projects. Interviews conducted with project managers for qualitative validation of the dataset and model also backed this hypothesis. These findings highlight the feasibility and benefits of adopting machine learning models to reduce budget overruns and enhance planning efficiency in road construction projects.