Integrating Professional Judgement with Machine Learning Models for Enhanced Construction Cost Estimation and Prediction in Contextually Variable Projects Kingston University in United Kingdom

This PhD project at Kingston University, within the Faculty of Engineering, Computing and the Environment, explores the integration of professional judgement with machine learning models to enhance construction cost estimation and prediction, particularly for projects with variable contextual factors. Traditional approaches to construction cost estimation, such as stochastic and parametric methods, have been supplemented by algorithmic tools to improve accuracy and precision. However, the dynamic nature of construction projects—each unique in design, client requirements, and environmental context—means that professional judgement remains central to the selection of data inputs, estimation processes, and evaluation of outputs.

Previous research has attempted to model professional judgement in cost estimation, but there is a significant opportunity to develop a hybrid approach that combines expert evaluation of project-specific contexts with technologically advanced, data-driven predictions based on historical cost data. This project aims to bridge the gap between algorithmic cost estimation methods and the nuanced, context-sensitive judgement of professionals, proposing a framework that leverages machine learning for robust predictions while incorporating expert insights to adapt to the unique characteristics of each construction project.

The research will involve developing and testing hybrid models, analysing the interplay between algorithmic outputs and professional judgement, and evaluating the effectiveness of these models in real-world construction scenarios. The successful candidate will join a vibrant research community at Kingston University and benefit from interdisciplinary collaboration across engineering, computing, and environmental domains.

Funding for this position is available through the Graduate School studentships competition for October 2026 entry. The studentship may cover tuition fees and provide a stipend; applicants are encouraged to consult the Kingston University PhD Studentships page for full details. Eligibility requirements include a strong academic background in civil engineering, computer science, or a related field, with experience or interest in machine learning and construction cost modelling. English language proficiency is required as per university standards.

The application deadline is March 4, 2026. Prospective students should review the Graduate School Studentships information and the Faculty research page for guidance on the application process. This is an excellent opportunity for candidates interested in advancing the state of construction cost estimation through innovative, interdisciplinary research.