Abstract

The construction industry’s digital transformation has not prevented numerous construction projects from experiencing cost overruns. This study aims to investigate the integration patterns of artificial intelligence (AI) into project cost control (PCC) processes within the construction industry over the past decade. Research on cost control practices with AI integration was extracted from the Scopus database over the period of 2015 to 2025. The VOSviewer text-mining tool was utilised to examine the connections within PCC and AI practices in the construction sector. The study found three clusters for the index keywords: Cluster 1: Construction project management and machine learning; Cluster 2: Artificial intelligence in cost control; and Cluster 3: Construction cost. The results presented the top ten authors, the top five countries, and research themes, with Egypt receiving the most citations and India producing the most documents on AI integration in PCC discussions. In practice, researchers can quickly identify key authors, influential journals, and topical areas related to AI in PCC research, thereby promoting more similar research. Construction professionals can use these findings to assess best practices and explore AI cost-control innovations. This study improves understanding of AI integration in PCC within the construction industry by identifying key concepts and consolidating research to aid effective project cost management. Workplace Stressors, Mental Health and

Authors and affiliations

  • Construction Management and Quantity Surveying, University of Johannesburg, Johannesburg, South Africa; Construction Technology and Quantity Surveying, Kumasi Technical University, Kumasi, Ghana

How to cite

Kofi Owusu Adjei, , Murendeni Liphadzi (2026). Integration of Artificial Intelligence into Project Cost Control Practices in the Construction Industry: A Bibliometric Analysis.In: SURE-Built 2026 — 2nd Global Scholarship for Sustainable Built Environment Research Conference. SureBuilt Series.

Related papers

Diagnosing Environmental Drivers of Carbon Monoxide Concentration in Kaduna Metropolis Using Interpretable Machine Learning

Reuben Jobien Jacob, Abdullahi Babagana, Kenneth Onyemauche Ezenwa

SURE-Built 2026 — 2nd Global Scholarship for Sustainable Built Environment Research Conference · Digital Built Environment · September 3, 2026

Reliable spatial information on urban air pollution is essential for effective environmental management and sustainable urban development. This study modelled and interpreted the spatial distribution of carbon monoxide (CO) concentration in Kaduna Metropolis, Nigeria,…

Analysis of Post-Retrofit Changes in Window Operation Behavior in Retrofitted University Buildings Using Machine Learning

Yaseen Jamal, Arshad Al Shaekery Alexander Malkwitz

SURE-Built 2026 — 2nd Global Scholarship for Sustainable Built Environment Research Conference · Digital Built Environment · September 3, 2026

Building energy retrofits are a key strategy for reducing energy consumption in the existing building stock; however, the success of this initiative is dependent on occupant behavior after the retrofit. Specifically, changes in window…

Geospatial Evidence for Sustainable Urban Development

Michael Mutale, Maureen Mukuntoᵃ, Mwape Malama

SURE-Built 2026 — 2nd Global Scholarship for Sustainable Built Environment Research Conference · Digital Built Environment · September 3, 2026

Zambia michael.mutale@cbu.ac.zm Rapid urbanisation in developing countries poses significant threats to land cover integrity and ecological sustainability. This study assesses Land Use and Land Cover (LULC) dynamics in Kitwe District, Zambia, over a twenty-year…