Authors : Akanni Prince Oluleye; Makinde Olutunji Johnson; Obasa Ayotomide James; Oluwasanya Taiwo

Volume/Issue : Volume 11 - 2026, Issue 9 - September

Google Scholar : https://tinyurl.com/wm326f4e

DOI : https://doi.org/10.38124/ijisrt/26sep1183

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Residential building construction in Lagos State, Nigeria, continues to be affected by recurring structural failures and quality non-conformances linked to an overreliance on manual, inconsistently applied quality control (QC) practices and a fragile regulatory enforcement environment. While international evidence increasingly shows that Machine Learning (ML) technologies can substantially improve construction quality monitoring, no empirical study had systematically examined the existing state of QC practice or the level of ML application specifically within the Lagos State residential construction context. This study evaluate the application of Machine Learning in construction quality control within the study area.

Keywords : Quality Control, Machine Learning, Residential Housing, Construction Technology Adoption.

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