Research Publications
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Publication Embargo Fusion of machine learning to enhance the adaptability of lean construction maturity models (LCMMs)(Emerald Publishing, 2026-06-09) Jayanetti, J.K.D.D.T; Fernando, M. H; Ranadewa, K.A.T.O; Perera, B.A.K.S.Purpose – Lean construction maturity models (LCMMs) offer a structured approach to guide the implementation of lean practices in construction organisations. However, in developing countries such as Sri Lanka, their adaptability is constrained by contextual barriers and limited technological integration. Although numerous emerging technologies are available, the integration of machine learning (ML) into LCMMs remains largely unexplored. Thus, this study aims to investigate how ML can be fused with LCMMs to improve their adaptability in the Sri Lankan construction context. Design/methodology/approach – Rooted in pragmatism, the research adopted a qualitative choice, using the Delphi technique. Three rounds of semi-structured interviews were conducted in eight phases to collect data from 25 experts. Data were validated via two cases and analysed using code-based content analysis. Findings – The study identified a seven-stage LCMM, with the seventh level being the “Lean Ideal Level”. In total, 25 barriers were mapped across these seven stages. To address these, ten ML attributes were identified, and their suitability was assessed across each level. A comprehensive integration framework was developed, outlining relevant ML tools, techniques and fusion methods. Organisations at early maturity levels face more barriers, which decrease with progression, while ML attribute suitability shows the opposite trend, with fewer applicable attributes in early stages and more at advanced levels. Originality/value – To the best of the authors’ knowledge, this is the first study to bridge ML and LCMMs for developing countries, offering a structured, evidence-based framework that aligns ML capabilities with maturity stages. It advances theoretical understanding of LCMM adaptability while introducing a novel integration pathway tailored for resource-constrained contexts.Publication Embargo Lean maturity model for the Sri Lankan construction industry: investigation of key model assessing components(Taylor and Francis Ltd., 2026-02-27) Jayanetti J.K.D.D.T.; Perera B.A.K.S; Waidyasekara K.G.A.S.; Siriwardana, M; Ranadewa K.A.T.OImplementing lean practices in the construction industry remains challenging, particularly due to the lack of effective mechanisms to assess lean construction maturity. Despite the presence of limited literature on lean maturity models, no model has been developed specifically for the Sri Lankan construction sector. Addressing this gap, the present study takes an initial step toward developing a Lean Construction Maturity Model tailored to the Sri Lankan context by identifying the essential components required for its assessment. Adopting a pragmatic stance, the research employed the qualitative Delphi technique, involving 73 expert interviews conducted over three iterative rounds, followed by five validation interviews. Directed Content analysis was used to extract key elements for the model. The study identified three core components necessary for assessing lean construction maturity: attributes, process areas, and indicators. Specifically, eight attributes were revealed including Production Efficiency, Waste Elimination, Quality Management, People, Customer Focus, Lean Leadership, Transparency, and Lean Philosophy. These attributes are supported by 28 process areas and 140 indicators. Together, these elements form a structured, layered framework for assessing lean maturity. The study contributes original insights by considering the cultural, economic, and institutional dynamics influencing lean implementation in Sri Lanka. While the findings establish foundational components, further research is needed to develop and validate a complete maturity model. Practically, the study enables a more systematic and locally relevant approach to lean adoption, supporting improved industry performance. Socially, it promotes resource efficiency and project success, contributing to more responsible and sustainable construction practices in the Sri Lankan context.