Research Publications Authored by SLIIT Staff

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This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.

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Has files: YesSubject: search.filters.subject.Risk management

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    PublicationOpen Access
    Critical risk factors influencing the management of disruptions in construction projects: Insights from recent challenges in Sri Lanka
    (Elsevier Ltd, 2025-01) Rajapakshe, W
    The construction industry has a significantly contribute to the economy of Sri Lanka. However, in recent years, its overall share of the national output has declined, primarily due to the impacts of the COVID-19 pandemic, political instability, and ongoing economic challenges. Many construction firms halted projects and laid off employees, highlighting the critical need for effective risk management during crises to predict and mitigate risks. This study explores emerging risk factors in Sri Lanka's construction sector post-crisis. Using a three-phase linear decision-making model, the research combines a literature review, a survey of 290 construction professionals, and structural equation modelling (SEM) to identify major risk factors across four phases of risk management. Key risks out of 23 include general factors like health and safety issues, material costs, regulations, political interference, corruption, and labor shortages, while phase-specific risks involve delays, budget overruns, payment delays, and cancellations. The study revises the risk registers to improve risk management strategies. While the findings are context-specific to Sri Lanka, they may offer indicative insights for other developing countries facing similar crisis-driven disruptions. The practical implications extend to multinational and local companies, supported by data from 22 countries, offering a comprehensive framework for addressing construction industry challenges in volatile environments. The novelty of this study lies in its use of theoretical triangulation to align Classical Risk Management theory with real-world operational risk factors, revealing critical overlaps, behavioral influences, and contextual gaps in traditional frameworks.
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    PublicationOpen Access
    Comparison of cardiovascular risk prediction models developed using machine learning based on data from a Sri Lankan cohort with World Health Organization risk charts for predicting cardiovascular risk among Sri Lankans: A cohort study
    (BMJ Publishing Group, 2025-01-15) Mettananda, C; Solangaarachchige, M; Haddela, P; Dassanayake, A.S; Kasturiratne, A; Wickremasinghe, R; Kato, N; De Silva, H.J
    Introduction Models derived from non-Sri Lankan cohorts are used for cardiovascular (CV) risk stratification of Sri Lankans. Objective To develop a CV risk prediction model using machine learning (ML) based on data from a Sri Lankan cohort followed up for 10 years, and to compare the predictions with WHO risk charts. Design Cohort study. Setting The Ragama Health Study (RHS), an ongoing, prospective, population-based cohort study of patients randomly selected from the Ragama Medical Office of Heath area, Sri Lanka, focusing on the epidemiology of non-communicable diseases, was used to develop the model. The external validation cohort included patients admitted to Colombo North Teaching Hospital (CNTH), a tertiary care hospital in Sri Lanka, from January 2019 through August 2020. Participants All RHS participants, aged 40-64 years in 2007, without cardiovascular disease (CVD) at baseline, who had complete data of 10-year outcome by 2017, were used for model development. Patients aged 40-74 years admitted to CNTH during the study period with incident CV events or a disease other than an acute CV event (CVE) with complete data for CVD risk calculation were used for external validation of the model. Methods Using the follow-up data of the cohort, we developed two ML models for predicting 10-year CV risk using six conventional CV risk variables (age, gender, smoking status, systolic blood pressure, history of diabetes, and total cholesterol level) and all available variables (n=75). The ML models were derived using classification algorithms of the supervised learning technique. We compared the predictive performance of our ML models with WHO risk charts (2019, Southeast Asia) using area under the receiver operating characteristic curves (AUC-ROC) and calibration plots. We validated the 6-variable model in an external hospital-based cohort. Results Of the 2596 participants in the baseline cohort, 179 incident CVEs were observed over 10 years. WHO risk charts predicted only 10 CVEs (AUC-ROC: 0.51, 95% CI 0.42 to 0.60), while the new 6-variable ML model predicted 125 CVEs (AUC-ROC: 0.72, 95% CI 0.66 to 0.78) and the 75-variable ML model predicted 124 CVEs (AUC-ROC: 0.74, 95% CI 0.68 to 0.80). Calibration results (Hosmer-Lemeshow test) for the 6-variable ML model and the WHO risk charts were χ 2 =12.85 (p=0.12) and χ 2 =15.58 (p=0.05), respectively. In the external validation cohort, the sensitivity, specificity, positive predictive value, negative predictive value, and calibration of the 6-variable ML model and the WHO risk charts, respectively, were: 70.3%, 94.9%, 87.3%, 86.6%, χ 2 =8.22, p=0.41 and 23.7%, 79.0%, 35.8%, 67.7%, χ 2 =81.94, p<0.0001. Conclusions ML-based models derived from a cohort of Sri Lankans improved the overall accuracy of CV-risk prediction compared with the WHO risk charts for this cohort of Southeast Asians.