Browsing by Author "Meddage, D.P.P"

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Open Access
Evaluating expressway traffic crash severity by using logistic regression and explainable & supervised machine learning classifiers
(Elsevier, 2023-07-09) Shashiprabha, M.J.P.S; Kelum, S.R.M; Meddage, D.P.P; Pasindu, H.R; Gomes, P.I.A
The number of expressway road accidents in Sri Lanka has significantly increased (by 20%) due to the expansion of the transport network and high traffic volume. It is crucial to identify the causes of these crashes for effective road safety management. However, traditional statistical methods may be insufficient due to their inherent assumptions. This study utilized explainable machine learning to investigate the factors that affect the severity of traffic crashes on expressways. The study evaluated two groups of traffic crashes: fatal or severe crashes, and other crashes that included non-severe injuries or only property damage. Five factors that contribute to crashes were analyzed: road surface condition, road alignment, location, weather condition, and lighting effect. Four machine learning models (Random Forest (RF), Decision Tree (DT), extreme gradient boosting (XGB), K-Nearest Neighbor (KNN)) were developed and compared with Logistic Regression (LR) using 223 training and 56 testing data instances. The study revealed that the machine learning algorithms provided more accurate predictions than the LR model. To explain the machine learning models, Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) were used. These methods revealed that all five features decreased the possibility of occurrence of fatal accidents. SHAP and LIME explanations confirmed the known interactions between factors influencing crash severity in expressway operational conditions. These explanations increase the trust of end-users and domain experts on machine learning models. Furthermore, the study concluded that using explainable machine learning methods is more effective than traditional regression analysis in evaluating safety performance. Additionally, the results of the study can be utilized to improve road safety by providing accurate explanations for decision-making processes for black-box models. © 2023
Open Access
Exploring the applicability of expanded polystyrene (EPS) based concrete panels as roof slab insulation in the tropics
(Elsevier Ltd, 2022-07) Meddage, D.P.P; Chadee, A; Jayasinghe, M. T. R; Rathnayake, U
Heat transfer through roof slabs significantly increases the operational energy consumption of buildings. Therefore, passive implementations are necessary to improve the thermal performance of roof slabs in tropical climates. This paper presents a novel roof slab insulation using expanded polystyrene (EPS) based lightweight concrete panels. The workflow consists of field experiments and numerical simulations performed in Design Builder. Moreover, we offered a holistic life-cycle approach to investigate the economic and environmental feasibility of alternate forms. Accordingly, the roof slab with 75 mm EPS insulation and a white exposed surface performed satisfactorily. Corresponding decrease in life cycle cost, carbon emission (kgCO2e), and operational energy consumption were 8.3%, 20%, and 41%, respectively. The overall eco-efficiency index (EEI) implies that the recommended insulation system is environmentally and economically feasible under tropical climatic conditions. Further, manufacturing EPS concrete is eco-friendly since it reduces EPS waste content which does not decay through natural means.