Browsing by Author "Sandamal, K"

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Open Access
Development of Roughness Prediction Model for Sri Lankan Expressways
(Sri Lanka Institute of Information Technology, 2023-03-25) Nilawfer, S; Madushani, S; Sandamal, K; Gomes, A
Expressways play a pivotal role in industrial and export development in Sri Lanka by providing access to the production sector in addition to the passenger transport in between transport hubs. A reliable pavement performance prediction model is essential for pavement management systems to optimize the cost of maintenance and rehabilitation planning. In this study, pavement roughness prediction of expressways in the long-term performance was conducted using International Roughness Index (IRI) which is used as a global parameter to measure the ride comfort of road users and the unevenness of pavement. Firstly, initial IRI values for Sri Lankan expressways were established by using current data and found that, it varies between 0.90 to 1.45 m/km. Secondly, IRI prediction model developed with cumulative traffic volume, considering outer lane IRI as the dependent variable due to higher deterioration rate compared to inner lane. Moreover, it was found that, there is a good relationship between IRI with cumulative traffic with R-squared of 0.60. Further, it can be concluded that, the outcomes of this study can be effectively used for Sri Lankan context in long term performance evaluation and expressway maintenance planning.
Open Access
Pavement Roughness Prediction Using Explainable and Supervised Machine Learning Technique for Long-Term Performance
(MDPI, 2023-06-15) Sandamal, K; Shashiprabha, S; Muttil, N; Rathnayake, U
Maintaining and rehabilitating pavement in a timely manner is essential for preserving or improving its condition, with roughness being a critical factor. Accurate prediction of road roughness is a vital component of sustainable transportation because it helps transportation planners to develop cost-effective and sustainable pavement maintenance and rehabilitation strategies. Traditional statistical methods can be less effective for this purpose due to their inherent assumptions, rendering them inaccurate. Therefore, this study employed explainable and supervised machine learning algorithms to predict the International Roughness Index (IRI) of asphalt concrete pavement in Sri Lankan arterial roads from 2013 to 2018. Two predictor variables, pavement age and cumulative traffic volume, were used in this study. Five machine learning models, namely Random Forest (RF), Decision Tree (DT), XGBoost (XGB), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN), were utilized and compared with the statistical model. The study findings revealed that the machine learning algorithms’ predictions were superior to those of the regression model, with a coefficient of determination (R2) of more than 0.75, except for SVM. Moreover, RF provided the best prediction among the five machine learning algorithms due to its extrapolation and global optimization capabilities. Further, SHapley Additive exPlanations (SHAP) analysis showed that both explanatory variables had positive impacts on IRI progression, with pavement age having the most significant effect. Providing accurate explanations for the decision-making processes in black box models using SHAP analysis increases the trust of road users and domain experts in the predictions generated by machine learning models. Furthermore, this study demonstrates that the use of explainable AI-based methods was more effective than traditional regression analysis in IRI prediction. Overall, using this approach, road authorities can plan for timely maintenance to avoid costly and extensive rehabilitation. Therefore, sustainable transportation can be promoted by extending pavement life and reducing frequent reconstruction.