Browsing by Author "Palitha, S"

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    PublicationOpen Access
    Influence of Crumb Rubber and Coconut Coir on Strength and Durability Characteristics of Interlocking Paving Blocks
    (MDPI, 2022-07-13) Gamage, S; Palitha, S; Meddage, D. P. P; Mendis, S; Azamathulla, H. M; Rathnayake, U
    Interlocking Paving Blocks (IPB) are, nowadays, a widely used construction material. As a result of the surge in demand for IPBs, alternative materials have been investigated to be used for IPBs. This study investigated the strength and durability characteristics (compressive strength, split tensile strength, density, water absorption, skid resistance, and abrasion resistance) of IPBs in the presence of (waste materials) crumb rubber (CR) and coconut coir fibers (CCF). Both compressive and split tensile strength increased in the presence of CCF to a certain extent. CR-based IPBs showcased an increase in skid resistance that satisfied both SLS 1425 and BS EN 1338 specifications. Abrasion depths of CR-based and CCF-based samples show a comparable increase in values when the respective fraction (CR or CCF) increases. Therefore, this research fills the knowledge gap, highlighting the importance of incorporating waste materials (CR and CCF) for the IPB industry rather than open dumping.
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    PublicationOpen Access
    Predicting adhesion strength of micropatterned surfaces using gradient boosting models and explainable artificial intelligence visualizations
    (Elsevier, 2023-06-27) Ekanayake, I.U; Palitha, S; Gamage, S; Meddage, D.P.P.; Wijesooriya, K; Mohotti, D
    Fibrillar dry adhesives are widely used due to their effectiveness in air and vacuum conditions. However, their performance depends on various factors. Previous studies have proposed analytical methods to predict adhesion strength on micro-patterned surfaces. However, the method lacks interpretation on which parameters are critical. This research utilizes gradient-boosting machine learning (ML) algorithms to accurately predict adhesion strength. Additionally, explainable machine learning (XML) methods are employed to interpret the underlying reasoning behind the predictions. The analysis demonstrates that gradient boosting models achieve a high correlation coefficient (R > 0.95) in accurately predicting pull-off force on micro-patterned surfaces. The use of XML methods provides insights into the importance of features, their interactions, and their contributions to specific predictions. This novel, explainable, and data-driven approach holds potential for real-time applications, aiding in the identification of critical features that govern the performance of fibrillar adhesives. Furthermore, it improves end-users’ confidence by offering human-comprehensible explanations and facilitates understanding among non-technical audiences