Faculty of Engineering

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Author: search.filters.author.Mohotti, DHas files: Yes

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    PublicationEmbargo
    Machine learning prediction of web-crippling strength in cold-formed steel beams with staggered slotted perforations
    (Elsevier Ltd, 2025-01) Gatheeshgar, P; Ranasinghe R.S.S; Simwanda, L; Meddage D.P.P.; Mohotti, D
    The application of staggered slotted perforations in cold-formed steel (CFS) members is increasingly prominent in modern construction. Understanding the web-crippling strength of CFS beams, especially those with staggered slotted perforations, is crucial in structural engineering. This study employs machine learning (ML) models to predict the web-crippling strength of these beams under one-flange loading conditions, specifically interior-one-flange and end-one-flange loading. The research utilises a comprehensive dataset comprising 576 web-crippling strength results obtained through numerical modelling. The dataset includes parameters such as yield strength, thickness, corner radius, slot length, slot width, and bearing plate length. Four different ML algorithms—k-nearest neighbour (KNN), random forest (RF), support vector regression (SVR), and artificial neural network (ANN)—are developed and evaluated. Performance metrics, including coefficient of determination (R²), mean squared error (MSE), root mean square error (RMSE), mean absolute error (MAE) and mean normalised bias (MNB) are used to assess model accuracy. The random forest model outperforms others in both the training and testing phases. Shapley additive explanation (SHAP) and partial dependence plots further analyse the influence of input features on web crippling strength. This study presents a robust ML-based approach for predicting web crippling strength, providing engineers with a time-efficient alternate method.
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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