Papers by pasindu meddage
Fibrillar dry adhesives are widely used due to their effectiveness in air and vacuum conditions. ... more 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.
Climate
Climate change has had a significant impact on the tourism industry in many countries, leading to... more Climate change has had a significant impact on the tourism industry in many countries, leading to changes in policies and adaptations to attract more visitors. However, there are few studies on the effects of climate change on Sri Lanka’s tourism industry and income, despite its importance as a destination for tourists. A study was conducted to analyze the holiday climate index (HCI) for Sri Lanka’s urban and beach destinations to address this gap. The analysis covered historical years (2010–2018) and forecasted climatic scenarios (2021–2050 and 2071–2100), and the results were presented as colored maps to highlight the importance of HCI scores. Visual analysis showed some correlation between HCI scores and tourist arrivals, but the result of the overall correlation analysis was not significant. However, a country-specific correlation analysis revealed interesting findings, indicating that the changing climate can be considered among other factors that impact tourist arrivals. The r...
Case Studies in Construction Materials
Heat transfer through roof slabs significantly increases the operational energy consumption of bu... more 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 (kgCO 2 e), 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.
Buildings
Interlocking Paving Blocks (IPB) are, nowadays, a widely used construction material. As a result ... more 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.
Sensors
Predicting the bulk-average velocity (UB) in open channels with rigid vegetation is complicated d... more Predicting the bulk-average velocity (UB) in open channels with rigid vegetation is complicated due to the non-linear nature of the parameters. Despite their higher accuracy, existing regression models fail to highlight the feature importance or causality of the respective predictions. Therefore, we propose a method to predict UB and the friction factor in the surface layer (fS) using tree-based machine learning (ML) models (decision tree, extra tree, and XGBoost). Further, Shapley Additive exPlanation (SHAP) was used to interpret the ML predictions. The comparison emphasized that the XGBoost model is superior in predicting UB (R = 0.984) and fS (R = 0.92) relative to the existing regression models. SHAP revealed the underlying reasoning behind predictions, the dependence of predictions, and feature importance. Interestingly, SHAP adheres to what is generally observed in complex flow behavior, thus, improving trust in predictions.
Fluids
Surface water quality is degraded due to industrialization; however, it is one of the widely used... more Surface water quality is degraded due to industrialization; however, it is one of the widely used sources for water supply systems worldwide. Thus, the polluted water creates significant issues for the health of the end users. However, poor attention and concern can be identified on this important issue in most developing countries, including Sri Lanka. The Kelani River in Sri Lanka is the heart of the water supply of the whole Colombo area and has the water intake for drinking purposes near an industrialized zone (Biyagama). Therefore, this study intends to analyze the effect of industrialization on surface water quality variation of the Kelani River basin in Sri Lanka in terms of the water quality index (WQI). We proposed a regression model to predict the WQI using the water quality parameters. Nine water quality parameters, including pH, total phosphate, electric conductivity, biochemical oxygen demand, temperature, nitrates, dissolved oxygen, chemical oxygen demand, and chlorine...
Journal of Wind Engineering and Industrial Aerodynamics
This study used explainable machine learning (XML), a new branch of Machine Learning (ML), to elu... more This study used explainable machine learning (XML), a new branch of Machine Learning (ML), to elucidate how ML models make predictions. Three tree-based regression models, Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boost (XGB), were used to predict the normalized mean (C p,mean), fluctuating (C p,rms), minimum (C p,min), and maximum (C p,max) external wind pressure coefficients of a low-rise building with fixed dimensions in urban-like settings for several wind incidence angles. Two types of XML were usedfirst, an intrinsic explainable method, which relies on the DT structure to explain the inner workings of the model, and second, SHAP (SHapley Additive exPlanations), a post-hoc explanation technique used particularly for the structurally complex XGB. The intrinsic explainable method proved incapable of explaining the deep tree structure of the DT, but SHAP provided valuable insights by revealing various degrees of positive and negative contributions of certain geometric parameters, the wind incidence angle, and the density of buildings that surround a low-rise building. SHAP also illustrated the relationships between the above factors and wind pressure, and its explanations were in line with what is generally accepted in wind engineering, thus confirming the causality of the ML model's predictions.
Asian Journal of Civil Engineering
Buildings require energy to maintain their performance. In consequence, built environments cause ... more Buildings require energy to maintain their performance. In consequence, built environments cause a surge in the world's energy demand. Providing passive measures is an effective method of optimizing operational energy usage. In this study, we propose insulation materials (thermal barrier type and resistive insulation) for the walls of a building. Experiments were performed on small-scale physical models constructed with; (a) no insulation, (b) sawdust-cement mortar, and (c) retroreflective (RR) material for external walls. In addition, regression models were developed to predict indoor air temperature with insulation. Subsequently, associated operational energy-saving and decrease in emissions were estimated for each material. The comparison reveals RR (sawdust-cement mortar) is effective in warm (overcast) climatic conditions. Developed regression models have shown a good agreement with experimental results (R > 0.8). Moreover, sawdust-cement mortar (RR) materials contributed a 9% (13.4%) reduction in operational energy and a 9% (13.3%) decrease in CO 2 emissions. The project highlights the potential to utilize sawdust-a waste material-and RR material as wall insulation to decrease intense operational energy demand.
Case Studies in Construction Materials
Machine learning (ML) techniques are often employed for the accurate prediction of the compressiv... more Machine learning (ML) techniques are often employed for the accurate prediction of the compressive strength of concrete. Despite higher accuracy, previous ML models failed to interpret the rationale behind predictions. Model interpretability is essential to appeal to the interest of domain experts. Therefore, overcoming research gaps identified, this research study proposes a way to predict the compressive strength of concrete using supervised ML algorithms (Decision tree, Extra tree, Adaptive boost (AdaBoost), Extreme gradient boost (XGBoost), Light gradient boosting method (LGBM), and Laplacian Kernel Ridge Regression (LKRR). Alternatively, SHapley Additive exPlainations (SHAP)-a novel black-box interpretation approach-was employed to elucidate the predictions. The comparison revealed that tree-based algorithms and LKRR provide acceptable accuracy for compressive strength predictions. Moreover, XGBoost and LKRR algorithms evinced superior performance (R ¼ 0.98). According to SHAP interpretation, XGBoost predictions capture complex relationships among the constituents. On the other hand, SHAP provides unified measures on feature importance and the impact of a variable for a prediction. Interestingly, SHAP interpretations were in accordance with what is generally observed in the compressive behavior of concrete, thus validating the causality of ML predictions.
Buildings
Conventional methods of estimating pressure coefficients of buildings retain time and cost constr... more Conventional methods of estimating pressure coefficients of buildings retain time and cost constraints. Recently, machine learning (ML) has been successfully established to predict wind pressure coefficients. However, regardless of the accuracy, ML models are incompetent in providing end-users’ confidence as a result of the black-box nature of predictions. In this study, we employed tree-based regression models (Decision Tree, XGBoost, Extra-tree, LightGBM) to predict surface-averaged mean pressure coefficient (Cp,mean), fluctuation pressure coefficient (Cp,rms), and peak pressure coefficient (Cp,peak) of low-rise gable-roofed buildings. The accuracy of models was verified using Tokyo Polytechnic University (TPU) wind tunnel data. Subsequently, we used Shapley Additive Explanations (SHAP) to explain the black-box nature of the ML predictions. The comparison revealed that tree-based models are efficient and accurate in wind-predicting pressure coefficients. Interestingly, SHAP provid...
Materials today communications, 2023
Fibrillar dry adhesives are widely used due to their effectiveness in air and vacuum conditions. ... more 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.
Sensors
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Fluids, 2022
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Climate, 2023
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Buildings, 2022
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Buildings, 2022
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Asian journal of civil engineering, 2022
Buildings require energy to maintain their performance. In consequence, built environments cause ... more Buildings require energy to maintain their performance. In consequence, built environments cause a surge in the world's energy demand. Providing passive measures is an effective method of optimizing operational energy usage. In this study, we propose insulation materials (thermal barrier type and resistive insulation) for the walls of a building. Experiments were performed on small-scale physical models constructed with; (a) no insulation, (b) sawdust-cement mortar, and (c) retroreflective (RR) material for external walls. In addition, regression models were developed to predict indoor air temperature with insulation. Subsequently, associated operational energy-saving and decrease in emissions were estimated for each material. The comparison reveals RR (sawdust-cement mortar) is effective in warm (overcast) climatic conditions. Developed regression models have shown a good agreement with experimental results (R > 0.8). Moreover, sawdust-cement mortar (RR) materials contributed a 9% (13.4%) reduction in operational energy and a 9% (13.3%) decrease in CO 2 emissions. The project highlights the potential to utilize sawdust-a waste material-and RR material as wall insulation to decrease intense operational energy demand.
Case studies in construction materials, 2022
Heat transfer through roof slabs significantly increases the operational energy consumption of bu... more 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 (kgCO 2 e), 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.
Case studies in construction materials, 2022
Machine learning (ML) techniques are often employed for the accurate prediction of the compressiv... more Machine learning (ML) techniques are often employed for the accurate prediction of the compressive strength of concrete. Despite higher accuracy, previous ML models failed to interpret the rationale behind predictions. Model interpretability is essential to appeal to the interest of domain experts. Therefore, overcoming research gaps identified, this research study proposes a way to predict the compressive strength of concrete using supervised ML algorithms (Decision tree, Extra tree, Adaptive boost (AdaBoost), Extreme gradient boost (XGBoost), Light gradient boosting method (LGBM), and Laplacian Kernel Ridge Regression (LKRR). Alternatively, SHapley Additive exPlainations (SHAP)-a novel black-box interpretation approach-was employed to elucidate the predictions. The comparison revealed that tree-based algorithms and LKRR provide acceptable accuracy for compressive strength predictions. Moreover, XGBoost and LKRR algorithms evinced superior performance (R ¼ 0.98). According to SHAP interpretation, XGBoost predictions capture complex relationships among the constituents. On the other hand, SHAP provides unified measures on feature importance and the impact of a variable for a prediction. Interestingly, SHAP interpretations were in accordance with what is generally observed in the compressive behavior of concrete, thus validating the causality of ML predictions.
Journal of wind engineering and industrial aerodynamics, 2022
This study used explainable machine learning (XML), a new branch of Machine Learning (ML), to elu... more This study used explainable machine learning (XML), a new branch of Machine Learning (ML), to elucidate how ML models make predictions. Three tree-based regression models, Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boost (XGB), were used to predict the normalized mean (C p,mean), fluctuating (C p,rms), minimum (C p,min), and maximum (C p,max) external wind pressure coefficients of a low-rise building with fixed dimensions in urban-like settings for several wind incidence angles. Two types of XML were usedfirst, an intrinsic explainable method, which relies on the DT structure to explain the inner workings of the model, and second, SHAP (SHapley Additive exPlanations), a post-hoc explanation technique used particularly for the structurally complex XGB. The intrinsic explainable method proved incapable of explaining the deep tree structure of the DT, but SHAP provided valuable insights by revealing various degrees of positive and negative contributions of certain geometric parameters, the wind incidence angle, and the density of buildings that surround a low-rise building. SHAP also illustrated the relationships between the above factors and wind pressure, and its explanations were in line with what is generally accepted in wind engineering, thus confirming the causality of the ML model's predictions.
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Papers by pasindu meddage