http://ir.lib.ruh.ac.lk/handle/iruor/7343 Sun, 19 Jul 2026 22:51:10 GMT 2026-07-19T22:51:10Z http://ir.lib.ruh.ac.lk/handle/iruor/21498 Improvement of the performance of recycled concrete powder-based artificial aggregates through the acceleration carbonation effect induced by polyethyleneimine admixture Shuqing, Tao; Qijun, Yu; Yang, Yu; Appuhamy, J.M.Ruwan S. Abstract Manufactured sand, recycled concrete powder, and cement were used as raw materials to produce recycled concrete powder-based artificial aggregates (RCP-AA) via cold-bonding granulation technology. The effects of curing methods and polyethyleneimine (PEI) dosage on the macro performance and microstructure of RCP-AA were analyzed. The recycled concrete powder contains a small number of active substances that can react with CO₂ to form CaCO3, thereby enhancing the performance of the artificial aggregates. Additionally, the secondary and tertiary amine groups in PEI molecules react with CO₂ to promote the carbonation process and form carbonates. This process not only improves carbonation efficiency under milder conditions, reducing the dependence on high CO₂ concentrations and pressure, but also significantly enhances the microstructure and performance of the aggregates. The results showed that carbonation and PEI addition significantly increased aggregate density, reduced water absorption, and enhanced single particle strength by promoting CaCO3 formation and improving the microstructure. Specially, the water absorption of RCP-AA reduced from 13.9 % to 9.8 %, and the early strength reached 3.07 MPa at an 8 % PEI dosage. Microstructural characterization further confirmed the effects of carbonation, showing a reduction in Ca(OH)2 content, an increase in CaCO3 formation, significant pore structure optimization, and improved carbonation efficiency and structural uniformity. The maximum CaCO3 generation occurred within the PEI dosage range of 8–12 %, while a 16 % dosage hindered carbonation due to the formation of overly dense polymer films. Considering both performance and practical applicability, an 8 % PEI dosage was recommended as the optimal level for improving the structure and mechanical properties of RCP-AA. This study provides a theoretical foundation for the preparation and curing of sustainable building materials, with the proposed method not only enhancing material performance but also offering significant potential for promoting the application of low-carbon, green materials, thereby supporting the construction industry's environmental sustainability. Fri, 13 Jun 2025 00:00:00 GMT http://ir.lib.ruh.ac.lk/handle/iruor/21498 2025-06-13T00:00:00Z http://ir.lib.ruh.ac.lk/handle/iruor/10716 Hybrid grey exponentials moothing approach for predicting transmission dynamics of the COVID-19 outbreak in Sri Lanka Seneviratna, D.M.K.N.; Rathnayaka, R.M. Kapila Tharanga Purpose – The Coronavirus (COVID-19) is one of the major pandemic diseases caused by a newly discovered virus that has been directly affecting the human respiratory system. Because of the gradually increasing magnitudeoftheCOVID-19pandemicacrosstheworld,ithasbeensparkingemergenciesandcriticalissuesin the healthcare systems around the world. However, predicting the exact amount of daily reported new COVID cases is the most serious issue faced by governments around the world today. So, the purpose of this current study is to propose a novel hybrid grey exponential smoothing model (HGESM) to predicting transmission dynamics of the COVID-19 outbreak properly. Design/methodology/approach – As a result of the complications relates to the traditional time series approaches, the proposed HGESM model is well defined to handle exponential data patterns in multidisciplinary systems. The proposed methodology consists of two parts as double exponential smoothing and grey exponential smoothing modeling approach respectively. The empirical analysis of this study was carried out on the basis of the 3rd outbreak of Covid-19 cases in Sri Lanka, from 1st March 2021 to 15th June 2021. Out of the total 90 daily observations, the first 85% of daily confirmed cases were used during the training, and the remaining 15% of the sample. Findings – The new proposed HGESM is highly accurate (less than 10%) with the lowest root mean square error values in one head forecasting. Moreover, mean absolute deviation accuracy testing results confirmed that the new proposed model has given more significant results than other time-series predictions with the limited samples. Originality/value – The findings suggested that the new proposed HGESM is more suitable and effective for forecasting time series with the exponential trend in a short-term manner. Fri, 01 Jan 2021 00:00:00 GMT http://ir.lib.ruh.ac.lk/handle/iruor/10716 2021-01-01T00:00:00Z http://ir.lib.ruh.ac.lk/handle/iruor/10711 Short-term and long-term dynamics between macroeconomic indicators and market fluctuation: a study of Colombo stock exchange Rathnayaka, R.M. Kapila Tharanga; Seneviratna, D.M.K.N.; Jianguo, Wei As a developing market, the high volatile fluctuations with instability patterns are the common phenomenon in the Colombo Stock Exchange of Sri Lanka. The miscellaneous type of micro and macro-economic conditions directly effect on the market fluctuations. By using Vector Autoregressive Regression and Vector Error Correlation Model to capture the linear inter-dependencies, this study examines the equilibrium relationships between the stock market indices and macro-economic factors in Sri Lankan during the period from January, 2009 to December 2015. The study revealed that macroeconomic variables have direct effect on high volatility in stock market fluctuations in the Colombo Stock Exchange. Furthermore, the results show that Colombo stock exchange is sensitive to the macroeconomic variables such as market capitalization, real gross domestic product and broad money supply. Fri, 01 Jan 2016 00:00:00 GMT http://ir.lib.ruh.ac.lk/handle/iruor/10711 2016-01-01T00:00:00Z http://ir.lib.ruh.ac.lk/handle/iruor/10695 Grey system based novel approach for stock market forecasting Rathnayaka, R.M. Kapila Tharanga; Seneviratna, D.M.K.N.; Jianguo, Wei Purpose – Making decisions in finance have been regarded as one of the biggest challenges in the modern economy today; especially, analysing and forecasting unstable data patterns with limited sample observations under the numerous economic policies and reforms. The purpose of this paper is to propose suitable forecasting approach based on grey methods in short-term predictions. Design/methodology/approach – High volatile fluctuations with instability patterns are the common phenomenon in the Colombo Stock Exchange (CSE), Sri Lanka. As a subset of the literature, very few studies have been focused to find the short-term forecastings in CSE. So, the current study mainlyattemptedtounderstandthetrendsandsuitableforecastingmodelinordertopredictthefuture behaviours in CSE during the period from October 2014 to March 2015. As a result of non-stationary behavioural patterns over the period of time, the grey operational models namely GM(1,1), GM(2,1), grey Verhulst and non-linear grey Bernoulli model were used as a comparison purpose. Findings – Theresultsdisclosedthat,greypredictionmodelsgeneratesmallerforecastingerrorsthan traditional time series approach for limited data forecastings. Practical implications – Finally, the authors strongly believed that, it could be better to use the improved grey hybrid methodology algorithms in real world model approaches. Originality/value – However, for the large sample of data forecasting under the normality assumptions, the traditional time series methodologies are more suitable than grey methodologies; especially GM(1,1) give some dramatically unsuccessful results than auto regressive intergrated moving average in model pre-post stage. Thu, 01 Jan 2015 00:00:00 GMT http://ir.lib.ruh.ac.lk/handle/iruor/10695 2015-01-01T00:00:00Z