Rolling dynamic compaction (RDC),which employs non-circular module towed behind a tractor,is an innovative soil compaction method that has proven to be successful in many ground improvement applications.RDC involves r...Rolling dynamic compaction (RDC),which employs non-circular module towed behind a tractor,is an innovative soil compaction method that has proven to be successful in many ground improvement applications.RDC involves repeatedly delivering high-energy impact blows onto the ground surface,which improves soil density and thus soil strength and stiffness.However,there exists a lack of methods to predict the effectiveness of RDC in different ground conditions,which has become a major obstacle to its adoption.For this,in this context,a prediction model is developed based on linear genetic programming (LGP),which is one of the common approaches in application of artificial intelligence for nonlinear forecasting.The model is based on in situ density-related data in terms of dynamic cone penetrometer (DCP) results obtained from several projects that have employed the 4-sided,8-t impact roller (BH-1300).It is shown that the model is accurate and reliable over a range of soil types.Furthermore,a series of parametric studies confirms its robustness in generalizing data.In addition,the results of the comparative study indicate that the optimal LGP model has a better predictive performance than the existing artificial neural network (ANN) model developed earlier by the authors.展开更多
Rolling Dynamic Compaction(RDC),which is a ground improvement technique involving non-circular modules drawn behind a tractor,has provided the construction industry with an improved ground compaction capability,especi...Rolling Dynamic Compaction(RDC),which is a ground improvement technique involving non-circular modules drawn behind a tractor,has provided the construction industry with an improved ground compaction capability,especially with respect to a greater influence depth and a higher speed of compaction,resulting in increased productivity. However,to date,there is no reliable method to predict the effectiveness of RDC in a range of ground conditions. This paper presents a new and unique predictive tool developed by means of artificial neural networks(ANNs) that permits a priori prediction of density improvement resulting from a range of ground improvement projects that employed 4-sided RDC modules;commercially known as"impact rollers". The strong coefficient of correlation(i.e. R>0.86) and the parametric behavior achieved in this study indicate that the model is successful in providing reliable predictions of the effectiveness of RDC in various ground conditions.展开更多
Sri Lanka is considered a highly fluctuating economy in the South Asian region.Understanding the behavior of economics is of utmost important to obtain the maximum benefit.Stock market can be considered as one of the ...Sri Lanka is considered a highly fluctuating economy in the South Asian region.Understanding the behavior of economics is of utmost important to obtain the maximum benefit.Stock market can be considered as one of the key influencers to the economy whereas the behavior of the stock market would highly define the behaviors of the economic system.It is required to identify the stock market measures and their contribution for the market development to recognize the influence of stock market.The immense importance of its actions on the market performance leads to find more about the stock market’s measures.This research contains the evidence of the study conducted to identify the stock markets development and behavior measures such as all share price index,market capitalization,dividend yield,price to earnings ratio and shares traded equity.All of these variables were used to obtain a model to describe and predict performance of stock market over the time.The secondary data from the CSE(Colombo Stock Exchange)is studied which the trend analysis was conducted for each series of data and results were used for the analysis.A statistical analysis was carried out to identify the measures of stock market depicts that all the measures of the stock market have influences on the stock market development except for the dividend yield,a useful fact in the process of decision making in many aspects.展开更多
基金supported under Australian Research Council’s Discovery Projects funding scheme(project No. DP120101761)
文摘Rolling dynamic compaction (RDC),which employs non-circular module towed behind a tractor,is an innovative soil compaction method that has proven to be successful in many ground improvement applications.RDC involves repeatedly delivering high-energy impact blows onto the ground surface,which improves soil density and thus soil strength and stiffness.However,there exists a lack of methods to predict the effectiveness of RDC in different ground conditions,which has become a major obstacle to its adoption.For this,in this context,a prediction model is developed based on linear genetic programming (LGP),which is one of the common approaches in application of artificial intelligence for nonlinear forecasting.The model is based on in situ density-related data in terms of dynamic cone penetrometer (DCP) results obtained from several projects that have employed the 4-sided,8-t impact roller (BH-1300).It is shown that the model is accurate and reliable over a range of soil types.Furthermore,a series of parametric studies confirms its robustness in generalizing data.In addition,the results of the comparative study indicate that the optimal LGP model has a better predictive performance than the existing artificial neural network (ANN) model developed earlier by the authors.
基金supported under Australian Research Council's Discovery Projects funding scheme (project number DP120101761)
文摘Rolling Dynamic Compaction(RDC),which is a ground improvement technique involving non-circular modules drawn behind a tractor,has provided the construction industry with an improved ground compaction capability,especially with respect to a greater influence depth and a higher speed of compaction,resulting in increased productivity. However,to date,there is no reliable method to predict the effectiveness of RDC in a range of ground conditions. This paper presents a new and unique predictive tool developed by means of artificial neural networks(ANNs) that permits a priori prediction of density improvement resulting from a range of ground improvement projects that employed 4-sided RDC modules;commercially known as"impact rollers". The strong coefficient of correlation(i.e. R>0.86) and the parametric behavior achieved in this study indicate that the model is successful in providing reliable predictions of the effectiveness of RDC in various ground conditions.
文摘Sri Lanka is considered a highly fluctuating economy in the South Asian region.Understanding the behavior of economics is of utmost important to obtain the maximum benefit.Stock market can be considered as one of the key influencers to the economy whereas the behavior of the stock market would highly define the behaviors of the economic system.It is required to identify the stock market measures and their contribution for the market development to recognize the influence of stock market.The immense importance of its actions on the market performance leads to find more about the stock market’s measures.This research contains the evidence of the study conducted to identify the stock markets development and behavior measures such as all share price index,market capitalization,dividend yield,price to earnings ratio and shares traded equity.All of these variables were used to obtain a model to describe and predict performance of stock market over the time.The secondary data from the CSE(Colombo Stock Exchange)is studied which the trend analysis was conducted for each series of data and results were used for the analysis.A statistical analysis was carried out to identify the measures of stock market depicts that all the measures of the stock market have influences on the stock market development except for the dividend yield,a useful fact in the process of decision making in many aspects.
