Masonry compressive strength prediction using artificial neural networks

The masonry is not only included among the oldest building materials, but it is also the most widely used material due to its simple construction and low cost compared to the other modern building materials. Nevertheless, there is not yet a robust quantitative method, available in the literature, which can reliably predict its strength, based on the geometrical and mechanical characteristics of its components. This limitation is due to the highly nonlinear relation between the compressive strength of masonry and the geometrical and mechanical properties of the components of the masonry. In this paper, the application of artificial neural networks for predicting the compressive strength of masonry has been investigated. Specifically, back-propagation neural network models have been used for predicting the compressive strength of masonry prism based on experimental data available in the literature. The comparison of the derived results with the experimental findings demonstrates the ability of artificial neural networks to approximate the compressive strength of masonry walls in a reliable and robust manner.- (undefined

Worthy to Lose Some Money for Better Air Quality: Applications of Bayesian Networks on the Causal Effect of Income and Air Pollution on Life Satisfaction in Switzerland

One important determinant of well-being is the environmental quality. Many countries apply environmental regulations, reforms and policies for its improvement. However, the question is how the people value the environment, including the air quality. This study examines the association between air pollution and life satisfaction using the Swiss Household Panel survey over the years 2000–2013. We follow a Bayesian network (BN) strategy to estimate the causal effect of the income and air pollution on life satisfaction. We look at five main air pollutants: the ground-level ozone (O3), sulphur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO) and particulate matter of 10 micrometres (PM10). Then, we calculate the individuals’ marginal willingness to pay (MWTP) of reducing air pollution that aims to improve their life satisfaction. Beside the BN model, we take advantage of the panel structure of our data and we follow two approaches as robustness check. This includes the adapted probit fixed effects and the generalised methods of moments system. Our findings show that O3 and PM10 present the highest MWTP values ranging between $8000 and$12,000, followed by the remained air pollutants with MWTP extending between $2000 and$6500. Applying the BNs, we find that the causal effect of income on life satisfaction is substantially increased. We also show the causal effects of air pollutants remain almost the same, leading to lower values of willingness to pay

SURFACE ROUGHNESS PREDICTION OF ELECTRO-DISCHARGE MACHINED COMPONENTS USING ARTIFICIAL NEURAL NETWORKS

Electro-Discharge machining (EDM) is a thermal process comprising a complex metal removal mechanism, which involves the formation of a plasma channel between the tool and the workpiece electrodes leading to the melting and evaporation of the material to be removed. EDM is considered especially suitable for machining complex contours with high accuracy, as well as for materials that are not amenable to conventional removal methods. However, several phenomena negatively affecting the surface integrity of EDMed workpieces need to be taken into account and studied in order to achieve the optimization of the process. Recently, artificial neural networks (ANN) have emerged as a novel modeling technique capable to provide reliable results and readily integrated into a lot of technological areas. In this paper, ANN models for the prediction of the mean surface roughness of electro-discharge machined surfaces are presented. The comparison of the derived results with experimental findings demonstrates the promising potential of using back propagation neural networks (BPNNs) for the reliable and robust approximation of the Surface Roughness of Electro-discharge Machined Components

SURFACE ROUGHNESS PREDICTION OF ELECTRO-DISCHARGE MACHINED COMPONENTS USING ARTIFICIAL NEURAL NETWORKS

Electro-Discharge machining (EDM) is a thermal process comprising a complex metal removal mechanism, which involves the formation of a plasma channel between the tool and the workpiece electrodes leading to the melting and evaporation of the material to be removed. EDM is considered especially suitable for machining complex contours with high accuracy, as well as for materials that are not amenable to conventional removal methods. However, several phenomena negatively affecting the surface integrity of EDMed workpieces need to be taken into account and studied in order to achieve the optimization of the process. Recently, artificial neural networks (ANN) have emerged as a novel modeling technique capable to provide reliable results and readily integrated into a lot of technological areas. In this paper, ANN models for the prediction of the mean surface roughness of electro-discharge machined surfaces are presented. The comparison of the derived results with experimental findings demonstrates the promising potential of using back propagation neural networks (BPNNs) for the reliable and robust approximation of the Surface Roughness of Electro-discharge Machined Components