Supervised Intelligence Committee Machine to Evaluate Field Performance of Photocatalytic Asphalt Pavement for Ambient Air Purification

Abstract

The ability of a titanium dioxide (TiO₂) photocatalytic nanoparticle to trap and to decompose organic and inorganic air pollutants makes it a promising technology as a pavement coating to mitigate the harmful effects of vehicle emissions. Statistical models and artificial intelligence (AI) models are two applicable methods to quantify photocatalytic efficiency. The objective of this study was to develop a model based on field-collected data to predict the nitrogen oxide (NO_x) reduction. To achieve this objective, the supervised intelligent committee machine (SICM) method as a combinational black box model was used to predict NO_x concentration at the pavement level before and after TiO₂ application on the pavement surface. SICM predicts NO_x concentration by a nonlinear combination of individual AI models through an artificial intelligent system. Three AI models—Mamdani fuzzy logic, artificial neural network, and neuro-fuzzy—were used to predict NO_x concentration in the air as a function of traffic count and climatic conditions, including humidity, temperature, solar radiation, and wind speed before and after the application of TiO₂. In addition, an intelligent committee machine model was developed by combining individual AI model output linearly through a set of weights. Results indicated that the SICM model could provide a better prediction of NO_x concentration as an air pollutant in the complex and multidimensional air quality data analysis with less residual mean square error than that given by multivariate regression models.