Supervised intelligence committee machine to evaluate field performance of photocatalytic asphalt pavement for ambient air purification

Ataallah Nadiri, Marwa M. Hassan, Somayeh Asadi

Research output: Contribution to journalArticle

13 Citations (Scopus)

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 fieldcollected data to predict the nitrogen oxide (NOx) reduction. To achieve this objective, the supervised intelligent committee machine (SICM) method as a combinational black box model was used to predict NOx concentration at the pavement level before and after TiO2 application on the pavement surface. SICM predicts NOx 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 NOx 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 NOx 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.

Original languageEnglish (US)
Pages (from-to)96-105
Number of pages10
JournalTransportation Research Record
Volume2528
DOIs
StatePublished - Jan 1 2015

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Air purification
Asphalt pavements
Nitrogen oxides
Artificial intelligence
Pavements
Air
Solar wind
Intelligent systems
Solar radiation
Air quality
Mean square error
Titanium dioxide
Fuzzy logic
Atmospheric humidity
Nanoparticles
Neural networks

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Mechanical Engineering

Cite this

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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 fieldcollected data to predict the nitrogen oxide (NOx) reduction. To achieve this objective, the supervised intelligent committee machine (SICM) method as a combinational black box model was used to predict NOx concentration at the pavement level before and after TiO2 application on the pavement surface. SICM predicts NOx 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 NOx 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 NOx 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.",
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Supervised intelligence committee machine to evaluate field performance of photocatalytic asphalt pavement for ambient air purification. / Nadiri, Ataallah; Hassan, Marwa M.; Asadi, Somayeh.

In: Transportation Research Record, Vol. 2528, 01.01.2015, p. 96-105.

Research output: Contribution to journalArticle

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