Somayeh Asadi

Sustainable Photocatalytic Asphalt Pavements for Mitigation of Nitrogen Oxide and Sulfur Dioxide Vehicle Emissions

The ability of titanium dioxide (TiO2) photocatalytic nanoparticles to trap and decompose organic and inorganic air pollutants render them a promising technology as a pavement coating to mitigate the harmful effects of vehicle emissions. This technology may revolutionize construction and production practices of hot-mix asphalt by introducing a new class of mixtures with superior environmental performance. The objective of this study was to assess the benefits of incorporating TiO2 into asphalt pavements. To achieve this objective, the photocatalytic effectiveness and durability of a water-based spray coating of TiO2 was evaluated in the laboratory. This study also presents the field performance of the country’s first air-purifying photocatalytic asphalt pavement, located on the campus of Louisiana State University. Laboratory evaluation showed that TiO2 was effective in removing NOₓ and SO2 pollutants from the air stream, with an efficiency ranging from 31–55% for NOₓ pollutants and 4–20% for SO2 pollutants. The maximum NOₓ and SO2 removal efficiencies were achieved at an application rate of 0.05 L/m². The efficiency of NOₓ reduction is affected by the flow rate of the pollutant, relative humidity, and ultraviolet (UV) light intensity. In the field, NOₓ concentrations were monitored for both the coated and uncoated sections to directly measure photocatalytic degradation. Furthermore, nitrates were collected from the coated and uncoated areas for evidence of photocatalytic NOₓ reduction. Results from both approaches show evidence of photocatalytic NOₓ reduction. Further field evaluation is needed to determine the durability of the surface coating.

Evaluation of Self-Healing Mechanisms in Concrete with Double-Walled Sodium Silicate Microcapsules

AbstractThe objective of this study is to evaluate a new generation of self-healing materials that hold promise for better durability and performance. The in situ polymerization method was used to develop double-walled microcapsules. The microcapsules were prepared in a single batch process containing sodium silicate as the healing agent encapsulated in double-walled polyurethane/urea-formaldehyde (PU/UF) microcapsules. Double-walled microcapsules provide enhanced durability at high temperatures compared with single-walled microcapsules while preserving adequate interfacial bonding of microcapsules. A parametric study was carried out to investigate the effect of different parameters such as agitation rate, pH, and temperature on the performance of the microcapsules and to determine the optimum microencapsulation procedure. The prepared microcapsules were then incorporated into self-healing concrete beams. To monitor the healing process of the cracks, microcracks were created by imposing a certain magnitud...

Durability Quantification of TiO2 Surface Coating on Concrete and Asphalt Pavements

AbstractThe use of nanosized titanium dioxide in photocatalytic pavements is a promising approach to combat air pollution. Past research focused on the effects of environmental and operational parameters on photocatalytic efficiency and its performance under laboratory and field conditions. Few studies have attempted to quantify the durability of the technology integrated with in-service photocatalytic pavements. This study developed and tested a new photocatalytic quantification method used to quantify the short-term durability of a TiO2 spray application on two pavement surfaces: concrete and asphalt. This was accomplished through developing a nitrate extraction method that could be used on in-service pavements without requiring core extraction. Results of the proposed method were compared to results obtained from the Japanese Industrial Standards (JIS) method. The experimental program included testing photocatalytic samples in the laboratory for NOx reduction and nitrate accumulation based on the JIS m...

Laboratory Evaluation of Environmental Performance of Photocatalytic Titanium Dioxide Warm-Mix Asphalt Pavements

AbstractThe use of titanium dioxide (TiO2) coating for pavements has received considerable attention in recent years to improve air quality near large metropolitan areas. However, the proper method of applying TiO2 to asphalt pavements is still unclear. This study evaluated the benefits of incorporating TiO2 in the preparation of warm-mix asphalt (WMA). Two application methods to integrate TiO2 were evaluated, a water-based TiO2 solution applied as a thin coating and using TiO2 as a modifier to asphalt binder in the preparation of WMA. On the basis of the results of the experimental program, it was determined that the photocatalytic compound was not effective in degrading NOx in the air stream when used as a modifier to the binder in the preparation of WMA. This could be attributed to the fact that only a small amount of TiO2 is present at the surface. When used as part of a surface spray coating, TiO2 was effective in removing nitrogen oxide (NOx-) pollutants from the air stream with an efficiency rangin...

Performance evaluation of an attic radiant barrier system using three-dimensional transient finite element method

A three-dimensional transient finite element model was developed to evaluate the thermal performance of an attic radiant barrier system as an energy-efficient insulation technology. The finite element model simulates different heat transfer modes including convection, radiation, and conduction. Hourly experimental data collected in a housing complex in Zachary, LA, were used to validate the finite element analysis. Upon validation, the design variables and their influence on the performance of the radiant barrier insulation system were investigated. Among the different design variables, the presence of an air gap on both sides of the radiant barrier had a significant effect on the performance of the insulation system.

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

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 (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.

Artificial intelligence modeling to evaluate field performance of photocatalytic asphalt pavement for ambient air purification

In recent years, the application of titanium dioxide (TiO2) as a photocatalyst in asphalt pavement has received considerable attention for purifying ambient air from traffic-emitted pollutants via photocatalytic processes. In order to control the increasing deterioration of ambient air quality, urgent and proper risk assessment tools are deemed necessary. However, in practice, monitoring all process parameters for various operating conditions is difficult due to the complex and non-linear nature of air pollution-based problems. Therefore, the development of models to predict air pollutant concentrations is very useful because it can provide early warnings to the population and also reduce the number of measuring sites. This study used artificial neural network (ANN) and neuro-fuzzy (NF) models to predict NOx concentration in the air as a function of traffic count (Tr) and climatic conditions including humidity (H), temperature (T), solar radiation (S), and wind speed (W) before and after the application of TiO2 on the pavement surface. These models are useful for modeling because of their ability to be trained using historical data and because of their capability for modeling highly non-linear relationships. To build these models, data were collected from a field study where an aqueous nano TiO2 solution was sprayed on a 0.2-mile of asphalt pavement in Baton Rouge, LA. Results of this study showed that the NF model provided a better fitting to NOx measurements than the ANN model in the training, validation, and test steps. Results of a parametric study indicated that traffic level, relative humidity, and solar radiation had the most influence on photocatalytic efficiency.

Sustainable Building Design: A Review on Recent Metaheuristic Methods

A notable portion of the total primary energy is consumed by today’s buildings in developed countries. In recent years, decision makers and planners are facing increased pressure to respond more effectively to a number of energy-related issues and conflicts. Therefore, this article strives to make a technical review of all relevant research applying simulation-based optimization methods to sustainable building design problems. A summary of the application of common heuristic and meta-heuristic methods to different fields of sustainable building design is given.

Using Building Energy Simulation and Geospatial Analysis to Determine Building and Transportation Related Energy Use

According to the U.S. Energy Information Administration (EIA), building and transportation sectors account for approximately 75% of CO₂ emissions. Given the magnitude of this statistic, many studies have been directed towards the issue of energy use and carbon emissions of the built environment. Most of these studies however, have focused only on either buildings or transportation systems. To analyze the dynamics of energy use associated with buildings and transportation systems, it is essential to explore the interactions between these two sectors in a single comprehensive model. This paper develops a network infrastructure model to determine the transportation energy intensity of a building as well as building energy consumption based on the residents’ lifestyle. The proposed model is developed using Geographic Information Systems (GIS) and Building Information Modeling (BIM) to identify the current trends in energy use associated with people behavior and infrastructure (buildings and transportation networks). BIM is used as a life cycle inventory to model and collect building-related information and material quantities, and GIS is used to define geo-referenced locations, storing attribute data, and displaying data on maps. The main input to the model would be characteristics of buildings and transportation networks, and socioeconomic data (population dynamics) collected from a survey. The model then generates the energy and carbon implications of the network in the form of a map.

Measuring End-User Satisfaction in the Design of Building Projects Using Eye- Tracking Technology

The importance of end-user participation in the design process of building and construction projects has been recognized and addressed by a number of researchers and practitioners. The main goal is to ensure that the project outcome meets the facility users’ needs. In order to understand their needs, a variety of approaches (e.g. focus groups, workshops, and questionnaires) for the building end-users participation in the design process have been presented in the literature. Despite the contributions and practical features of these methods, they require a significant amount of time and effort to conduct and interpret the participants’ responses. To overcome this limitation, this paper investigates the use of eye-tracking technology to measure and analyze end-user satisfaction. This study is carried out to test the hypothesis that the users’ satisfaction of design variations is related to their visual attention. In other words, design alternatives with high level of users’ satisfaction attract more attention. An experiment using four alternatives for the design of a facade is performed to test the effectiveness of eye-tracking technology. The design alternatives are developed and displayed in a virtual 3D environment. Participants are asked to rate their level of satisfaction with each alternative, while their interaction with the virtual models is recorded using eye-tracking. The results of the experiment are also demonstrated to domain experts to get a better understanding of the technology’s potential and challenges.