Introduction The advent of COVID-19 has impinged millions of people. The increased concern of the virus spread in confined spaces due to meteorological factors has sequentially fostered the need to improve indoor air quality. Objective This paper aims to review control measures and preventive sustainable solutions for the future that can deliberately help in bringing down the impact of declined air quality and prevent future biological attacks from affecting the occupant’s health. Methodology Anontology chart is constructed based on the set objectives and review of all the possible measures to improve the indoor air quality taking into account the affecting parameters has been done. Observations An integrated approach considering non-pharmaceutical and engineering control measures together for a healthy indoor environment should be contemplated rather than discretizing the available solutions. Maintaining social distance by reducing occupant density and implementing a modified ventilation system with advance filters for decontamination of viral load can help in sustaining healthy indoor air quality. Conclusion The review paper in the main, provides a brief overview of all the improvement techniques bearing in mind thermal comfort and safety of occupants and looks for a common ground for all the technologies based on literature survey and offers recommendation for a sustainable future.
This review presents the existing state-of-the-art practices of indoor environmental quality (IEQ) in naturally ventilated school buildings and is mainly focused on the components of IEQ like thermal comfort, indoor air quality with ventilation, and visual and acoustic comfort. This article also discusses the impacts of COVID-19 on naturally ventilated school buildings, highlighting the obviousness of dynamic applications that concentrate on reducing COVID-19 effects in naturally ventilated school buildings. The importance of the concerned issues and factors are discussed in detail for future research direction. This review is a step toward the development of the IEQ standard for naturally ventilated school buildings.
Concrete is the most commonly used construction material. The physical properties of concrete vary with the type of concrete, such as high and ultra-high-strength concrete, fibre-reinforced concrete, polymer-modified concrete, and lightweight concrete. The precise prediction of the properties of concrete is a problem due to the design code, which typically requires specific characteristics. The emergence of a new category of technology has motivated researchers to develop mechanical strength prediction models using Artificial Intelligence (AI). Empirical and statistical models have been extensively used. These models require a huge amount of laboratory data and still provide inaccurate results. Sometimes, these models cannot predict the properties of concrete due to complexity in the concrete mix design and curing conditions. To conquer such issues, AI models have been introduced as another approach for predicting the compressive strength and other properties of concrete. This article discusses machine learning algorithms, such as Gaussian Progress Regression (GPR), Support Vector Machine Regression (SVMR), Ensemble Learning (EL), and optimized GPR, SVMR, and EL, to predict the compressive strength of Lightweight Concrete (LWC). The simulation approaches of these trained models indicate that AI can provide accurate prediction models without undertaking extensive laboratory trials. Each model’s applicability and performance were rigorously reviewed and assessed. The findings revealed that the optimized GPR model (R = 0.9803) used in this study had the greatest accuracy. In addition, the optimized SVMR and GPR model showed good performance, with R-values 0.9777 and 0.9740, respectively. The proposed model is economic and efficient, and can be adopted by researchers and engineers to predict the compressive strength of LWC.
In this paper, curve-fitting and an artificial neural network (ANN) model were developed to predict R-Event. Expected number of new infections that arise in any event occurring over a total time in any space is termed as R-Event. Real-time data for the office environment was gathered in the spring of 2022 in a naturally ventilated office room in Roorkee, India, under composite climatic conditions. To ascertain the merit of the proposed ANN and curve-fitting models, the performances of the ANN approach were compared against the curve fitting model regarding conventional statistical indicators, i.e., correlation coefficient, root mean square error, mean absolute error, Nash-Sutcliffe efficiency index, mean absolute percentage error, and a20-index. Eleven input parameters namely indoor temperature (T In ), indoor relative humidity (RH In ), area of opening (A O ), number of occupants (O), area per person (A P ), volume per person (V P ), CO 2 concentration (CO 2 ), air quality index (AQI), outer wind speed (W S ), outdoor temperature (T Out ), outdoor humidity (RH Out ) were used in this study to predict the R-Event value as an output. The primary goal of this research is to establish the link between CO 2 concentration and R-Event value; eventually providing a model for prediction purposes. In this case study, the correlation coefficient of the ANN model and curve-fitting model were 0.9992 and 0.9557, respectively. It shows the ANN model's higher accuracy than the curvefitting model in R-Event prediction. Results indicate the proposed ANN prediction performance (R=0.9992, RMSE=0.0018708, MAE=0.0006675, MAPE=0.8643816, NS=0.9984365, and a20-index=0.9984300) is reliable and highly accurate to predict the R-event for offices.
Buildings are accountable for waste generation, utilization of natural resources, and ecological contamination. The construction sector is one of the biggest consumers of resources available naturally and is responsible for significant CO2 emissions on the planet. The effects of the buildings on the environment are commonly determined using Life Cycle Assessments (LCA). The investigation and comparison of the Life Cycle Ecological Footprint (LCEF) and Life Cycle Energy (LCE) of five residential buildings situated in the composite climatic zone of India is presented in this study. The utilization of resources (building materials) along with developing a mobile application and a generic model to choose low emission material is the uniqueness of this study. The utilization of eco-friendly building materials and how these are more efficient than conventional building materials are also discussed. In this investigation, the two approaches, (a) Life Cycle Energy Assessment (LCEA) and (b) Life Cycle Ecological Footprint (LCEF), are discussed to evaluate the impacts of building materials on the environment. The energy embedded due to the materials used in a building is calculated to demonstrate the prevalence of innovative construction techniques over traditional materials. The generic model developed to assess the LCEA of residential buildings in the composite climate of India and the other results show that the utilization of low-energy building materials brings about a significant decrease in the LCEF and the LCE of the buildings. The results are suitable for a similar typology of buildings elsewhere in different climatic zone as well. The MATLAB model presented will help researchers globally to follow-up or replicate the study in their country. The developed user-friendly mobile application will enhance the awareness related to energy, environment, ecology, and sustainable development in the general public. This study can help in understanding and thus reducing the ecological burden of building materials, eventually leading towards sustainable development.
The progress of Indoor Environmental Quality (IEQ) research in school buildings has increased profusely in the last two decades and the interest in this area is still growing worldwide. IEQ in classrooms impacts the comfort, health, and productivity of students as well as teachers. This article systematically discusses IEQ parameters related to studies conducted in Indian school classrooms during the last fifteen years. Real-time research studies conducted on Indoor Air Quality (IAQ), Thermal Comfort (TC), Acoustic Comfort (AcC), and Visual Comfort (VC) in Indian school classrooms from July 2006 to March 2021 are considered to gain insight into the existing research methodologies. This review article indicates that IEQ parameter studies in Indian school buildings are tortuous, strewn, inadequate, and unorganized. There is no literature review available on studies conducted on IEQ parameters in Indian school classrooms. The results infer that in India, there is no well-established method to assess the indoor environmental condition of classrooms in school buildings to date. Indian school classrooms are bleak and in dire need of energy-efficient modifications that maintain good IEQ for better teaching and learning outcomes. The prevailing COVID-19 Pandemic, Artificial Intelligence (AI), National Education Policy (NEP), Sick Building Syndrome (SBS), Internet of Things (IoT), and Green Schools (GS) are also discussed to effectively link existing conditions with the future of IEQ research in Indian school classrooms.
Air pollution is increasing profusely in Indian cities as well as throughout the world, and it poses a major threat to climate as well as the health of all living things. Air pollution is the reason behind degraded indoor air quality (IAQ) in urban buildings. Carbon dioxide (CO2) is the main contributor to indoor pollution as humans themselves are one of the generating sources of this pollutant. The testing and monitoring of CO2 consume cost and time and require smart sensors. Thus, to solve these limitations, machine learning (ML) has been used to predict the concentration of CO2 inside an office room. This study is based on the data collected through real-time measurements of indoor CO2, number of occupants, area per person, outdoor temperature, outer wind speed, relative humidity, and air quality index used as input parameters. In this study, ten algorithms, namely, artificial neural network (ANN), support vector machine (SVM), decision tree (DT), Gaussian process regression (GPR), linear regression (LR), ensemble learning (EL), optimized GPR, optimized EL, optimized DT, and optimized SVM, were used to predict the concentration of CO2. It has been found that the optimized GPR model performs better than other selected models in terms of prediction accuracy. The result of this study indicated that the optimized GPR model can predict the concentration of CO2 with the highest prediction accuracy having R , RMSE, MAE, NS, and a20-index values of 0.98874, 4.20068 ppm, 3.35098 ppm, 0.9817, and 1, respectively. This study can be utilized by the designers, researchers, healthcare professionals, and smart city developers to analyse the indoor air quality for designing air ventilation systems and monitoring CO2 level inside the buildings.
The need for hot water in residential buildings requires a significant energy potential. Therefore, an efficient water heating system is important to achieve the goal of saving high-grade energy. The most simple and cheapest solar water heater is a flat plate solar collector (FPSC), which can increase the thermal energy of fluid by absorbing solar radiation. The performance of FPSC is comparatively low due to the dilute nature of solar insolation. Therefore, advancement of FPSC is being undertaken to improve the performance and achieve size reduction. In past, several techniques have been exploited to improve the performance of FPSC, which are presented in the present paper. These techniques include surface modifications, use of nanofluids, solar selective coating, and applications of a mini/macro channel, heat pipe, and vacuum around absorber. Surface modification on the absorber/absorber tube techniques are exploited to transfer the maximum possible solar energy to working fluids by increasing the heat transfer rate. Insertion of wire mesh, coil, and twisted tapes in the flow has great potential to increase the Nusselt number by 460% at the expense of a large pressure drop. Selective coating of Cu0.44 Ti0.44 Mn0.84 helps to absorb up to 97.4% of the incident solar energy, which is more significant. Many nanofluids have been exploited as heat transfer fluids, as they not only increase the performance but also reduce the fluid inventory. So, these techniques play a very prominent role in the performance of FPSC, which are discussed in detail. Summaries of the results are presented and recommendations proposed.
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