Evaluating embodied energy, carbon impact, and predictive precision through machine learning for pavers manufactured with treated recycled construction and demolition waste aggregate

This investigation assesses the embodied energy and carbon footprint in the manufacture of pavers using varying proportions of recycled Construction and Demolition Waste (CDW). Additionally, Thin Film Composite Polyamide fiber (TFC PA), extracted from end-of-life Reverse Osmosis (RO) membranes, is introduced as an additive to enhance the concrete's strength. Machine learning techniques, namely Artificial Neural Network (ANN), Support Vector Regression (SVR), and Response Surface Methodology (RSM), are employed to predict the mechanical properties of pavers. The study focuses on examining the energy required and embodied carbon in various mix proportions, as well as the mechanical properties-specifically compressive strength and split tensile strength of concrete with different CDW and TFC PA proportions. Findings reveal that the optimal percentage of TFC PA is 3 % for all CDW replacement proportions, resulting in low carbon content both in terms of energy and embodiment and in mechanical behavior. The implementation of ANN and SVR is conducted in MATLAB, while a Design Expert is employed to generate the experimental design for RSM. The RSM regression model demonstrates a robust correlation between variables and observed outcomes, with optimal p-values, R2 values, and f-values. The ANN model successfully captures the variability in the data. Additionally, the findings indicate a consistent superiority of the Support Vector Regression (SVR) model over both Artificial Neural Network (ANN) and Response Surface Model (RSM) models when considering diverse performance metrics such as residuals and correlation coefficients.

A sustainable novel approach of recycling end-of-life reverse osmosis (RO) membrane as additives in concrete and mathematical model with response surface methodology (RSM)

This study investigates a novel method of recycling end-of-life reverse osmosis (RO) membranes to enhance the mechanical properties of concrete. Reverse osmosis (RO) is a widely used technology for water purification and desalination, but it has various environmental problems that require attention. RO membranes have a short service life, resulting in the generation of end-of-life (E-o-L) RO membrane trash in thousands of tons each year, which is typically disposed of in landfills, polluting land resources. The RO membrane primarily comprises a thin-film composite of polyamide (TFC PA) as a filter medium. The material properties of TFC PA are studied and added to the concrete as an additive in the cement volume at 1 to 20%. The optimum mix is identified at 3.4% of TFC PA, and the mechanical properties are nearly 17% greater than the control mix. SEM/EDX shows a higher bonding between TFC PA and the concrete, and TGA results exhibit the mass loss increasing with the increase in TFC PA. MIP analysis shows that the volume of cumulative pores and intruded volume is increased with increasing TFC PA. A sustainability study was carried out to evaluate the energy consumed and embodied CO2 in the mixes and compared them with the control mix. The mathematical model for compressive strength at 7 days, 14 days, and 28 days is done using response surface methodology (RSM) with the independent variables of TFC PA, slump, and superplasticizer. The adopted regression illustrates a significant relationship between the response and variables, proving a higher R2 for all the models and optimal p-values for the responses. They show that TFC PA influences the enhancement of properties of concrete. The residual also proves to be very minimal in the model.

Assessing the embodied carbon and energy required for manufacturing sustainable concrete blocks using plastic pollution as a fiber

This study explores the utilization of polyethylene bags and PET bottles as a fiber in the production of lightweight non-autoclaved plastic fiber-reinforced aerated concrete (NAPFRAC), which has the potential to replace conventional bricks. The study begins by examining global plastic pollutant production and their characterization and the need for a forecast of plastic pollution worldwide. Optimization using Design-Expert 9.0 is used to estimate the optimum mix of NAPFRAC. The mechanical properties of the optimum mix are determined, and a scaled-down model of wall panels is cast to study their behavior and vertical and horizontal ultimate load-carrying capacity. The results are compared to those of conventional first-class burnt clay bricks, and it is found that NAPFRAC wall panels show a 28% increase in vertical load-carrying capacity and 40% in horizontal load-carrying capacity. An analytical study of a high-rise building with NAPFRAC as infill panels is carried out to check the reduction of steel reinforcement in structural sections. Microstructural analysis using SEM (scanning electron microscopy) and XRD (X-ray diffraction) is conducted to identify the morphology and mineralogical composition of the NAPFRAC. Energy studies are also carried out on the mix ratio to identify the embodied carbon dioxide and energy required. Overall, this study highlights the potential of NAPFRAC as a lightweight alternative to conventional bricks. The use of plastic waste as a fiber in concrete production could have a positive impact on the environment by reducing plastic pollution. The results of this study could also have practical applications in the construction industry, especially in the design of high-rise buildings.

A classic critique on concrete adsorbing pollutants emitted by automobiles and statistical envision using trend analysis

Globally, road transportation is responsible for about 1/3 of total air pollution, among which Co₂ , No_X and So_X are major by volume which are directly responsible for ozone layer depletion. As these activities continue to rise, the nature faces the threat of an unprecedented environmental catastrophe. In this survey, the adsorbent and the methods used to adsorb the vehicle emission pollutant directly from the ambient air and enhance the air quality are reviewed. There are extensive number of adsorbents available namely titanium oxide, polyethyleneimine (PEI), activated carbon, and other natural admixtures and methods that adsorb these pollutants such as Co₂; specifically, polyethyleneimine (PEI) is a polymer-based product which has a behavior of adsorbing Co₂ directly from ambient air. A carbon-neutral technology for eliminating anthropogenic CO₂ emissions has been proposed, trapping CO₂ from the atmosphere. The major concern including Co₂ adsorption rate and methods to evaluate the volume of adsorption with time has been reviewed. Trend analysis depends on the reason that can foresee what will occur later on by seeing what has happened beforehand. The trend analysis method was used in this study to model the adsorption rate of PEI based on their pore diameter. When it comes to designing regression analysis, R² values of 0.2 to 0.3 indicate that statistical tests have no meaningful impact and further research is needed. For adsorption of No_X, a lot of adsorbents are used namely sodium bentonite, zeolite, activated carbon, and other natural minerals. Among them, activated carbon enhances the adsorption rate of No_x. Vast research has been conducted to analyze the adsorption rate, advantages, disadvantages, and the behavior of different adsorbent with the concrete. Literally for SO_x, the adsorbents that are widely used are coal ash, copper oxide, and silicate. The characterization of different adsorbents with their adsorbate can be analyzed by some test methods as follows: Fourier-transform infrared spectroscopy (FTIR), BET analysis, scanning electron microscopy (SEM), field emission scanning electron microscopy (FESEM) analysis, surface area, X-ray diffraction (XRD), porosity analysis, and more according to the requirement. The quantity of adsorbent and adsorbate could be evaluated for facing the real-time constraints through a detailed research by review.

Multiple linear and non-linear regression analyses of various soil and terrain indices with regard to their efficiency in the determination of temporal changes in LST values within Trichy district of Tamil Nadu, India

Land surface temperature (LST) of the terrain is the result of thermal energy received from the sun as the source. The study site was Trichy district of Tamil Nadu state in India wherein the LST values pertaining to all the belonging pixels have been calculated using generalized split window (GSW) algorithm from the Landsat-8 satellite images on two different dates with a gap of 5 years between them. Along with the LST calculation on these 2 dates, corresponding NDWI, NDVI, NDBI, NDMI, BSI, DBI, DBSI, LSE, and albedo values have also been calculated. Then, changes in the dependent variable (LST) and in the each of the respective explanatory or independent variables have been undergone through the process of multiple linear and non-linear regression analysis to find out the best-fitting model set of explanatory variables that best describes the variation in LST values at a location. In this research, it has been found that a non-linear model set comprising five independent variables like change in BSI, LSE, NDWI, NDBI, and albedo seem to be the best-fitting model for predicting the variations in the dependent variable to the maximum degree and also with acceptable redundancy. Also, it has been observed that changes in LSE or albedo values at any place within the study-site could play the most significant role in having an influence in the LST value change that could be recorded in the same over any given time period, thus proving the vastness of the effect that could be made by an increase in urbanization or structural development on the extent of positive change in LST.

Measurement of urinary calcium using AT89C51RD2 microcontroller

A simple and inexpensive absorption technique for determination of calcium ion in urine samples is developed, comprising a light emitting diode (650 nm) as the light source and photodiode as the detector with AT89C51RD2 microcontroller. The design of the system and details of interface, calibration, and procedure of operation are explained in this paper. Software is developed to monitor sample processing and to display the results in liquid crystal display screen. With 15 microl sample volume, a linear output is obtained in the range of 2.5-7.5 mM calcium with a detection limit of 0.06 mM. Interferences from other cations such as monovalent ion and divalent ion are investigated in the expected range, which are normally present in clinical samples, and absorption changes over the pH range of 3-12 are also determined. This system has been demonstrated successfully for the successive assay of calcium in urine samples, with the results comparing well to those achieved and in good agreement with values obtained with the current clinical spectrophotometric method at 95% of confidence level.