However, the thermal conductivity of the NAAC mixture combined with soda-lime glass sand and glass fiber was lower than that of the NAAC mixture containing normal sand. Furthermore, the combined use of glass sand glass fiber also increases the strength up to 2 times.
- Problem statement
- Research Objectives and Scopes
- Thesis structure
Development of proportions of NAAC mixture composed of combined waste glass sand and glass fibers for improvement of thermal performance. Evaluation of the combined effect of waste soda lime glass sand and glass fibers on improving the thermal properties of NAAC.
- Aerated Concrete and Non–Autoclaved Aerated Concrete
- Waste Soda-Lime Glass
- DesignBuilder Software
- Multivariate Linear Regression Approach
They summarized that the minor differences may be due to the error in input data such as unspecified parameters. As previously mentioned, one of the objectives of the thesis is to develop a statistical model to predict the thermal conductivity of NAAC.
- Aggregate properties
- Cementitious materials
- Mix Design
- Sample Preparation
- Testing Methods
- Thermal Conductivity Test
- Compressive Strength Test
- Flexural Strength Test
- Ultrasonic Pulse Velocity Test
- Porosity, Density, and Absorption
In addition, ultrasonic pulse velocity (UPV) samples were cast in a cubic mold and 50 mm on each side. After curing in a water bath for the appropriate days, the samples were boiled in a water bath at 100 °C for 1 day and then dried in an oven at 85 °C for 1 day. After that, all the samples were tested and the measurements were evaluated based on the appropriate test methods and procedures, which are clearly described in the next chapter.
As previously mentioned, 50 mm cubic specimens were developed for this test and a hydraulic laboratory machine was used to determine their strength (Figure 3.6). The following procedures were performed to test the compressive strength of NAAC: First, the sample was placed in the center of the machine between the test surfaces. Another type is continuously cured samples, where samples were tested for pulse rate and re-cured in water for the next corresponding days, and then retested for pulse rate.
As mentioned earlier, after 48 hours the samples were removed from the mold and their mass was measured (𝑀𝑀0). Then the samples were cured in water baths at room temperature for 7 days, 14 days and 28 days, and after corresponding days their mass was measured (𝑀𝑀1). After the samples were boiled in a 100°C water bath for 1 day (𝑀𝑀2) and were immersed in water (𝑀𝑀3).
Test Results and Discussion
Physical and Mechanical Properties of NAAC
- Relationship between dry density and porosity
- Relationship between dry density and water absorption
- Relationship between porosity and water absorption
- Compressive strength development
- Flexural strength development
- Relationship between ultrasonic pulse velocity and compressive strength
The following Figure 4.4 – Figure 4.7 demonstrate the effect of mix parameters on the compressive strength of NAAK. As can be observed from Figure 4.4, replacing normal sand with glass sand increases the compressive strength of NAAK. Moreover, the compressive strength of mix 3 containing 30% glass sand is three times higher than the batch mix (M1).
The compressive strength of NAAC containing glass sand The following figure 4.5 indicates the compressive strength of NAAC containing 30%. Furthermore, the maximum compressive strength was observed in 28 days cured specimens containing 3% glass fiber. For example, the compressive strength of 7-day cured specimens increases with increasing glass fiber, whereas the 14-day compressive strength decreases.
The following Figure 4.9 describes the effect of fly ash content on the flexural strength of NAAC. According to the results, the 15% replacement with fly ash adversely affects the flexural strength of NAAC. The following figure 4.10 interprets the effect of glass fiber on the improvement of the flexural strength of NAAC.
Relation between mixture parameters of NAAC and thermal conductivity
- Effect of glass content on thermal conductivity
- Effect of fly ash content on thermal conductivity
- Effect of fiber content on thermal conductivity
As already mentioned, series 2 contains three different mixes that include partial replacement of cement with fly ash (0%, 15% and 30%). As can be seen in Figure 4.14, the replacement of cement with fly ash results in an increase in the thermal conductivity of NAAC. For example, in 14-day-cured samples, the addition of 15% fly ash (M4) increases the thermal conductivity by 27%, and a 30% replacement of fly ash (M5) leads to an increase in thermal conductivity of up to 41% over the samples. which contains 0% fly ash (M3).
Only in the 7-day cured samples, 15% and 30% replacement of cement with fly ash has an insignificant effect on the thermal properties of NAAC, containing only 5% and 1.15% increase in thermal conductivity, respectively. This chapter explains the combined effect of glass sand and glass fibers on the thermal properties of NAAC. As expected, mainly the increase in glass fiber content results in an increase in the thermal conductivity of NAAC, except for the 28-day cured samples.
As was observed, there is no clear evolution in a thermal conductivity of 28-day cured samples. The last three mixes (M9, M10 and M11) were developed by combining 30% glass sand, 30% fly ash, 1%, 2% and 3% glass fiber, respectively, and the thermal conductivity results of these mixes are shown in Figure 4.16 . The combination of glass fiber with fly ash negatively affects the thermal properties of NAAC because it increases the thermal conductivity of NAAC.
Relation between physical properties of NAAC and thermal conductivity
- Effect of curing age on thermal conductivity
- Effect of dry density on thermal conductivity
- Effect of porosity on thermal conductivity
- Effect of water absorption capacity on thermal conductivity
- Effect of specimen thickness on thermal conductivity
The trend lines of 7 - day and 14 - day cured samples are the same as can be seen in Figure 4.18 and correlation coefficients between density and thermal conductivity R2 are 74.56% and 58.47%, accordingly. This result of 28-day cured samples is shown in Figure 4.18-d, where the increase in density leads to the increase in thermal conductivity. Relationship between thermal conductivity and density (a) 7-day cure (b) 14-day cure (c) 28-day cure (d) 28-day cure except M1.
Given that the maximum dependence between density and thermal conductivity observed in the 14-day cured samples, these samples would be highlighted as the most successful. All sample results, both overall and by Series, show a good correlation between porosity and thermal conductivity. As an example, the plot of porosity versus thermal conductivity of 7-day cured samples by total and batch is shown in Figure 4.20.
However, the individual analysis of Series shows that in 14-day cured samples the correlation between porosity and thermal conductivity is better (Table 4.6). Based on the results, it was observed that as absorption increases, thermal conductivity decreases. The following table 4.7 shows the correlation coefficients between the absorption and thermal conductivity of NAAC.
Thermal Conductivity Prediction Model, Energy Saving Simulation, and
Energy Saving Simulation: Tool – Design Builder
- Geometric aspects of residential building
- Result and Discussion
As can be seen from the graphs, the mixture 2, which contains 15% replacement with waste glass sand, has the lowest heating and cooling loads per month, due to the lowest thermal conductivity value. Since January and July were the coldest and warmest months in a year with the lowest and highest outdoor temperatures (Table 5.5), the maximum heating and cooling loads were observed in these months. The mixture 2 records 76391.86 kWh and 10412 kWh of heating and cooling loads in January and July, respectively.
Furthermore, the largest heating and cooling loads per month were observed in mixture 5, which has the highest thermal conductivity value. Therefore, the mixture containing only waste glass sand has the lowest heating and cooling load per month, due to the lowest thermal conductivity value. The replacement with 1%, 2% and 3% glass fiber increased the thermal conductivity value, therefore they have more heating and cooling loads than the mixture containing only waste glass sand.
However, the heating and cooling load results of combined mixtures (M6, M7, M8) which consisted of both waste glass sand and fiberglass are lower than batch mixture 1. As can be seen from Table 5.8, three different residential wall layers. compared in terms of energy saving. Moreover, the use of NAAC Mixture 2 in construction of wall layers is also beneficial in terms of site and energy source.
Development of thermal conductivity prediction model based on multi-variable linear
Based on the comparison, the NAAC mixture containing only glass sand was found to be more beneficial than the design wall in terms of energy conservation. Therefore, one of these variables must be dropped and cannot be included in the regression analysis. In addition, based on the part of the analysis where the relationship between absorbency and porosity was evaluated, it was found that absorbency and porosity have the highest ratio and equal importance.
Glass sand and normal sand also have a high correlation, but due to the importance of these variables in predicting thermal conductivity, it was decided to leave these variables for further analysis. However, according to further analysis, as a p-value, it will be decided which will be included in the thermal conductivity modelling. Since they already appeared in Chapter 4, it was decided to skip them in this chapter.
Then the models can be compared and the best model to predict the thermal conductivity can be selected. As shown in Table 5.11, Best Subset Analysis provides the 7 different models, more specifically it shows how many and exactly what type of variables should be compared to predict the most accurate comparison and they will be selected based on the criteria that are described below in Table 5.12. The same analyzes were performed with 7-day and 28-day cured specimens, and their results are shown below in Table 5.13.
Feasibility of NAAC
Therefore, model #5, which contains the largest number of variables, will be chosen as the best-fitting model. Therefore, it can be summarized that Equation 5.1 can be used as a prediction model for the thermal conductivity of NAAC samples. Furthermore, based on the energy analysis, this mixture is summarized as more energy efficient than the other combined mixtures.
Therefore, the next step is to estimate the cost of glass sand and NAAC samples production. The jaw crusher and ball mill machinery will be used to grind the glass and prepare the glass sand, while the mixer will be used to prepare the NAAC samples. The cost analysis also includes the electricity costs of all machines, as shown in Table 5.16.
Based on the feasibility analysis, the total cost of 1 m3 of NAAC, consisting of combined glass sand and fiberglass, is $67. These costs are slightly higher than conventional materials in Kazakhstan, due to the use of fiberglass. However, the money can be saved every year on the energy bills because the developed NAAC will provide better mechanical and thermal properties than those conventional materials.
34;Improving energy efficiency in buildings while simultaneously reducing the amount of waste by using autoclaved aerated concrete from power industry waste." Energy and buildings. 34;Using waste glass as an aggregate in concrete." At the 7th Annual General Meeting of UK CARE. 34; Effect of different fiber reinforcements on thermal and mechanical properties of autoclaved aerated concrete." Building and construction materials.
34;Analysis of annual energy demand for heating and cooling for office buildings in different climates in Turkey." Energy and Buildings 40, no. 34;Current validation of energy simulation and investigation of energy management strategies (Case Study: An office building in Semnan, Iran).” Case studies in thermal engineering. 34; Load characteristics and operating strategies of buildings integrated with multi-storey double skin facade." Energy and buildings.
34;Combined Use of Design of Experiment and Dynamic Building Simulation in Energy Performance Assessment in Tropical Residential Buildings." Energy and Buildings. 34;Using Factor Analysis Results in a Multiple Linear Regression Model to Predict Kernel Weight in Ankara Walnuts." J. 34; A Direct Demand Forecasting Model for Small Urban Communities Using Multiple Linear Regression.” Transportation Research Record.