# Methodology for the Prediction of the Thermal Conductivity of Concrete by Using Neural Networks
## 3 Methodology
### Case Study  
The proposed model is applied to conduct a broad case study on concrete mixtures. As previously highlighted in the introduction, precise thermal conductivity predictions for building materials can significantly enhance building thermal performance. Given that the thermal conductivity of concrete can be influenced by the type and quantity of each constituent, this study seeks to predict this critical property based on the mass composition of these constituents. A comprehensive literature review is conducted to create a database of 200 points compiled from various relevant studies published in the literature. Each selected paper presents a different type of concrete, such as recycled aggregate concrete, lightweight concrete, fiber-reinforced foamed concrete, moderate-strength concrete, and concrete with vermiculite. This variation allows the development of a generalist model. The dataset compiled in this context consists of concrete from 12 sources (Table 2) with modern constituent materials: ceramic, slag, silica fume, fly ash, steel fiber, propylene fiber, recycled aggregate, lightweight aggregate, waste aggregate, and foam. The model's inputs are defined according to the available data and the influence of each feature on the thermal conductivity. Here, a total of 18 input parameters that affect the output, thermal conductivity, are considered. The selected input parameters for this case study are density (D), water-to-cement ratio (W/C), water content (W), cement content (C), ceramic powder content (Ce), fine aggregate content (FAg), coarse aggregate content (CAg), natural aggregate content (NAg), recycled aggregate content (RAg), light aggregate content (LAg), waste aggregate content (WAg), fly ash content (FA), silica fume content (SF), slag content (S), fiber content (F), other admixture content (Ad), superplasticizer content (Sp), and foam volume content  
Figure 4: Graphical representation of a neuron. Figure 5: Flowchart of a Generative Adversarial Network (GAN). (FV). The values of TC lie in the range of 0.37-2.70 W/m.K with a standard deviation of 0.5 W/m.K. The dataset used in this study comprises input parameters collected from various independent research groups, each of which conducted studies with different objectives and, consequently, different sets of input variables. As a result, certain parameters that might appear to be missing in Table 2 are not absent; rather, they were simply not included in specific studies due to the varying focuses of each research effort. Therefore, the absence of certain data points in specific groups is by design and does not represent missing values in the conventional sense. Each group of data was used in a complete form relevant to its particular context, ensuring that the model was trained and validated with the available and intended inputs, without the negative impact of non-existent data. \begin{table}
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