# Methodology for the Prediction of the Thermal Conductivity of Concrete by Using Neural Networks
## 3 Methodology
### Case Study
** [38] & **[39]** & **[40]** \\ \hline N\({}^{\circ}\) of data & 15 & 14 & 82 & 5 & 9 & 21 \\ D & [kg/m\({}^{3}\)] & 1617–2195 & 1540–2297 & 1144–3075 & 2017–2199 & 2290–2355 & 1626–2380 \\ W/C & - & 0.40–0.50 & 0.25–0.57 & 0.22–0.62 & 0.32–0.80 & 0.35 & 0.35–0.55 \\ W & [kg/m\({}^{3}\)] & 130–150 & 140–558 & 133–524 & 124 & 167.5 & 120–190 \\ C & [kg/m\({}^{3}\)] & 260–375 & 0–1792 & 242–1762 & 154–385 & 245–324 & 90–110 \\ Ce & [kg/m\({}^{3}\)] & - & - & - & 0–231 & - & - \\ FAg & [kg/m\({}^{3}\)] & 0–658 & 0–702 & 0–1167 & 775 & 740 & 280–365 \\ CAG & [kg/m\({}^{3}\)] & 0–605 & 0–1103 & 0–1850 & 175 & 458 & - \\ NAg & [kg/m\({}^{3}\)] & 0–987 & - & - & 770 & 577 & 0–440 \\ RAg & [kg/m\({}^{3}\)] & 0–1710 & - & - & - & - & 0–460 \\ LAG & [kg/m\({}^{3}\)] & - & - & - & - & - & 0–320 \\ WAg & [kg/m\({}^{3}\)] & 0–1645 & - & - & - & - & - \\ FA & [kg/m\({}^{3}\)] & - & 0–973 & 0–104 & 392 & 26.25–105 & 0–30 \\ SF & [kg/m\({}^{3}\)] & - & - & 0–89 & - & 26.25–52.5 & - \\ S & [kg/m\({}^{3}\)] & - & 0–1282 & - & - & 26.25–105 & - \\ F & [kg/m\({}^{3}\)] & - & - & - & - & - & 0–20 \\ Ad & [kg/m\({}^{3}\)] & - & 0–8.81 & - & - & - & - \\ Sp & [kg/m\({}^{3}\)] & - & - & - & 3.4–4 & 1.8 & - \\ FV & [kg/m\({}^{3}\)] & - & - & - & - & - & - \\ TC & [W/m.K] & 0.74–1.40 & 0.66–1.69 & 0.30–2.50 & 1.21–1.55 & 0.95–1.17 & 0.70–1.20 \\ \hline  & & Sargam et al. & Sargam et al. & Cavalline & Kurpinska & Gencel et al. & Bayraktar \\  & & [41] & [42] & et al. [43] & et al. [44] & [31] & et al. [45] \\ \hline N\({}^{\circ}\) of data & 4 & 1 & 17 & 4 & 4 & 20 \\ D & [kg/m\({}^{3}\)] & 2281–2367 & 2389 & 1434–2219 & 1234–2244 & 1194–1370 & 1418–1988 \\ W/C & - & 0.45 & 0.43 & 0.29–0.54 & 0.40 & 0.63–1.37 & 0.75–1.88 \\ W & [kg/m\({}^{3}\)] & 157 & 151 & 155–218 & 220–310 & 471–580 & 338 \\ C & [kg/m\({}^{3}\)] & 349 & 281 & 268–454 & 500–700 & 422–750 & 180–450 \\ Ce & [kg/m\({}^{3}\)] & - & - & - & - & - & - \\ FAg & [kg/m\({}^{3}\)] & 772–821 & 890 & 0–1559 & 0–1300 & - & 0–1091 \\ CAG & [kg/m\({}^{3}\)] & - & - & - & - & 149–192 & - \\ NAg & [kg/m\({}^{3}\)] & 0–1171 & 900 & 0–1068 & - & - & - \\ RAg & [kg/m\({}^{3}\)] & 0–1036 & - & - & - & - & - \\ LAG & [kg/m\({}^{3}\)] & - & - & 0–1101 & - & - & - \\ WAg & [kg/m\({}^{3}\)] & - & - & - & 0–490 & - & 0–1091 \\ FA & [kg/m\({}^{3}\)] & - & 71 & 0–102 & - & - & - \\ SF & [kg/m\({}^{3}\)] & - & - & - & - & - & - \\ S & [kg/m\({}^{3}\)] & - & - & - & - & - & 0–270 \\ F & [kg/m\({}^{3}\)] & - & - & - & - & - & 0–9 \\ Ad & [kg/m\({}^{3}\)] & - & - & - & - & - & - \\ Sp & [kg/m\({}^{3}\)] & - & - & - & 3–4.9 & - & - \\ FV & [kg/m\({}^{3}\)] & - & - & - & - & - & 50–100 \\ TC & [W/m.K] & 0.89–1.14 & 2.70 & 0.69–1.78 & 0.37–1.19 & 0.33–0.52 & 0.50–0.75 \\ \hline  & & [PERSON] et al. & [PERSON] et al. & Cavalline & Kurpinska & [PERSON] et al. & Bayraktar \\  & & [41] & [42] & et al. [43] & et al. [44] & [31] & et al. [45] \\ \hline N\({}^{\circ}\) of data & 4 & 1 & 17 & 4 & 4 & 20 \\ D & [kg/m\({}^{3}\)] & 2281–2367 & 2389 & 1434–2219 & 1234–2244 & 1194–1370 & 1418–1988 \\ W/C & - & 0.45 & 0.43 & 0.29–0.54 & 0.40 & 0.63–1.37 & 0.75–1.88 \\ W & [kg/m\({}^{3}\)] & 157 & 151 & 155–218 & 220–310 & 471–580 & 338 \\ C & [kg/m\({}^{3}\)] & 349 & 281 & 268–454 & 500–700 & 422–750 & 180–450 \\ Ce & [kg/m\({}^{3}\)] & - & - & - & - & - & - \\ FAg & [kg/m\({}^{3}\)] & 772–821 & 890 & 0–1559 & 0–1300 & - & 0–1091 \\ CAG & [kg/m\({}^{3}\)] & - & - & - & - & 149–192 & - \\ NAg & [kg/m\({}^{3}\)] & 0–1171 & 900 & 0–1068 & - & - & - \\ RAg & [kg/m\({}^{3}\)] & 0–1036 & - & - & - & - & - \\ LAg & [kg/m\({}^{3}\)] & - & - & 0–1101 & - & - & - \\ WAg & [kg/m\({}^{3}\)] & - &In order to train the model, the compiled dataset is randomly divided into training (80%) and validation sub-dataset (20%). Then, the reliability and reproducibility of the ANN model are evaluated on a new testing dataset. The predictive performance of the final model is thoroughly assessed using the metrics mentioned above.