# Methodology for the Prediction of the Thermal Conductivity of Concrete by Using Neural Networks
## 5 Conclusions
[CrossRef]
* [PERSON] et al. (2014) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Generative Adversarial Nets. _Adv. Neural Inf. Process. Syst._**2014**, _27_, 2672-2680. [CrossRef]
* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] Modeling Tabular Data using Conditional GAN. _Adv. Neural Inf. Process. Syst._**2019**, _32_. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON] GAN-Based Approaches for Generating Structured Data in the Medical Domain. _Appl. Sci._**2022**, _12_, 7075. [CrossRef]
* [PERSON] (1998) [PERSON] Modeling of strength of high-performance concrete using artificial neural networks. _Cem. Concr. Res._**1998**, _28_, 1797-1808. [CrossRef]
* [PERSON] (2007) [PERSON] Modeling slump flow of concrete using second-order regressions and artificial neural networks. _Cem. Concr. Compos._**2007**, _29_, 474-480. [CrossRef]
* [PERSON] (2020) [PERSON]; [PERSON]; [PERSON] Estimation of the compressive strength of concretes containing ground granulated blast furnace slag using hybridized multi-objective ANN and salp swarm algorithm. _Constr. Build. Mater._**2020**, _248_, 118676. [CrossRef]
* [PERSON] (2020) [PERSON] Four-layer perceptron approach for strength prediction of UHPC. _Constr. Build. Mater._**2020**, _256_, 119465. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON] Properties prediction of environmentally friendly ultra-high-performance concrete using artificial neural networks. _Eur. J. Environ. Civ. Eng._**2022**, _26_, 2319-2343. [CrossRef]
* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON] An Artificial Neural Network Model to Predict the Thermal Properties of Concrete Using Different Neurons and Activation Functions. _Adv. Mater. Sci. Eng._**2019**, _2019_, 3831813. [CrossRef]
* [PERSON] et al. (2021) [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] Application of artificial neural networks to predict insulation properties of lightweight concrete. _Appl. Sci._**2021**, _11_, 10544. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Optimization of foam concrete characteristics using response surface methodology and artificial neural networks. _Constr. Build. Mater._**2022**, _337_, 127575. [CrossRef]
* [PERSON] et al. (2013) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Modeling of thermal conductivity of concrete with vermiculible by using artificial neural Networks approaches. _Exp. Heat Transf._**2013**, _26_, 360-383. [CrossRef]
* [PERSON] et al. (2012) [PERSON]; [PERSON]; [PERSON] Effective Prediction of Thermal Conductivity of Concrete Using Neural Network Method. _Int. J. Concr. Struct. Mater._**2012**, \(6\), 177-186. [CrossRef]
* [PERSON] and [PERSON] (2015) [PERSON]; [PERSON] [PERSON] Comparison between ANFIS and ANN for estimation of the thermal conductivity coefficients of construction materials. _Sci. Iran._**2015**, _22_, 2001-2011. * _[PERSON] et al. (2023)_ [PERSON]; [PERSON]; [PERSON] Investigation of thermal conductivity of rubberized concrete as an energy-efficient building material and modeling by artificial intelligence. _Arch. Civ. Mech. Eng._**2023**, _23_, 168. [CrossRef]
* _[PERSON] et al. (2022)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Application of Artificial Neural Networks in Construction Management: A Scientometric Review. _Buildings_**2022**, _12_, 952. [CrossRef]
* _[PERSON] et al. (2015)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] Study on thermal properties of recycled aggregate concrete and recycled concrete blocks. _Constr. Build. Mater._**2015**, _94_, 620-628. [CrossRef]
* _[PERSON] et al. (2003)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] An experimental study on thermal conductivity of concrete. _Cem. Concr. Res._**2003**, _33_, 363-371. [CrossRef]
* _[PERSON] et al.