# Methodology for the Prediction of the Thermal Conductivity of Concrete by Using Neural Networks
## 5 Conclusions
; supervision: [PERSON] and A.H. All authors have read and agreed to the published version of the manuscript. The authors would like to acknowledge financial support from the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES), Finance Code 001, Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq), Fundacao de Amparo a Pesquisa do Estado do Rio de Janeiro (Faper) 2019-E-26/202.568/2019 (245653), and \"Ministerio de Ciencia, Innovacion y Universidades\" of Spain [PID2021-127713 OA-I00, PID2021-123511 OB-C32, PID2021-123511 OB-C33, PID2021-124139 NB-C22-MCIN/AEI/10.13039/501100011033/FEDER, EU, TED2021-129851B-I00-/AEI/10.13039/501100011033/Union Europea NextGenerationEU/PRTR and RED2022-134219-T]. Not applicable. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. The authors declare no conflicts of interest. * [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON] Machine learning based prediction model for thermal conductivity of concrete. _J. Build. Eng._**2021**, _34_, 101956. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON] Predictive modelling of sustainable lightweight foamed concrete using machine learning novel approach. _J. Build. Eng._**2022**, _56_, 104746. [CrossRef]
* [PERSON] and [PERSON] (2020) [PERSON]; [PERSON] Machine learning study of the mechanical properties of concretes containing waste foundry sand. _Constr. Build. Mater._**2020**, _243_, 118152. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] An ensemble machine learning approach for prediction and optimization of modulus of elasticity of recycled aggregate concrete. _Constr. Build. Mater._**2020**, _244_, 118271. [CrossRef]
* [PERSON] (2020) [PERSON] Machine learning approach for predicting concrete compressive, splitting tensile, and flexural strength with waste foundry sand. _J. Build. Eng._**2023**, _70_, 106363. [CrossRef]
* [PERSON] et al. (2006) [PERSON]; [PERSON]; [PERSON] A concrete mix proportion design algorithm based on artificial neural networks. _Cem. Concr. Res._**2006**, _36_, 1399-1408. [CrossRef]
* [PERSON] and [PERSON] (2009) [PERSON]; [PERSON] Evaluation of chloride penetration in high performance concrete using neural network algorithm and micro pore structure. _Cem. Concr. Res._**2009**, _39_, 814-824. [CrossRef]
* [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON] Predicting carbonation depth of concrete using a hybrid ensemble model. _J. Build. Eng._**2023**, _76_, 107320. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Prediction of surface chloride concentration of marine concrete using ensemble machine learning. _Cem. Concr. Res._**2020**, _136_, 106164. [CrossRef]
* [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Predictive models for concrete properties using machine learning and deep learning approavhes: A review. _J. Build. Eng._**2023**, _63_, 105444. [CrossRef]
* [PERSON] and [PERSON] (2018) [PERSON]; [PERSON]; [PERSON] Compressive strength prediction of environmentally friendly concrete using artificial neural networks. _J. Build. Eng._**2018**, _16_, 213-219. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] An integrated building energy performance evaluation method: From parametric modeling to GA-NN based energy consumption prediction modeling. _J. Build. Eng._**2022**, _45_, 103571. [CrossRef]
* [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Experimental study and machine learning algorithms for evaluating the performance of U-shaped ultra-high performance reinforced fiber concrete under static and impact loads. _J. Build. Eng._**2023**, _70_, 106389. [CrossRef]
* [PERSON] and [PERSON] (2020) [PERSON]; [PERSON] [PERSON] Compressive strength assessment of concrete containing metakaolin using ANN. _[PERSON]. Rehabil. Civ. Eng._**2020**, \(8\), 15-27.