# Methodology for the Prediction of the Thermal Conductivity of Concrete by Using Neural Networks
## 2 Overview of Neural Networks  
Over the years, ANNs have been applied to predict different properties of concrete due to their ability to model complex non-linear relationships. Regarding the property predictions, the mechanical properties are the most evaluated, with compressive strength being the most investigated in different machine learning models. [PERSON] was one of the pioneers applying an ANN to predict the properties of concrete and developed some models to predict compressive strength and slump [23; 24]. [PERSON] et al. estimated compressive strength using a hybrid model of an ANN and the salp swarm algorithm [25]. [PERSON] trained an MLP model to forecast the compressive strength for a given ultra-high-performance concrete mixture design [26]. Besides this, another work also developed an ANN with a feedforward backpropagation algorithm to predict slump flow and compressive strength while incorporating silica fume, limestone powder, recycled glass powder, and fluid catalytic cracking residue [27]. Although many studies are developing new models to predict the properties of concrete, only a few papers have investigated thermal conductivity or other thermal properties. [PERSON] et al. [28] trained different structures of an ANN model to predict thermal conductivity through five parameters--density, compressive strength, tensile strength, porosity, and ultrasonic pulse velocity. The best solution performance was achieved with a neural network with three layers and the following neuron sequence of 5, 25, and 1 in each layer. [PERSON] et al. [6] evaluated nine machine learning models, and MLP showed the highest prediction accuracy using the maxout activation function and three hidden layers, each containing 100 neurons. [PERSON] et al. [29] also investigated the influence of varying the number of neurons in the hidden layer to forecast the thermal conductivity of lightweight concrete. The model presented a sigmoid function and a structure with four layers: an input layer with two neurons, a first hidden layer varying from 2 to 12, a second hidden layer ranging from 2 to 17, and an output with one neuron. [PERSON] et al. [30] used an ANN and ANOVA to investigate the effect of partial replacement with waste marble powder and rice husk ash instead of fine aggregate and cement into foam concrete. The results indicated the ANN as the most adequate to estimate the thermal conductivity. [PERSON] et al. [31] compared two neural networks, a radial basis neural network and an MLP, to predict the thermal conductivity of concrete with vermiculite and concluded that the former had greater accuracy. Different types of ANNs have been successfully applied to predict the thermal conductivity of concrete. Table 1 compares some studies using distinct architectures of ANNs, and indicates that most models employ the backpropagation algorithm and do not have an extensive database. Besides this brief literature overview, a general bibliometric analysis was performed to identify the most relevant and influential literature in predicting the properties of concrete using machine learning. This approach enables the identification of critical research gaps and areas for future investigation. To proceed with this analysis, VOSviewer software 1.6.19 [35] was used. It visually represents the research and supports researchers in finding insights into the research domain. Consequently, it can lead to the more effective development of prediction models of thermal properties. In order to evaluate the development of the research regarding property predictions using machine learning, a bibliometric study in the Web of Science database was performed. The following query was used: (\"concrete\") AND (\"properties\" OR \"thermal conductivity\") AND (\"machine learning\" OR \"deep learning\" OR \"artificial neural network\" OR \"ANN\"). The search criteria used in this bibliometric study summarized 1673 articles and reviewed articles published between 2014 and 2023.