# Methodology for the Prediction of the Thermal Conductivity of Concrete by Using Neural Networks
## 1 Introduction  
Driven by a growing sustainability awareness, energy efficiency is a topic that researchers are addressing more and more. As in other areas, concerns about energy efficiency solutions for buildings have largely increased. For this reason, the demand for the development of materials to improve thermal performance in buildings has increased. An appropriate design and material selection can lead to more environmentally responsible and economically sustainable construction practices. Using materials for better thermal efficiency can preserve indoor thermal comfort despite fluctuations in outdoor environment conditions and reduce energy consumption [1]. In the building design phase, vast possibilities for methods and materials exist to guarantee an adequate combination to achieve an energy-efficient building. Two types of methods can be used to improve the energy efficiency of a building: active methods and passive methods. The first type of method incorporates energy-efficient technologies to use or produce energy in the building, such as heat pumps and solar photovoltaic panels. The second type of method uses some countermeasures to reduce the thermal transmittance of the structure and enhance this efficiency using materials with low thermal conductivity. For instance, concrete can be used as part of the building envelope to improve energy efficiency [2]. It is the most used and stands out the most among the different building materials due to its unique features. Besides its resistance, durability, and low cost, itsinherent thermal mass attracts decision-makers. It allows heat to be absorbed, stored, and gradually released, stabilizing indoor temperatures and reducing unnecessary heating or cooling [3]. However, as concrete is a composite material of cement, water, fine aggregate, and coarse aggregate, its composition presents a wide variety, leading its properties to undergo significant variations [4]. Adjusting these constituents and their quantities allows the properties of both fresh and hardened states to be tailored to achieve the required design specifications. Thus, a specific property can be increased or decreased by changing the mass composition or including different constituents in a concrete mixture. Thermal conductivity is one fundamental property affecting a material's energy efficiency, which measures how well a material conducts heat. Regarding the effect of heat transfer in a building, materials with low thermal conductivity value are used as insulation. They reduce the heat transfer between theinside and outside of the building, which maintains comfortable temperatures and reduces the energy consumption of heating and cooling systems. Factors influencing concrete's thermal conductivity include the constituents' type and weight, such as fine and coarse aggregates, supplemental cementitious materials (SCMs), and fibers [5]. Since a concrete composition contains 50-70% aggregates, their mineralogy and volume in the mixture have the most effect on the thermal conductivity of concrete [6]. Accurately predicting the thermal conductivity of concrete becomes vital to improving buildings' energy efficiency. A prediction model can help avoid many time-consuming experiments in the design and material selection phases. In recent years, machine learning (ML) models have been used to predict some properties of concrete, such as compressive strength [7], tensile strength [8], modulus of elasticity [9], flexural strength [10], slump [11], chloride penetration [12], carbonation depth [13], and surface chloride concentration [14]. Although many researchers have used ML, there is a limitation in studies developing prediction models of the thermal properties of concrete [15]. Among the machine learning-based models, the artificial neural network (ANN) is one of the most employed ones to solve complex problems and has applications in many fields [16; 17; 18]. The importance of ANNs lies in their ability to learn and make decisions based on data, which makes them highly valuable in different areas.