# Application of Artificial Neural Networks to Predict Insulation Properties of Lightweight Concrete
## 1 Introduction  
Lightweight cement composites, as well as lightweight concrete (LWC), are a group of materials with an increasing demand in construction. LWC has a lower density and a lower coefficient of thermal conductivity than ordinary concrete [1]. In order to obtain sufficiently high strength of LWC as a construction material, its composition can be modified [2; 3; 4]. Due to the tendency to reduce CO\({}_{2}\) emissions, especially in countries where building heating is needed, more and more requirements regarding thermal insulation for external walls are being introduced. On the other hand, in countries where the air temperature remains high and air-conditioning units are in extensive use, the same LWC material solutions can reduce heat transfer into the building interior [5; 6]. Therefore, there is an increasing demand for construction materials with high thermal insulation and relatively high compressive strength at the same time [7; 8]. All over the world, research is conducted on the modification of LWC properties to create positive impact on the environment. To improve thermal insulation properties of buildings, the origin and properties of LWA used for the production of LWC should be taken into account [9; 10]. LWA may be of natural origin, such as pumice or fired clay, and may be produced artificially from waste, such as fly ash, sewage sludge, or glass cullet. The properties of artificial LWAs can vary considerablydepending on the origin of the main waste material used in their composition and the way in which it is processed. Until recently, LWC design relied heavily on the knowledge and experience of LWA and concrete producers, as it is difficult to recreate conditions, similar to those on the actual construction site, in the laboratory [11; 12]. However, increasing research capabilities allow for the precise determination both of the very properties of LWA and for the simulation of conditions close to the environment (high or low temperatures) and to update our knowledge about the properties of LWC [13; 14]. The authors of this publication focused mainly on the possibility of using a ready mix of LWA: granulated expanded glass (GEGA) and sintered fly ash (GAA). GEGA, classified as a lightweight material, is appreciated for its low density and much higher thermal insulation than normal weight concrete (NWC) with natural aggregate [15]. GAA, on the other hand, is characterized by high compressive strength. The use of two types of the aggregates with different properties in different proportions allows modeling of LWC properties in terms of density, compressive strength and thermal conductivity [16]. Experimental and analytical-experimental design methods are used to determine the effect of the components used on the characteristics of the LWC mixture. The laboratory tests require high level of researchers' involvement, they are time-consuming and therefore costly. Moreover, in order to obtain the desired effect, whether in terms of specific density, strength or thermal insulation, the tests must be repeated many times. The tests should also be repeated in the event of change in the type or quality of the main components of mix concrete [17]. ANN can be used to predict output data based on a defined set of input data as the authors of the works show [17; 18; 19; 20]. The main aim of the work was to present a proposed application for the use of ANN to forecast mechanical properties and thermal conductivity in correlation with porosity and density of LWC depending on the mixing ratio of GEGA and GAA. The main benefit of using computer technology with an ANN testing method to solve a predetermined problem is that there is no need to derive mathematical dependencies, because in the process of ANN learning, appropriate values are assigned to subsequent variables, and then the solution obtained in the process of experimental research is implemented [21; 22]. In the presented work, the results of LWC tests, obtained earlier in the laboratory, were analyzed.