###### Abstract
The result showed that the mechanical properties and density were improved from 400 kg/m\({}^{3}\) to 1650 kg/m\({}^{3}\), and PUC demonstrates high bond properties with cement-based materials. [PERSON] [23] reported that PU foam decreases mechanical behavior and improves shrinkage performance. [PERSON] et al. [26] reported that cation PU accelerates the hydration of cement, modifies its mineral content, and decreases cement's brittleness. Moreover, the inclusion of a 0.5% PU content improved the tensile property at early age of curing time. [PERSON] et al. [27] introduced nano silica as a filler material in polymer nanocomposite for the improvement in mechanical properties. In the last decade, conventional linear and nonlinear analyses have been employed to evaluate the properties of concrete. For instance, the design of the experiment and regression analysis was used to determine concrete behavior and optimize the concrete mixtures. However, these conventional techniques have some deficiencies when dealing with complex problems. Previous studies have indicated that artificial intelligence has been used in engineering applications to solve complex problems. These include artificial neural networks [28; 29] emotional intelligence and traditional FFNNs [30], novel data intelligent algorithms [31], the Hammerstein-Wiener and SVM models [32], ANNs, K-nearest neighbor, regression tree, and Multi-response optimization [28; 33] being adopted. [PERSON] et al. [34] evaluated the performance of credit scoring from the mining data techniques using classification, regression trees, and adaptive regression splines. [PERSON] and [PERSON] [35] analyzed the compressive strength of high-performance concrete using classification and regression techniques. [PERSON] et al. [36] used a multi-objective ANN model to analyze the porosity on the prediction of hardening behavior of mortar involving micro and nano-silica. The results showed that concrete flexural strength was estimated with high accuracy. [PERSON] et al. [37] established artificial neural networks, decision trees, and gradient-boosting models to predict concrete properties subjected to high temperatures. [PERSON] et al. [38] used a multiscale approach to estimate the strength of nano-engineered concrete involving nano-silica. Moreover, several studies have attempted to analyze the hardened behavior of polyurethane cement concrete using conventional and artificial intelligence techniques. Few studies have been performed so far concerning polyurethane-cement concrete. For instance, [PERSON] and [PERSON] [39] proposed a combined theoretical deduction and experimental method to predict the fatigue life of a polyurethane cement composite subjected to low and high temperatures. The authors reported that the proposed formula could effectively evaluate polyurethane cement composites' fatigue life and limit. [PERSON] et al. (2018) used the ANN algorithm to study the effect of powdered tire waste and the epoxy resin content on the mechanical properties of polymer concrete. [PERSON] et al. (2018) developed an ANN model for the prediction of mechanical properties (compressive and flexural strength) of polymer concrete containing fly ash. The polymer concrete containing different fly ash and resin contents was tested to evaluate the effect of fly ash on the strength. PU-cement mortar has been widely used as a repair material for civil engineering infrastructure such as concrete buildings and pavement. However, previous (2018; 2018; 2019) studies have indicated that PU binder negatively affects cementitious material by reducing mechanical properties. On the other hand, nanosilica improves the mechanical properties of cementitious materials (mortar and ordinary concrete) by reducing pore structures, thus densifying the materials, which leads to the improvement in mechanical properties (2018; 2019), was incorporated into the PU-cement mortar in this study, targeting the mitigation of the negative effect PU binder in the mortar.