# Hybrid Metaheuristic-Neural Assessment of the Adhesion in Existing Cement Composites
## 1 Introduction  
Layered cement composites, recently an issue attracting numerous researchers [1; 2; 3; 4], usually consist of an overlay placed on an existing concrete substrate. It is always necessary to ensure an appropriate bond between the overlay and existing concrete substrate. The measure of this bond is the value of the pull-off adhesion (\(f_{\text{b}}\)), obtained using the pull-off method [5]. This method is time consuming due to the delay time of the curing of the resin used for bonding the steel disc. Moreover, results can be influenced by variation on the rupture surface, the orientation and position of the aggregate onto the disc, the disc material, diameter, and thickness, the backpressure system, and also the speed of load application [6; 7; 8]. The tested surface in each of the measuring places is damaged and the efficiency of this method depends on the number of measuring places. The damage has to be repaired after the test. When considering the above, it is advisable to use non-destructive testing (NDT) methods for the purpose of the assessment of the interlayer bond. It has been verified in practice that the impact-echo (I-E) and impulse response (IR) methods may be used individually to make a successful zero-one assessment [9; 10]. However, it is not possible to individually use the above-mentioned NDT methods to make a full, reliable assessment of the value of \(f_{\text{b}}\)[11; 12; 13]. In such acase it is helpful to use artificial neural networks (ANNs) which, in recent years, are increasingly used in civil engineering [14; 15; 16; 17; 18; 19; 20; 21; 22; 23]. A new method of identifying \(f_{\rm b}\) by means of the ANN method has recently been proposed [24; 25; 26; 27; 28; 29]. The database for this identification was created using 3D morphological parameters, which were evaluated on the existing concrete substrate surface using 3D LASER scanning, and also acoustic parameters obtained with the use of the IR and I-E methods on the surface of the overlay. Various methods have been used, such as radial basis functions (RBF) [24], multi-layer perceptron (MLP) [25; 26; 27], and principal component analysis (PCA), in combination with self-organizing feature maps (SOFM) [28]. It is evident that there is no possibility in existing layered cement composites of obtaining 3D morphological parameters of the existing concrete substrate surface. Consequently, it is not possible to adopt the previously-developed method to identify the value of \(f_{\rm b}\) in existing cement composites. Thus, the attempt presented in [29] proved that it is possible to identify the value of \(f_{\rm b}\) between the overlay and existing concrete substrate in existing cement composites on the basis of the acoustic parameters obtained on the overlay surface using the ANN, IR, and I-E methods. The multi-layer perceptron ANN with the gradient descent (MLP-GS) learning algorithm has been found to be useful for this purpose. However, values of determination coefficient \(R^{2}\) greater than 0.77 were not satisfied. Simple back-propagation (BP) has also been used for optimizing the ANN, which is plagued with inconsistent and unpredictable performances, a slow learning rate, and becoming trapped in local minima [30; 31]. Thus, in some applications it is necessary to improve the performance of the ANN with the use of optimization algorithms. The imperialist competitive algorithms (ICA), the genetic algorithm (GA), or particle swarm optimization (PSO) have recently been used for this purpose [32]. The recently developed ICA is a randomized population method inspired by the human political-social evolution [33; 34; 35; 36; 37]. It belongs to the metaheuristic group of methods that are expected to become more popular in various engineering applications [38; 39; 40; 41; 42; 43; 44]. In the ICA, a number of colonial countries, along with their colonies, try to find a general optimal point in solving the optimization problem. Different methods are then introduced to solve the optimization problems [45; 46].