# An Evolutionary Neural Network Approach for Slopes Stability Assessment
## 1 Motivation and Background  
Transportation infrastructures are a key and strategic asset in our day-to-day life. In fact, nations frequently spend money maintaining or improving their transportation networks in an effort to create more secure and effective infrastructure. The main issue currently facing nations with highly developed transportation systems is how to keep it running in all circumstances. How to identify the crucial network components that need funding to be allocated for their upkeep or repair is a crucial issue from the perspective of managing a transportation network. As a result, and in order to maximize the budget that is available, decision support tools are needed to assist decision makers in identifying such key network components and selecting the optimal course of action for allocating the budget that is available. Slopes are possibly the component in the framework of transportation networks, particularly for railways, for which their failure can have thelargest effects on a number of levels, including potentially significant economic damage and loss of life. As a result, it is critical to create techniques to spot possible issues before they become failures. Many models and approaches have been proposed over time to identify slope failures. However, since the majority were designed for natural slopes, they have considerable drawbacks when used on constructed (human-made) slopes. Additionally, they have limited network level applicability because the majority of the current systems were developed using small databases or based on individual case studies. The need for data from complex tests or costly monitoring systems represents an extra obstacle to the application of the majority of current slope stability assessment systems. Below, we summarize some approaches found in the literature for slope failure detection. The current methods for evaluating slope stability were compiled by [PERSON] and [PERSON] [1], which they classify as limit equilibrium (LE) methods, numerical analysis methods, artificial neural networks, and limit analysis methods. Recently, [PERSON] et al. [2] also published a brief review of the actual methods for slope stability assessments, having dived them into five distinct groups instead of four. Among all these approaches, the literature has emphasizedthe finite elements methods [3], reliability analysis [4; 5], and those approaches based on machine learning algorithms [6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25]. More recently, some new approaches have been proposed based on the vector sum method [26; 27]. In 2015, [PERSON] et al. [28] developed a new and versatile statistical system based on the evaluation of various factors affecting the stability of a given slope. By weighting the different factors, a final indicator of the slope stability can be determined. Later, in 2016, an evidence-based asset management strategy was proposed by [PERSON] et al. [29], which is comprised of the establishment of a risk-based prioritization matrix for all earthwork assets and the quantification of the probability of earthwork failure. These and numerous other approaches that can be found in the literature, from the perspective of large-scale network management, are constrained to a limited applicability domain and reliant on information that can be difficult and expensive to obtain. In addition, determining whether a slope will fail or not is frequently a difficult task involving many variables and high dimensionality. In order to work around these kinds of restrictions and aid in the decision-making process for large-scale network management, a first endeavor was recently made [30; 31] by comparing two popular types of data mining (DM) algorithms: artificial neural networks (ANNs) [32; 33] and support vector machines (SVMs) [34]. When compared with other simpler learning models (e.g., multiple regression or logistic regression models), the ANN and SVM models are more flexible learners, being capable of learning complex input-to-output mappings.