# An Evolutionary Neural Network Approach for Slopes Stability Assessment
## 5 Final Remarks  
After a first attempt to identify the stability of rock and soil cuttings and embankments using machine learning algorithms and taking into account the visual features that are usually collected during routine inspections, a second attempt was made by combining artificial neural networks (ANNs) and genetic algorithms (GA), a combination also known as an evolutionary neural network (ENNs), for feature selection and slope stability classification purposes. This innovative approach allows ANNs and GAs to work together to assess the stability of a slope. Although the overall performance did not improve significantly, the new models presented some important advantages. First, to achieve the same overall performance, the number of input variables decreased to less than 30. This is a key point from a practical point of view since it endows the model with more flexibility when less information is available. Furthermore, this can allow the railway infrastructure management companies to collect less information to determine the stability level of their slope network, which, in turn, will allow them to reduce costs related to routine inspections. Second, better performance is achieved for the identification of classes \"C\" and \"D\", which correspond to a higher probability of failure, without significantly compromising the model's performance on the remaining classes. From a safety point of view, this represents a significant improvement, allowing for the strategic investment of the available budget on critical slopes. In addition, such a strategic maintenance plan can reduce traffic constraints due to slope failures and, consequently, the probability of severe incidents. In summary, these results are encouraging and consist of a promising research direction towards a more efficient identification of the stability of slopes, particularly for minority classes. Thus, as a future development, we intend to explore other feature selection approaches as well as different strategies to handle imbalanced data (e.g., the usage of Tomek links or Gaussian copula transformations [72]). In addition, considering the high number of data available for each of the three slope types, a deep learning approach may provide an important contribution toward the prediction models' efficiency. Despite the fact that new attempts are still necessary, this study demonstrates once more the extreme difficulty of such a task, namely by considering only visual features. Nonetheless, this difficult task is of the utmost importance, especially for developed nations that must maintain large-scale transportation networks despite limited budgets for maintenance and network operations. All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by [PERSON], [PERSON], [PERSON] and D.T. The first draft of the manuscript was written by [PERSON], and all authors commented on previous versions of the manuscript. All authors have read and agreed to the published version of the manuscript. This work was supported by FCT, the \"Fundacao para a Ciencia e a Tecnologia\", within the Institute for Sustainability and Innovation in Structural Engineering (ISISE), project UID/ECI/04029/2013, as well as Project Scope: UID/CEC/00319/2013 and through the post-doctoral grant fellowship with reference SFRH/BPD/94792/2013. This work was also partly financed by FEDER (Fundo Europeu de Desenvolvimento Regional) funds through the Competitivity Factors Operational Programme--COMPETE and by national funds through FCT within the scope of the project POCI-01-0145-FEDER-007633. This work has also been supported by COMPETE: POCI-01-0145-FEDER-007043. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** No human participants were involved in this research. **Data Availability Statement:** The data that support the findings of this study are not publicly available due to privacy issues.