Reports on precipitation and rainfall data show that Congo has a tropical climate characterized by heavy rainfall for eight (08) months out of twelve (12), with volumes reaching up to 225 mm in November and April. Land use and the construction of various structures by the population do not take into account the runoff caused by heavy rainfall. This situation poses serious problems for the architectural quality of buildings and the environment. As a result, human settlements are extremely precarious and unsanitary. Each rainfall causes damage to roads, flooding of plots, silting and soil pollution. Field observations and data from recent newspapers and publications have identified demographic and rainfall characteristics as well as phenomena that cause damage to the urban environment. This study highlights the need to develop a national building standard that takes into account heavy and intense rainfall in the Congo. The main conclusions show that rainfall is one of the key factors influencing the quality of the built environment, that is to say buildings and infrastructure. The anti-erosion development model proposed in this study uses eco-parceling to strengthen the resilience of buildings in the face of natural events. The implementation of such development plans could help engineers and public authorities in the urban crisis linked to natural disasters.

In practical engineering systems, accounting for various uncertainties during the design process is paramount. However, reliability analysis in structural engineering often entails substantial computational costs, particularly when dealing with implicit performance functions and scenarios involving very low failure probabilities. This inherent complexity underscores the challenges faced in real-world applications, where efficient and accurate reliability assessments are crucial for ensuring structural integrity and safety. In recent years, the concept of utilizing surrogate models for reliability analysis has garnered significant attention. The approach outlined in this study employs an innovative surrogate framework that concurrently integrates Cascade-forward Neural Networks (CFNN) and Self-Organizing Map (SOM) networks, alongside an optimized kriging model. The final reliability assessment is then determined as a weighted average of the outputs from these integrated models. To comprehensively illustrate the effectiveness of the proposed algorithm, a diverse range of examples are included: five mathematical examples and five engineering examples. Furthermore, a detailed discussion highlights the benefits of this proposed method in comparison to alternative approaches. The results demonstrate the effective performance of the developed methodology. For instance, in the mathematical examples, the minimum improvement observed over other methods is an 81%, coupled with an approximate 0% error in reliability calculation. Similarly, for the engineering examples, a minimum improvement of 47% is achieved over existing methods, with the reliability calculation error remaining low at approximately 1%.