Selection of Optimized Retaining Wall Technique Using Self-Organizing Maps

Construction projects in urban areas tend to be associated with high-rise buildings and are of very large-scales; hence, the importance a project’s underground construction work is significant. In this study, rational model based on machine learning (ML) was developed. ML algorithms programs that can learn from data improve experience without human intervention. self-organizing maps (SOMs) were utilized. An SOM an alternative existing methods involves subjective decision-making process because developed used for training classify effectively recognize patterns embedded input space. addition, unlike methods, easily create feature map by mapping multidimensional simple two-dimensional data. The objective study develop as approach selecting retaining wall technique. N-fold cross-validation adopted validate accuracy evaluate its reliability. findings useful method, demonstrated study. maximum 81.5%, average 79.8%.