Abstract
Construction projects in urban areas tend to be associated with high-rise buildings and are of very large-scales; hence, the importance of a project’s underground construction work is significant. In this study, a rational model based on machine learning (ML) was developed. ML algorithms are programs that can learn from data and improve from experience without human intervention. In this study, self-organizing maps (SOMs) were utilized. An SOM is an alternative to existing ML methods and involves a subjective decision-making process because a developed model is used for data training to classify and effectively recognize patterns embedded in the input data space. In addition, unlike existing methods, the SOM can easily create a feature map by mapping multidimensional data to simple two-dimensional data. The objective of this study is to develop an SOM model as a decision-making approach for selecting a retaining wall technique. N-fold cross-validation was adopted to validate the accuracy of the SOM model and evaluate its reliability. The findings are useful for decision-making in selecting a retaining wall method, as demonstrated in this study. The maximum accuracy of the SOM was 81.5%, and the average accuracy was 79.8%.
Highlights
The selection of a suitable construction method is necessary because of site conditions and the unique characteristics of each construction project
The retaining wall techniques are divided into six groups: SW, SCW, CIP, JetGr, LWGr, and HSCW, which are generally used on site
In the feature map of the underground excavation depth, this area corresponds to the red area, which is applied at the extended drilling depth
Summary
The selection of a suitable construction method is necessary because of site conditions and the unique characteristics of each construction project. An essential task in underground construction is selecting suitable retaining wall techniques according to the excavation depth and other working parameters. In most cases, existing AI techniques require a significant amount of data to produce reliable results. Another limitation of previous studies was that the technique was chosen without following a reasonable validation process between the variables and excavation methods. Choi and Lee [2] reported that the inappropriate forecasting of a retaining wall technique has led to significant schedule delays and increased costs, owing to unexpected changes in the construction method during the actual construction. A self-organizing map (SOM) model was used to select a retaining wall construction method during the design phase of a construction project. The results of this study, conclusions, and suggestions for future research are discussed in the concluding section
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