Machine learning techniques generally require thousands of cases to derive a reliable conclusion, but such a large number of excavation cases are very difficult to acquire in the construction domain. There have been efforts to develop retaining wall selection systems using machine learning techniques but based only on a couple of hundred cases of excavation work. The resultant rules were inconsistent and unreliable. This paper proposes an improved decision tree for selecting retaining wall systems. After retaining wall systems were divided into three components, i.e., the retaining wall, the lateral support, and optional grouting, a series of logistic regression analyses, analysis of variance (ANOVA), and chi-square tests were used to derive the variables and a decision tree for selecting retaining wall systems. The prediction accuracy rates for the retaining walls, lateral supports, and grouting were 82.6%, 80.4%, and 76.9%, respectively. These values were higher than the prediction accuracy rate (58.7%) of the decision tree built by an automated machine learning algorithm, Classification and Regression Trees (CART), with the same data set.
Bibliographical noteFunding Information:
The authors would like to thank the following for providing financial support: the Ministry of Construction & Transportation (MOCT), Korea , and the Korea Institute of Construction and Transportation in Technology Evaluation and Planning (KICTTEP) through Innovative and Rapid Construction Technologies Joint Venture (IRCT) under Program No. 05 RND Core Technology D02-01.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Civil and Structural Engineering
- Building and Construction