Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
246916 | Automation in Construction | 2012 | 7 Pages |
Slope collapse prediction inference errors may be divided into two types, namely 1) predicted collapse followed by actual non-collapse (i.e., α error) and 2) predicted non-collapse followed by actual collapse (i.e., β error). As limited time and information make it difficult to reduce the rate of prediction error, making predictions in a manner that considers decision maker risk preferences in order to consider the preferred α to β error ratio in road slope maintenance strategy formulation represents an important issue.This study proposes an innovative inference model, the Risk Preference based Support Vector Machine Inference Model (RP-SIM). RP-SIM infers the mapping relationship between input and output variables from historical cases using a Support Vector Machine (SVM), and then uses a fast messy genetic algorithm (fmGA) to conduct an optimal search based on α and β values set in accordance with actual decision maker risk preference.
► This study proposes an innovative inference model, the RP-SIM that incorporates decision maker risk preference. ► RP-SIM infers the mapping relationship between input and output variables from historical cases using an SVM. ► RP-SIM uses an fmGA to conduct an optimal search based on α and β values set in accordance with risk preference. ► Effectively considers decision maker risk preference.