Risk Score Inference for Bridge Maintenance Project Using Evolutionary Fuzzy Least Squares Support Vector Machine

Abstract

Bridges are essential infrastructure of the transportation network. Therefore, maintenance tasks are mandatory to prevent these structures from degradation over time. In practice, funding availability for maintenance projects is often confined and this necessitates prioritization of different bridges that are in need of remedial activities. The writers aim to construct an artificial intelligence (AI) approach, evolutionary fuzzy least-squares support-vector machine (LSSVM) inference model (EFLSIM), for prioritizing bridges based on risk scores (RSs). In EFLSIM, fuzzy logic (FL) is utilized to enhance the capability of approximate reasoning and to deal with subjective information, which is obtained from human judgment. The inference model employs LSSVM as a supervised learning technique to infer the fuzzy input-output mapping relationship. Differential evolution (DE) is integrated into the model to optimize its tuning parameters. Experimental results and comparison illustrates that EFLSIM can successfully absorb and simulate human knowledge in the bridge-assessment process. Additionally, the newly built model has outperformed other benchmark approaches in terms of both reliability and accuracy. A 10-fold cross-validation process has demonstrated that the EFLSIM has achieved more than 38% reduction in RMS error compared to other benchmark methods. Thus, the proposed AI approach is a promising tool to support decision-makers in bridge-maintenance planning.