Condition Assessment of Suspension Bridges Using Local Variable Weight and Normal Cloud Model

A systematic work has been presented for condition assessment of suspension bridges in this study. Initially, a four-layer index system is built up. Subsequently, 45 experts are invited to determine the index weights by processing the experts’ opinions using the Group Decision-Making (GDM). The assigned weights seem more reasonable (especially weights of the tower and auxiliary facility) when compared with those in the existing China’s code. Next, assessment algorithms, the Local Variable Weight Model (LVWM) and Normal Cloud Model (NCM), are established based on the characteristics of the bridge condition assessment. The LVWM adjusts weight properly to make assessment results in line with the actual situation under extreme cases. The NCM describes not only the fuzziness but also the randomness in the assessment process. Finally, a case study is illustrated to verify the effectiveness of the methodology. For sake of highlighting the advantages of the LVWM, two more models are subjected to the case study, which are the Constant Weight Model (CWM) and Traditional Variable Weight Model (TVWM). Consequently, the assessment result of the LVWM is more in keeping with the actual situation than those of the CWM or TVWM.