Data Envelopment Analysis for Efficiency Measurement of Bridge Resilience

Abstract

The resilience of a bridge is computed using different quantitative and qualitative assessment methodologies. However, the resilience score obtained by these assessment approaches is insufficient for the decision-makers for setting a priority level for bridges in need of resilience improvement. To address this issue, the present study develops a methodology using the data envelopment analysis (DEA) approach. A total of 12 bridges are selected as the decision-making units in the DEA model. This study considers the variables such as age, area, design high flood level, and finish road level of the bridge as inputs, and bridge resilience index as the output variable. Based on these variables, three frameworks are developed to compute the efficiency of bridge resilience. A variable return to scale with the output-oriented formulation of DEA is selected to compute the efficiency of bridge resilience in all three frameworks. Thus, the proposed methodology enables bridge owners to set a priority level for bridges in need of resilience improvement based on the scores of the assessment methodology.

Uncontrollable Factors Analysis on Sustainable Highway Routine Maintenance Management: A Case Study of Shaanxi Province in China

“…In essence, this can be viewed as a process involving multiple inputs and multiple outputs. Based on this characteristic, DEA is one appropriate method to evaluate highway maintenance management performance (HMMP), which can be mainly classified into two aspects in existing studies: maintenance operations [28][29][30][31][32][33] or bridge maintenance [19,[34][35][36][37].…”