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Resources-especially funds allotted-for the maintenance of bridges, are generally scanty. Thus, it becomes difficult to select bridges for maintenance from among several competing bridges to ensure their safety and serviceability to the desired level. A bridge health index is considered a reasonably accurate depiction of the condition of a bridge and hence is the basis for most of the decisions on fund allocation. However, it still remains to be seen whether such a decision-making tool results in an efficient fund allocation. From data collected on Indian bridges, it is observed that fund allocation based on bridge condition is not always judicious. Rather, a number of factors affect the final decision on fund allocation. Hence, an alternative approach of data envelopment analysis (DEA) has been used for scoring the efficiency of 14 bridges selected for the study. Depending on the availability of data, this method can take into account other factors besides the bridge health index that influence decisions on maintenance planning. The variables selected for the DEA are: bridge health index, deck area of the bridge, maintenance cost of the bridge, and the age of the bridge. The allocation of funds for the maintenance of bridges based on DEA has proved to be comparatively more efficient. This has been illustrated with the help of a numerical example. The proposed method would enable bridge authorities to formulate better strategies for planning and executing bridge maintenance activities in a cost-effective manner.

PurposeThe failure of a bridge is a complex phenomenon due to the involvement of a large number of factors. Bridge failure cannot be attributed to a single cause due to the intricacies involved in the relationship among the factors causing failure. The aim of this paper is to identify the various factors causing bridge failure and show an interdependence/relationship among them.Design/methodology/approachInterpretive structural modeling (ISM), which is a tool of structural analysis for interactive learning, has been used to develop a hierarchical structure of the phenomenon of bridge failure showing the interrelationship of the causative factors. Impact Matrix Cross Reference Multiplication Applied to a Classification (MICMAC) tool has been used to quantify and classify the factors for bridge failure on the basis of their influence and dependence.FindingsIn total, 14 factors, which may contribute to the failure of a bridge, have been identified through the literature survey and interaction with bridge experts. Even though all the 14 factors are important, the authors have found that the environmental factor has the maximum influence, while the age factor is the most dependent of all factors.Research limitations/implicationsThe proposed research utilizes the ISM and MICMAC tools and thus has all the limitations of structural modeling. The application of structural equation modeling in the civil engineering field is limited, but it is hoped that a similar approach can be applied in other areas of engineering.Originality/valueThe factors causing bridge failure have been identified and hierarchical interdependence model has been arrived at by using ISM. On the basis of MICMAC analysis, the factors have also been classified into three categories, namely, influent, autonomous and dependent. The study reaffirms the importance of all factors in the overall performance of a bridge.