An Invasive Weed Optimization-Based Fuzzy Decision-making Framework for Bridge Intervention Prioritization in Element and Network Levels

Abstract

Recently, the number of deteriorating bridges has drastically increased. Furthermore, tight maintenance budgets are cut down, imposing escalating adverse implications on the safety of bridges. This state of affairs entails the development of decision support systems for the effective management of bridges within the allocated budget. As such, this study introduces an invasive weed optimization-based fuzzy decision-making framework designated for bridge intervention prioritization in both element and network levels. The proposed decision-making platform encompasses three main tiers. The first tier is an optimized fuzzy analytical network process model that aims at computing the weighting vector of the bridge defects, namely corrosion, delamination, cracking, spalling and scaling. In this model, a genetic algorithm optimization model is formulated to improve the consistencies of judgment matrices through circumventing the imprecisions encountered by the classical judgment assignment. The second tier encompasses establishing an integrated bridge deck condition assessment model capitalizing on ground-penetrating radar and inspection reports. In it, the severities of the bridge defects are demonstrated in the form of fuzzy membership functions to address the inherent uncertainties of inspection. Subsequently, a variable-length invasive weed optimization model is structured to automatically calibrate the fuzzy membership functions. The third model is designed for structuring a bridge maintenance decision-making strategy stepping on the integrated condition index. The capabilities of the proposed framework were validated through several levels of comparisons. For instance, it significantly outperformed some of the current condition assessment models. Additionally, it inferred that the thresholds separating the four categories of the integrated bridge deck condition index are 75.651, 67.769 and 60.318.