Journal of Infrastructure Systems

Transportation infrastructures and operations can be severely impacted during flood events, leading to significant disruptions to the flow of goods and services. Although numerous studies have evaluated the direct impacts of flood events on the performance of transportation infrastructures, the indirect impacts or cascading effects have been rarely assessed. Hence, this paper examines the cascading effects of floods on transportation infrastructure using data mining algorithms. First, 33 effects of flood events on transportation infrastructure have been identified based on data collected for multiple flood events in New York and New Jersey. Second, association rule mining analysis was implemented to identify the key co-occurrences between flooding and the different events. Third, network analysis was conducted to quantify the co-occurrences or key combinations among the events. Fourth, cluster analysis was used to group or prioritize the cascading effects and co-occurring events into highly connected clusters to identify the most critical ones based on two scenarios: (1) without consideration of co-occurrences (Scenario 1); and (2) with consideration of co-occurrences (Scenario 2). The findings provided insights that while some cascading impacts could be individually critical/frequent (under Scenario 1), other cascading impacts could also result due to a combination of different effects that might not be perceived to be critical on the individual level but rather become critical when combined with other cascading events (under Scenario 2). The outcomes of this paper demonstrate the importance of considering the co-occurrences between the events and cascading effects, rather than analyzing them in isolation. This study adds to the body of knowledge by offering an analytical approach that could be used to identify and prioritize critical cascading effects of flood events on the operations and performance of transportation infrastructures.

The rail running band is a mathematical representation describing the continuous strip-shaped spatial surface resulting from the rolling contact operation of train wheels on the rail surface, which establishes a direct mapping relationship with the wheel–rail interaction, and the nature of this interaction significantly influences the safety and comfort of train operations. Therefore, accurate detection of the running band is crucial for enhancing the safety and comfort of train travel. Traditional running band detection relies on manual inspection methods, utilizing a scale for measurements on the rail. However, this approach is characterized by high labor costs, slow detection speeds, and a lack of systematic data preservation. This paper proposes R2Bnet, a lightweight semantic segmentation algorithm that achieves pixel-level detection of rail running bands. R2Bnet is an enhanced encoder-decoder architecture built upon ShuttleNet. Different from ShuttleNet, R2Bnet optimizes the number of repetitive codecs in ShuttleNet and redesigns the encoder’s residual structure to match the unique characteristics of rail running bands, allowing the backbone network to effectively capture long-range dependencies. Furthermore, R2Bnet integrates an efficient channel attention mechanism to enhance focus on critical regions and optimize feature representations. The $F$-measure and mean intersection over union (mIOU) achieved by R2Bnet on 300 testing images were 98.47% and 0.9617, respectively. Notably, R2Bnet outperformed six state-of-the-art models for semantic segmentation and demonstrated a significant 39% improvement in speed compared with the average speed of the six networks provided.