Bridge decks deteriorate faster compared to other bridge components, primarily influenced by traffic volume, while previous studies have examined the effect of bridge-wearing surfaces on deterioration, further understanding of the relationship between bridge performance and maintenance is needed for policy-making and planning purposes. In this study, we focus on nine influential variables to unravel the intricate connections among performance, deterioration, and maintenance of six distinct bridge-wearing surfaces: Monolithic Concrete, Gravel, Wood or Timber, Bituminous, Low Surface Concrete, and Other. Statistical analyses were employed to determine associations between variables and concepts, exploring similarities and differences across various wearing surface types. In particular, machine learning algorithms were utilized to model the maintenance considering the performance and deterioration of the six diverse wearing surfaces. This approach allowed for an examination of interactions between those variables and concepts. We further applied a well-performing prediction model (which achieved an accuracy of 0.86 and an AUC score of approximately 0.83) to obtain interpretable insights regarding bridge deck surfaces. Analysis with interpretable methods such as SHAP (Shapley additive explanation) and PDP (partial dependency plot) revealed that deterioration, deck age, deck area, and overall performance were the most influential variables among average daily traffic, average daily truck traffic, and the number of spans significantly influenced the maintenance of bridge deck condition with different wearing surfaces. Notably, a strong relationship between performance and maintenance was observed in specific wearing surface types, such as Monolithic Concrete and Wood or Timber, while Other surface types exhibited different patterns. These findings highlight the need for tailored approaches when assessing bridge health, considering the distinct characteristics of different bridge deck types.

Existing works typically operate on either image or text data from social media, but rarely work with both content types simultaneously. We propose and validate a technique for combining image and text data for predicting user engagement metrics based on social media data. We collected image and text data from 366,415 Facebook posts and a respective 1,305,375 million comments. The combined model achieves a 3.5x improvement in mean squared error when predicting share count and a 14% improvement for comment sentiment over single data type models. Finally, the study demonstrates the ability to pick more performant advertisement out of 16.7 billion pairs; the resulting machine learning models successfully predicts for a greater comment sentiment, comment count, and share count 93%, 65%, and 63% of the time.

In order to simulate virtual agents in the replica of a real facility across a long time span, a crowd simulation engine needs a list of agent arrival and destination locations and times that reflect those seen in the actual facility. Working together with a major metropolitan transportation authority, we propose a specification that can be used to procedurally generate this information. This specification is both uniquely compact and expressive—compact enough to mirror the mental model of building managers and expressive enough to handle the wide variety of crowds seen in real urban environments. We also propose a procedural algorithm for generating tens of thousands of high-level agent paths from this specification. This algorithm allows our specification to be used with traditional crowd simulation obstacle avoidance algorithms while still maintaining the realism required for the complex, real-world simulations of a transit facility. Our evaluation with industry professionals shows that our approach is intuitive and provides controls at the right level of detail to be used in large facilities (200,000+ people/day).

'Correction to: Model-Cart: A Machine Learning Meta-Framework with Explainability and Human-in-the-Loop' published in 'The 22nd International Conference on Information Technology-New Generations (ITNG 2025)'