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.

As organizations increasingly use open source software, they inevitably routinize open source project engagement to manage new open source risks. We explore the software package data exchange (SPDX) standard as a key open source product for routinizing the work that open source risk management entails. The development and subsequent adoption of SPDX raise the questions of how organizations participate in SPDX to routinize open source work to better integrate with their own open source risk management routines, how organizations make sense of SPDX when improving their own open source risk management routines, and how a community benefits from the experiential knowledge that organizational early adopters contribute back to it. To explore these questions, we conducted a single-case, multicomponent field study in which we connected with individuals who helped to develop and later employed SPDX in their own organizations. Our results contribute to explaining how organizations routinize open source project engagement by observing organizational commitments to routinize aspects of open source risk management through communal interactions, organizationally specific interpretations, and deployments.

License information for any non-trivial open-source software demonstrates the growing complexity of compliance management. Studies have shown that understanding open-source licenses is difficult. Prior research has not examined how developers would use interfaces displaying license text and its graphical models in studying a license. Consequently, a repeatable eye tracking-based methodology was developed to study user engagement when exploring open-source rights and obligations in a multi-modal fashion. Experiences of 10 participants in an exploratory case study design indicate that eye-tracking is feasible to quantitatively and qualitatively observe distinct interaction patterns in the use of license comprehension interfaces. A low correlation was observed between self-reported usability survey data and eye-tracking data. Conversely, a high correlation between eye-tracker and mouse data suggests the use of either in future studies. This paper provides a framework to conduct such studies as an alternative to surveys while offering interesting hypotheses for future studies.