Manual crack inspection is labor-intensive and impractical at scale, prompting a shift toward AI-based segmentation methods. We present a novel crack segmentation model that leverages the Segment Anything Model 2 (SAM 2) through transfer learning to detect cracks on masonry surfaces. Unlike prior approaches that rely on encoders pretrained for image classification, we fine-tune SAM 2, originally trained for segmentation tasks, by freezing its Hiera encoder and FPN neck, while adapting its prompt encoder, LoRA matrices, and mask decoder for the crack segmentation task. No prompt input is used during training to avoid detection overhead. Our aim is to increase robustness to noise and enhance generalizability across different surface types. This work demonstrates the potential of foundational segmentation models in enabling more reliable and field-ready AI-based crack detection tools.
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Municipalities play an important role in tackling city logistics related matters, having many instruments at hand. However, it is not self-evident that all municipalities use these instruments to their full potential. A method to measure city logistics performance of municipalities can help in creating awareness and guidance, to ultimately lead to a more sustainable environment for inhabitants and businesses. Subsequently, this research is focused on a maturity model as a tool to assess the maturity level of a municipality for its performance related city logistics process management. Various criteria for measuring city logistics performance are studied and based on that the model is populated through three focus fields (Technical, Social and Corporate, and Policy), branching out into six areas of development: Information and communication technology, urban logistics planning, Stakeholder communication, Public Private Partnerships, Subsidisation and incentivisation, and Regulations. The CL3M model was tested for three municipalities, namely, municipality of Utrecht, Den Bosch and Groningen. Through these maturity assessments it became evident the model required specificity complementary to the existing assessment interview, and thus a SWOT analysis should be added as a conclusion during the maturity assessment.
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In this mixed methods study, a moderated mediation model predicting effects of leader-member exchange (LMX) and organizational citizenship behaviors (OCB) on innovative work behaviors, with employability as a mediator, has been tested. Multi-source data from 487 pairs of employees and supervisors working in 151 small and medium-sized enterprises (SMEs) supported our hypothesized model. The results of structural equation modelling provide support for our model. In particular, the benefits of close relationships and high-quality exchanges between employee and supervisor (LMX), and fostering individual development as a result of employees’ OCB have an indirect effect on innovative work behaviors through positive effects on workers’ employability. Innovative work behaviors depend on employees’ knowledge, skills, and expertise. In other words, enhancing workers’ employability nurtures innovative work behaviors. In addition, we found a moderation effect of organizational politics on the relationship between employability and innovative work behaviors. Secondly, qualitative methods focusing on experiences of the antecedents and outcomes of employability were used to complement our quantitative results. All in all, this study has important consequences for managerial strategies and practices in SMEs and call for an awareness of the dysfunctional effect of perceived organizational politics.
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