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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The Internet introduces new business choices for customer interaction. In this article we introduce two claims. Firstly, we will show that the way companies shape their customer interaction, and not their sector or size, determine the market segmentation. Secondly, Internet dynamics and its effect on customer interaction rebalances the companies’ marketing and sales function: the Internet shortens the time window for new market opportunities and makes everyone a salesman. Therefore, traditional marketing activities become more and more part of Sales. Corporate communication and branding become more vital.
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Masonry structures represent the highest proportion of building stock worldwide. Currently, the structural condition of such structures is predominantly manually inspected which is a laborious, costly and subjective process. With developments in computer vision, there is an opportunity to use digital images to automate the visual inspection process. The aim of this study is to examine deep learning techniques for crack detection on images from masonry walls. A dataset with photos from masonry structures is produced containing complex backgrounds and various crack types and sizes. Different deep learning networks are considered and by leveraging the effect of transfer learning crack detection on masonry surfaces is performed on patch level with 95.3% accuracy and on pixel level with 79.6% F1 score. This is the first implementation of deep learning for pixel-level crack segmentation on masonry surfaces. Codes, data and networks relevant to the herein study are available in: github.com/dimitrisdais/crack_detection_CNN_masonry.
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aving access to accurate and recent digital twins of infrastructure assets benefits the renovation, maintenance, condition monitoring, and construction planning of infrastructural projects. There are many cases where such a digital twin does not yet exist, such as for legacy structures. In order to create such a digital twin, a mobile laser scanner can be used to capture the geometric representation of the structure. With the aid of semantic segmentation, the scene can be decomposed into different object classes. This decomposition can then be used to retrieve cad models from a cad library to create an accurate digital twin. This study explores three deep-learning-based models for semantic segmentation of point clouds in a practical real-world setting: PointNet++, SuperPoint Graph, and Point Transformer. This study focuses on the use case of catenary arches of the Dutch railway system in collaboration with Strukton Rail, a major contractor for rail projects. A challenging, varied, high-resolution, and annotated dataset for evaluating point cloud segmentation models in railway settings is presented. The dataset contains 14 individually labelled classes and is the first of its kind to be made publicly available. A modified PointNet++ model achieved the best mean class Intersection over Union (IoU) of 71% for the semantic segmentation task on this new, diverse, and challenging dataset.
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Purpose: Little is known about how tourists’ eating habits change between everyday life and holidays. This study aims to identify market segments based on changes in food consumption and experiences of a sun-and-sea destination’s local food. The authors evaluate to what extent tourists consume local food and assess the contribution of local food experiences to the tourists’ overall experience. Design/methodology/approach: The target population was all tourists visiting the Algarve in the Summer 2018 and included both domestic and international sun-and-sea tourists. A sample of 378 valid questionnaires was collected. Data analysis included descriptive analysis, statistical tests and cluster analysis. Findings: Cluster analysis identified three segments: non-foodies, selective foodies and local gastronomy foodies. Results indicate that tourists change their eating habits during holidays, eating significantly more seafood and fish and less legumes, meat, fast food and cereals and their derivatives. International and domestic sun-and-sea tourists reported that eating local food contributes significantly to their overall tourism experience. Practical implications: Sun-and-sea destinations should promote the offer of local dishes, especially those that include locally produced fish and seafood, to improve the tourist experience, differentiate the destination and increase sustainability. Originality/value: The authors address three identified research gaps: a posteriori segmentation based on tourists’ food consumption behaviour; measurement of changes in eating practices between home and in a sun-and-sea destination; and assessment of the role of food experiences to overall tourism experience of tourists visiting a sun-and-sea destination.
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Creative tourism has recently emerged as an important area of tourism development, particularly in the Global North. In the Global South, studies of the profile of creative tourists and their motives for partaking in creative tourism are limited. This paper investigates creative tourism demand among South African millennials, analysing what motivates their participation and developing a descriptive consumer profile. CHAID analysis was used for segmentation, revealing a group with a high participation intention and a second group with a low probability of creative tourism participation. Creative tourism intentions were linked to knowledge acquisition, skills and escape motivations, and demographic characteristics including relationship status and gender. Respondents were more likely to participate in domestic rather than international creative tourism, indicating the potential for creative tourism development in South Africa. The findings could help managers and policymakers meet the needs of creative tourists, addressing shortfalls in product development, experience design and marketing.
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Private Labels have transformed from value purchases into powerful brands. This paper develops a framework based on the four strategic dimensions of brand breadth, positioning, segmentation, and relationship with the store brand that retailers can uniquely draw upon to organise their brand portfolios. It examines the case of German retailer Rewe that successfully organises its private label portfolio along these dimensions. This paper argues that maintaining multi-tiered and multi-segmented private label portfolios can be important tools for retailers enabling them to cover broader markets, fulfil current consumer needs, build brand equity, and strengthen customer loyalty.
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An illustrative non-technical review was published on Towards Data Science regarding our recent Journal paper “Automatic crack classification and segmentation on masonry surfaces using convolutional neural networks and transfer learning”.While new technologies have changed almost every aspect of our lives, the construction field seems to be struggling to catch up. Currently, the structural condition of a building is still predominantly manually inspected. In simple terms, even nowadays when a structure needs to be inspected for any damage, an engineer will manually check all the surfaces and take a bunch of photos while keeping notes of the position of any cracks. Then a few more hours need to be spent at the office to sort all the photos and notes trying to make a meaningful report out of it. Apparently this a laborious, costly, and subjective process. On top of that, safety concerns arise since there are parts of structures with access restrictions and difficult to reach. To give you an example, the Golden Gate Bridge needs to be periodically inspected. In other words, up to very recently there would be specially trained people who would climb across this picturesque structure and check every inch of it.
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