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1. Solidification crack propagation and morphology dependence on processing parameters in AA6061 from ultra-high-speed x-ray visualization

   https://doi.org/https://doi.org/10.1016/j.addma.2021.101959  

   Kouraytem, Nadia; Chiang, Po-Ju; Jiang, Runbo; Kantzos, Christopher; Pauza, Joseph; Cunningham, Ross; Wu, Ziheng; Tang, Guannan; Parab, Niranjan; Zhao, Cang; et al (June 2021, Additive manufacturing)

   null (Ed.)

   Solidification or hot cracks are commonly observed defects in a number of metal alloys and may lead to deterioration of additively manufactured parts quality. In this study, ultra-high-speed x-ray radiography experiments enable the observation and characterization of bundles of hot-cracks that form in monobloc AA6061 substrate. The crack bundles are related to meltpool characteristics and pore formation. Crack propagation rate is also presented for the case of a crack that initiates from a pore. Two types of relevant pore formation are also described, namely keyhole porosity and crack-remelting porosity. The results of this study are expected to facilitate the validation of theoretical and numerical models of solidification cracking. 

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2. Passive atomic-scale optical sensors for mapping light flux in ultra-small cavities

   https://doi.org/10.1038/s41598-023-32010-y  

   Andričević, Pavao; Sellwood, Elaine L.; Eppes, Martha-Cary; Kook, Myungho; Jain, Mayank (December 2023, Scientific Reports)

   Abstract Understanding light propagation and attenuation in cavities is limited by lack of applicable light sensing technologies. Here we demonstrate the use of light-sensitive metastable states in wide bandgap aluminosilicates (feldspar) as passive optical sensors for high-resolution mapping of light flux. We develop non-destructive, infrared photoluminescence (IRPL) imaging of trapped electrons in cracks as thin as 50 µm width to determine the spatio-temporal evolution of light sensitive metastable states in response to light exposure. Modelling of these data yields estimates of relative light flux at different depths along the crack surfaces. Contrary to expectation, the measured light flux does not scale with the crack width, and it is independent of crack orientation suggesting the dominance of diffused light propagation within the cracks. This work paves way for understanding of how light attenuates in the minutest of cavities for applications in areas as diverse as geomorphology, biology/ecology and civil engineering. 

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3. Concrete Crack Pixel Classification Using an Encoder Decoder Based Deep Learning Architecture

   https://doi.org/10.1007/978-3-030-33720-9_46  

   Billah, Umme Hafsa; Tavakkoli, Alireza; La, Hung Manh (October 2019, The 14th International Symposium on Visual Computing (ISVC))

   Civil infrastructure inspection in hazardous areas such as underwater beams, bridge decks, etc., is a perilous task. In addition, other factors like labor intensity, time, etc. influence the inspection of infrastructures. Recent studies [11] represent that, an autonomous inspection of civil infrastructure can eradicate most of the problems stemming from manual inspection. In this paper, we address the problem of detecting cracks in the concrete surface. Most of the recent crack detection techniques use deep architecture. However, finding the exact location of crack efficiently has been a difficult problem recently. Therefore, a deep architecture is proposed in this paper, to identify the exact location of cracks. Our architecture labels each pixel as crack or non-crack, which eliminates the need for using any existing post-processing techniques in the current literature [5,11]. Moreover, acquiring enough data for learning is another challenge in concrete defect detection. According to previous studies, only 10% of an image contains edge pixels (in our case defected areas) [31]. We proposed a robust data augmentation technique to alleviate the need for collecting more crack image samples. The experimental results show that, with our method, significant accuracy can be obtained with very less sample of data. Our proposed method also outperforms the existing methods of concrete crack classification. 

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4. Deep Learning-Based Crack Detection Using Mask R-CNN Technique

   Tan, Chenjun; Uddin, Nasim; Mohammed, Yahya M. (August 2019, 9 th International Conference on Structural Health Monitoring of Intelligent Infrastructure)

   Cracks of civil infrastructures, including bridges, dams, roads, and skyscrapers, potentially reduce local stiffness and cause material discontinuities, so as to lose their designed functions and threaten public safety. This inevitable process signifier urgent maintenance issues. Early detection can take preventive measures to prevent damage and possible failure. With the increasing size of image data, machine/deep learning based method have become an important branch in detecting cracks from images. This study is to build an automatic crack detector using the state-of-the-art technique referred to as Mask Regional Convolution Neural Network (R-CNN), which is kind of deep learning. Mask R-CNN technique is a recently proposed algorithm not only for object detection and object localization but also for object instance segmentation of natural images. It is found that the built crack detector is able to perform highly effective and efficient automatic segmentation of a wide range of images of cracks. In addition, this proposed automatic detector could work on videos as well; indicating that this detector based on Mask R-CNN provides a robust and feasible ability on detecting cracks exist and their shapes in real time on-site. 

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5. Identification of prestress losses under early-age cracking in prefabricated prestressed slabs with complex cross-sections using long-gauge sensors

   https://doi.org/10.1177/14759217251362305  

   Liang, Yitian; Glisic, Branko (September 2025, Structural Health Monitoring)

   Off-site fabrication allows for efficient production and construction, while the prestressing process enhances the load-bearing capacity of structural components. Due to these advantages, the application of prestressed prefabricated structures increases significantly. However, various influences present at the early stage of fabrication, such as pouring conditions, friction with formworks, and early-age cracks, may cause differences between designed and real values of prestress forces, thereby affecting the bearing capacity and durability of prefabricated components. These differences are often reflected in the strain field. Therefore, it is of interest to monitor the performance of prefabricated structural components at early stages, that is., before, during, and after prestressing, by studying the internal strain distribution. This article aims at developing a methodology to identify prestress losses under early-age cracks in prefabricated prestressed beam-like concrete structures with a complex geometric cross-section and validating the application on a double-T slab of a five-floor garage at Princeton University. Embedded long-gauge strain sensors are used to monitor the strain at different locations. The focus of this article is on the analysis of the sensors embedded in the slab’s longitudinal direction (longitudinal sensors). The main challenges of this research include the non-linear strain distribution in the complex cross-section of the structures, which makes the Bernoulli hypothesis only partially valid, the uncertainties of geometric and mechanical parameters, and the effects of early-age crack opening on the evaluation of prestress forces. The developed methodology, based on the measurements of strain distribution before, during, and after prestressing, enabled the identification, that is, detection, localization, and quantification of prestress losses under early-age cracks in the prefabricated slab. The findings of this study have important implications for the design, construction, and maintenance of prefabricated structural components, enabling enhanced safety and durability throughout their service life. 

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