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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. Real-Time Road Crack Mapping Using an Optimized Convolutional Neural Network

   https://doi.org/10.1155/2019/2470735  

   Naddaf-Sh, M-Mahdi; Hosseini, SeyedSaeid; Zhang, Jing; Brake, Nicholas A.; Zargarzadeh, Hassan (September 2019, Complexity)

   Pavement surveying and distress mapping is completed by roadway authorities to quantify the topical and structural damage levels for strategic preventative or rehabilitative action. The failure to time the preventative or rehabilitative action and control distress propagation can lead to severe structural and financial loss of the asset requiring complete reconstruction. Continuous and computer-aided surveying measures not only can eliminate human error when analyzing, identifying, defining, and mapping pavement surface distresses, but also can provide a database of road damage patterns and their locations. The database can be used for timely road repairs to gain the maximum durability of the asphalt and the minimum cost of maintenance. This paper introduces an autonomous surveying scheme to collect, analyze, and map the image-based distress data in real time. A descriptive approach is considered for identifying cracks from collected images using a convolutional neural network (CNN) that classifies several types of cracks. Typically, CNN-based schemes require a relatively large processing power to detect desired objects in images in real time. However, the portability objective of this work requires to utilize low-weight processing units. To that end, the CNN training was optimized by the Bayesian optimization algorithm (BOA) to achieve the maximum accuracy and minimum processing time with minimum neural network layers. First, a database consisting of a diverse population of crack distress types such as longitudinal, transverse, and alligator cracks, photographed at multiple angles, was prepared. Then, the database was used to train a CNN whose hyperparameters were optimized using BOA. Finally, a heuristic algorithm is introduced to process the CNN’s output and produce the crack map. The performance of the classifier and mapping algorithm is examined against still images and videos captured by a drone from cracked pavement. In both instances, the proposed CNN was able to classify the cracks with 97% accuracy. The mapping algorithm is able to map a diverse population of surface cracks patterns in real time at the speed of 11.1 km per hour. 

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