Shape-based defect classification for Non Destructive Testing

The aim of this work is to classify the aerospace structure defects detected by eddy current non-destructive testing. The proposed method is based on the assumption that the defect is bound to the reaction of the probe coil impedance during the test. Impedance plane analysis is used to extract a feature vector from the shape of the coil impedance in the complex plane, through the use of some geometric parameters. Shape recognition is tested with three different machine-learning based classifiers: decision trees, neural networks and Naive Bayes. The performance of the proposed detection system are measured in terms of accuracy, sensitivity, specificity, precision and Matthews correlation coefficient. Several experiments are performed on dataset of eddy current signal samples for aircraft structures. The obtained results demonstrate the usefulness of our approach and the competiveness against existing descriptors.Comment: 5 pages, IEEE International Worksho

Rammed Earth Construction: A Proposal for a Statistical Quality Control in the Execution Process

Unlike other common contemporary construction materials such as concrete, mortars, or fired clay bricks, which are widely supported by international standards and regulations, building with rammed earth is barely regulated. Furthermore, its quality control is usually problematic, which regularly encourages the rejection of this technique. In the literature, many authors have suggested ways to safely build a rammed earth wall, but only a few of them have delved into its quality control before and during the construction process. This paper introduces a preliminary methodology and establishes unified criteria, based in a statistical analysis, for both the production and the quality control of this constructive technique in cases dealing with both samples and walls

Crack Localization and Detection in Small-Scale Reinforced Concrete Beams With Smart Technology

Reinforced concrete structures form the backbone of modern civil engineering, yet the emergence of cracks poses a significant challenge to their long-term integrity. The integration of smart sensors and data analytics further augments precision by enabling real-time data collection and analysis, allowing for early intervention. Continuous monitoring, facilitated by remote sensing and wireless communication, ensures a dynamic understanding of crack propagation. To validate the proposed approach, an experimental campaign was conducted using reinforced concrete beams. Three point bending tests were conducted on two small-scale reinforced concrete beams. Different configurations of SEC arrays were used on the two specimens to assess the capacity and limitation of the proposed approach. Results show that the sensing skin was capable of detecting and localizing cracks that formed in both specimens

Evaluation of non-destructive techniques for mechanical characterisation of earth-based mortars in masonry joints

In this paper, the use of non-destructive tests for the mechanical characterisation of earth-based mortars in masonry joints is discussed. Four testing methods, namely the penetrometer, Schmidt hammer, pendulum hammer and scratch test, originally developed for other types of mortar, are reviewed. The methods are applied to the earth-based mortars at the Wupatki Pueblo archaeological site, in Arizona, US. The outcomes of the experimental programme allowed to assess the reliability of the methods and to identify their limitations. Finally, the methods are compared in terms of six qualitative indicators, namely easy-of-use, consistency of results, range and granularity of results, respect towards cultural value, depth of investigation under the visible surface and versatility in application. Overall, the penetrometer test is recommended as the preferable method to characterise the mechanical performance of earth-based mortars.FCT - Fuel Cell Technologies Program(2022.09946

Imaging of Structural Timber Based on in Situ Radar and Ultrasonic Wave Measurements: A Review of the State-Of-The-Art

With the rapidly growing interest in using structural timber, a need exists to inspect and assess these structures using non-destructive testing (NDT). This review article summarizes NDT methods for wood inspection. After an overview of the most important NDT methods currently used, a detailed review of Ground Penetrating Radar (GPR) and Ultrasonic Testing (UST) is presented. These two techniques can be applied in situ and produce useful visual representations for quantitative assessments and damage detection. With its commercial availability and portability, GPR can help rapidly identify critical features such as moisture, voids, and metal connectors in wood structures. UST, which effectively detects deep cracks, delaminations, and variations in ultrasonic wave velocity related to moisture content, complements GPR’s capabilities. The non-destructive nature of both techniques preserves the structural integrity of timber, enabling thorough assessments without compromising integrity and durability. Techniques such as the Synthetic Aperture Focusing Technique (SAFT) and Total Focusing Method (TFM) allow for reconstructing images that an inspector can readily interpret for quantitative assessment. The development of new sensors, instruments, and analysis techniques has continued to improve the application of GPR and UST on wood. However, due to the hon-homogeneous anisotropic properties of this complex material, challenges remain to quantify defects and characterize inclusions reliably and accurately. By integrating advanced imaging algorithms that consider the material’s complex properties, combining measurements with simulations, and employing machine learning techniques, the implementation and application of GPR and UST imaging and damage detection for wood structures can be further advanced

Importance of Machine Vision Framework with Nondestructive Approach for Fruit Classification and Grading: A Review

Machine vision technology has gained significant importance in the agricultural industry, particularly in the non-destructive classification and grading of fruits. This paper presents a comprehensive review of the existing literature, highlighting the crucial role of machine vision in automating the fruit quality assessment process. The study encompasses various aspects, including image acquisition techniques, feature extraction methods, and classification algorithms. The analysis reveals the substantial progress made in the field, such as developing sophisticated hardware and software solutions, which have improved accuracy and efficiency in fruit grading. Furthermore, it discusses the challenges and limitations, such as dealing with variability in fruit appearance, handling different fruit types, and real-time processing. The identification of future research needs emphasizes the potential for enhancing machine vision frameworks through the integration of advanced technologies like deep learning and artificial intelligence.Additionally, it underscores the importance of addressing the specific needs of different fruit varieties and exploring the applicability of machine vision in real-world scenarios, such as fruit packaging and logistics. This review underscores the critical role of machine vision in non-destructive fruit classification and grading, with numerous opportunities for further research and innovation. As the agricultural industry continues to evolve, integrating machine vision technologies will be instrumental in improving fruit quality assessment, reducing food waste, and enhancing the overall efficiency of fruit processing and distribution