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Abstract In the food industry, the increasing use of automatic processes in the production line is contributing to the higher probability of finding contaminants inside food packages. Detecting these contaminants before sending the products to market has become a critical necessity. This paper presents a pioneering real-time system for detecting contaminants within food and beverage products by integrating microwave (MW) sensing technology with machine learning (ML) tools. Considering the prevalence of water and oil as primary components in many food and beverage items, the proposed technique is applied to both media. The approach involves a thorough examination of the MW sensing system, from selecting appropriate frequency bands to characterizing the antenna in its near-field region. The process culminates in the collection of scattering parameters to create the datasets, followed by classification using the Support Vector Machine (SVM) learning algorithm. Binary and multiclass classifications are performed on two types of datasets, including those with complex numbers and amplitude data only. High accuracy is achieved for both water-based and oil-based products.

In the field of rice processing and cultivation, it is crucial to adopt efficient, rapid and user-friendly techniques to detect the flavor values of various rice varieties. The conventional methods for flavor value assessment mainly rely on chemical analysis and technical evaluation, which not only deplete the rice resources but also incur significant time and labor costs. In this study, hyperspectral imaging technology was utilized in combination with an improved Particle Swarm Optimization Support Vector Machine (PSO-SVM) algorithm, i.e., the Grid Iterative Search Particle Swarm Optimization Support Vector Machine (GISPSO-SVM) algorithm, introducing a new non-destructive technique to determine the flavor value of rice. The method captures the hyperspectral feature data of different rice varieties through image acquisition, preprocessing and feature extraction, and then uses these features to train a model using an optimized machine learning algorithm. The results show that the introduction of GIS algorithms in a PSO-optimized SVM is very effective and can improve the parameter finding ability. In terms of flavor value prediction accuracy, the Principal Component Analysis (PCA) combined with the GISPSO-SVM algorithm achieved 96% accuracy, which was higher than the 93% of the Competitive Adaptive Weighted Sampling (CARS) algorithm. And the introduction of the GIS algorithm in different feature selection can improve the accuracy to different degrees. This novel approach helps to evaluate the flavor values of new rice varieties non-destructively and provides a new perspective for future rice flavor value detection methods. [ABSTRACT FROM AUTHOR]

In the food industry, the increasing use of automatic processes in the production line is contributing to the higher probability of finding contaminants inside food packages. Detecting these contaminants before sending the products to market has become a critical necessity. This paper presents a pioneering real-time system for detecting contaminants within food and beverage products by integrating microwave (MW) sensing technology with machine learning (ML) tools. Considering the prevalence of water and oil as primary components in many food and beverage items, the proposed technique is applied to both media. The approach involves a thorough examination of the MW sensing system, from selecting appropriate frequency bands to characterizing the antenna in its near-field region. The process culminates in the collection of scattering parameters to create the datasets, followed by classification using the Support Vector Machine (SVM) learning algorithm. Binary and multiclass classifications are performed on two types of datasets, including those with complex numbers and amplitude data only. High accuracy is achieved for both water-based and oil-based products. [ABSTRACT FROM AUTHOR]

X-ray fluorescence (XRF) is a non-destructive technique that measures the elemental concentration of different materials, including human bone. Recently, it began to be applied to paleopathological studies due to the development of portable devices and their relative ease of use. However, the lack of uniform procedures hampers comparability and reproducibility. This paper aims to provide guidelines for an efficient and standardized evaluation of bone elemental composition with a portable XRF (pXRF) device. This technical note is based on the application of the Thermo Scientific Niton XL3t 900 GOLDD+. This work includes suggestions for the choice and preparation of human bone samples, both from archaeological context and documented collections, and methodological procedures in pXRF setup, such as choice of calibration, assessment of accuracy, and analysis run time. Additionally, recommendations for data validation and statistical analysis are also included. This technique has great potential in paleopathology since bone chemical variations may be associated with different pathological conditions, environmental contamination (e.g., lead), and/or administered treatments, such as mercury. Following an expected increase in the number of studies, it is essential to establish good practices that allow results from different researchers to be comparable. X-ray fluorescence is a non-destructive technique that measures small concentrations (ppm) of elements from magnesium (12Mg) through bismuth (83Bi). pXRF does not detect elements lighter than Mg, and its lower energy excitation penetrates less than other techniques. Other research groups should test these guidelines and comment on their usefulness and replicability. [ABSTRACT FROM AUTHOR]

Almost all countries are using railways to transport passengers and freight. Due to heavy load and environmental exposure, these rail sections are prone to damage. Thus, structural health monitoring of the rail section is an essential topic of research to ensure safety and predict the remaining useful life of the track. Consequently, the detection and localisation of damages/cracks in rail section are crucial. It also helps in taking corrective action in time. In this context, the acoustic emission (AE) technique is found to be a powerful non-destructive testing technique which has great potential to localise cracks in rail sections. Actually, the initiation or growth of a damage/crack in a structure emits AE waves, which may be captured using AE sensors mounted on different locations of the structure. The damage itself acts as an AE source. Subsequently, in this study, a novel approach is developed to localise the AE source in a rail section using the artificial neural network (ANN). The ANN model is based on the experimental crack signals captured through a single AE sensor by placing it at different regions of the rail section. For each position of the sensor, cracks are simulated using pencil lead break at different locations along the length and depth of the rail section. It is observed that the developed ANN model predicts the location of the AE source with better accuracy in comparison to the existing localisation methods. The minimum error is found to be less than 1%. Therefore, it can be concluded that the developed ANN model is a suitable option for predicting potential crack locations during real-time monitoring of rail sections. [ABSTRACT FROM AUTHOR]

In the field of rice processing and cultivation, it is crucial to adopt efficient, rapid and user-friendly techniques to detect the flavor values of various rice varieties. The conventional methods for flavor value assessment mainly rely on chemical analysis and technical evaluation, which not only deplete the rice resources but also incur significant time and labor costs. In this study, hyperspectral imaging technology was utilized in combination with an improved Particle Swarm Optimization Support Vector Machine (PSO-SVM) algorithm, i.e., the Grid Iterative Search Particle Swarm Optimization Support Vector Machine (GISPSO-SVM) algorithm, introducing a new non-destructive technique to determine the flavor value of rice. The method captures the hyperspectral feature data of different rice varieties through image acquisition, preprocessing and feature extraction, and then uses these features to train a model using an optimized machine learning algorithm. The results show that the introduction of GIS algorithms in a PSO-optimized SVM is very effective and can improve the parameter finding ability. In terms of flavor value prediction accuracy, the Principal Component Analysis (PCA) combined with the GISPSO-SVM algorithm achieved 96% accuracy, which was higher than the 93% of the Competitive Adaptive Weighted Sampling (CARS) algorithm. And the introduction of the GIS algorithm in different feature selection can improve the accuracy to different degrees. This novel approach helps to evaluate the flavor values of new rice varieties non-destructively and provides a new perspective for future rice flavor value detection methods.

The significance of on-site measurements for the thermal characterization of building components is widely recognized. Walls thermal performance can be identified through the widely used Heat-Flow Meter (HFM) method, which has been frequently applied in literature. However, only a few scientific works have examined the convergence criteria suggested by the ISO 9869–1. Starting from this, the aim of this work is evaluating the convergence criteria in a case study using the progressive average method. Thermal conductance (C-value) measurements were carried out in a small-scale building, placed in the external area of a building school site near Rome (Italy). The building is internally equipped with an electric heating system. Heat-flux sensor and surface temperature probes were applied to assess the C-value of the envelope. The different switching-on of the heater characterized by different power levels and the reaching of regular thermal conditions after initial transients were considered to evaluate the acquired data and their influence on the results. The known walls stratigraphy allowed to compute and compare theoretical and experimental C-values. Based on the examined data, it is possible to assert that the convergence conditions are very strict, mainly due to the third criterion.

(1) The assessment of the mechanical properties of old timber is essential for the proper maintenance of wooden structures. (2) Among the non-destructive properties, the dynamic modulus of elasticity is one of the best predictors of the mechanical characteristics of the members, but it requires the determination of wood density to be determined. (3) Thus, wood density was estimated by drilling resistance measurements, developing species-specific prediction equations for silver fir, chestnut and poplar. (4) The estimated density was combined with the stress wave velocity propagating longitudinally through the wooden piece, and the dynamic modulus of elasticity was calculated. (5) Medium-high coefficient determinations (R2 from 0.79 to 0.94) were found for density estimation, and medium coefficient determinations (R2 from 0.53 to 0.60) were found for the estimation of the static modulus of elasticity using the dynamic modulus. [ABSTRACT FROM AUTHOR]

This paper presents vibration-based methods for detecting and quantifying delaminations in composite structures. Three different delamination sizes were considered numerically and experimentally. Our approach combines and compares finite element method (FEM) numerical analysis and experimental measurements to find the location of structural delaminations and identify their severity. First, we noticed that a low-frequency modal study comparing pristine and damaged plates could not provide local damage information. Hence, a high frequency-modal analysis was performed to obtain the local damage information. The damaged mode shapes corresponding to resonance frequencies that cause significant local vibration (high amplitude) over the damaged regions were calculated numerically. The result shows the capability of damage identification (location and size of structural delaminations) within the high-frequency modal analysis. The numerical results were validated experimentally. The experiments include electromechanical impedance spectroscopy (EMIS) and scanning laser Doppler vibrometer (SLDV) techniques. The EMIS experiment was implemented to predict the resonance frequencies which cause significant local vibration over the delamination regions. The spectra of the electromechanical response resemble the vibration response of the structure and detect the new resonances created by the presence of the delamination. The test plate's experimental resonance peaks show a good match with the resonance frequencies of the numerical modal analysis. The SLDV experiments measured the local vibration over the investigated scanning areas. The operational vibration shapes corresponding to predicted resonance frequencies were created. These experimental mode shapes may visually measure the delaminations by observing intense local vibration over the delamination zones. Comparing the numerical mode shapes and the operational vibration shapes shows a good match between numerical and experimental results. [ABSTRACT FROM AUTHOR]

Strawberry fruits are particularly appreciated by consumers for their sweet taste related to their soluble solids content (SSC). However, strawberries are characterized by a short shelf-life and high susceptibility to tissue infection, mainly by Botrytis cinerea. The SSC determination of strawberry fruit through traditional destructive techniques has some limitations related to the applicability, timing, and number of samples. The aims of this study are (i) to verify if any relation between SSC and B. cinerea susceptibility in the fruits of five strawberry cultivars occurs and (ii) to determine the SSC of strawberry fruits through near infrared spectroscopy (NIR). Principal component analysis was used to search for spectral differences among the strawberry genotypes. The partial least squares regression technique was computed in order to predict the SSC of the fruits collected during two harvesting seasons. Moreover, variable selection methods were tested in order to improve the models and get better predictions. The results demonstrated that there was a high correlation between SSC and B. cinerea susceptibility (R2 up to 0.87). The SSC was predicted with a standard error of 0.84 °Brix and R2p 0.75 (for the best model), which indicated the possibility to use the models for screening applications. NIR spectroscopy represents an important non-destructive alternative and finds remarkable applications in the agro-food market. [ABSTRACT FROM AUTHOR]

Potential step voltammetry (PSV) was introduced in earlier works as an advantageous alternative to traditional methods for measuring corrosion rate in reinforced concrete. The present study aims to improve PSV to maximize its applicability in corrosion rate monitoring, that is, beyond the narrowly-defined steel–concrete systems in which was initially validated. It was therefore identified necessary to address the most suitable PSV pulse amplitudes to accurately obtain the Tafel lines and, therefore, corrosion rate in steel-mortar systems with well-differentiated ohmic drop. PSV findings were compared to reference methods, i.e. Tafel intersection and linear polarization resistance. As a novelty, we propose a procedure to improve the reliability of the PSV-determined Tafel lines, which is based on three protocols (P1, P2 and P3). P1 consists of a specific pulse sequence to accurately characterize the morphology of the polarization curve without disturbing the system. P2 consists of two short pulses for determining the ohmic drop compensation factor. Finally, P3 consists of a simple calculation procedure to accurately adjust the PSV pulse amplitudes (∆V) to the steel–concrete system assessed, thus obviating the need for preset values and, therefore, ensuring accurate corrosion rate results. The procedure proposed is intended to improve PSV with a view to its consolidation as a reliable tool for the unsupervised monitoring of real structures. [ABSTRACT FROM AUTHOR]

Crop health can be predicted based on various biochemical variables of crops, which include chlorophyll, phenol, carbohydrate, lipid, protein, hydrogen peroxide, and proline as these variables play a critical role in maintaining the intricate phytochemistry of crop plants. In-situ monitoring of the above- mentioned variables is very cumbersome and laborious, so it is atrociously needed to identify some alternatives to monitor these variables in crop plants. Assessing these variables using satellite data may be a good choice provided it has a high spatial and temporal resolution. Sentinel-2 satellite sensor contains VNIR/SWIR spectral region, two red-edge bands, and also has good spatial as well as temporal resolution so it may be the finest option. Precise information of the field is required for the development of a retrieval algorithm for which the drone data is used, as it is highly accurate ground truth reference data. In previous studies, the researchers have primarily focused on the monitoring of biophysical and morphological parameters of crop plants like leaf area index, plant height, and stomatal conductance using these spectral features. Because monitoring biochemical variables of crop plants using satellite derivatives is still a difficult undertaking for academics, just a few studies have been published. As a result, in this study, an attempt is made to establish a methodology for monitoring sugarcane crop biochemical characteristics utilising satellite-derived variables. Satellite derivatives, i.e., vegetation indices are extracted using Sentinel-2 data while biochemical variables of the crop (as mentioned above) are analyzed using leaf samples in the laboratory using optimized protocols. Subsequently, a Machine learning-based Gaussian process regression model is developed for all the biochemical variables using the different combinations of vegetation indices. The developed model showed promising results with R2 greater than 0.7 and normalized root mean square error (NRMSE) less than 0.2 thus holding good potential for effective monitoring the crop health condition remotely.

Summary: The excellent high‐durability features of ultra‐high performance fibre‐reinforced concrete (UHPFRC) have been verified in laboratory studies, but its performance under service conditions are being studied. Indeed, structural health monitoring (SHM) can be considered an efficient strategy to assess built structures in which concrete matrix performance differs from that those found when assessing laboratory samples (variable actions, cracking, etc.). This work presents INESSCOM, an automated corrosion rate monitoring system, as an innovative support to SHM strategy to monitor UHPFRC structures in terms of durability. Its innovation lies in its durable and multi‐parametric sensor designed to be embedded in multiple parts of a structure. The results from previous laboratory tests and those obtained during real‐time monitoring of an offshore UHPFRC raft are presented. Acceptable deviation of 20% was obtained in corrosion rate measurements with the advantageous reference‐electrode‐free cell of the sensor with respect to the classical three‐electrode cell. Furthermore, sensor provided accurate corrosion measurements in UHPFRC despite its extremely high electrical resistivity and large amount of steel fibres. After 17‐month monitoring of the UHPFRC raft, excellent performance was evidenced under service conditions with corrosion rate values always <0.1 μA/cm2. Conversely, corrosion rate reached 0.4 μA/cm2 in a conventional concrete specimen installed for comparison. Corrosion initiation and propagation stages were clearly defined through the corrosion‐penetration‐damage (μm) diagram obtained for the specimen. Present work positions INESSCOM as an innovative support to structural health monitoring strategy in UHPFRC structures. [ABSTRACT FROM AUTHOR]

Non-destructive testing (NDT) methods are a diagnostic tool for evaluating the risk of failure or the need for repair and renovation. In analyzing constructions of high historical value, destructive diagnostic methods should be avoided. This study is a comprehensive NDT investigation of the masonry tower topped with a steel dome, a remnant of the overhead telecommunications network from the end of the 19th century. Visual inspection and research made it possible to assess the degree of damage to the structure. Stress–strain state analysis showed the sufficient load-bearing capacity of the steel dome. In addition, calculations have shown that the masonry tower is subjected to significant horizontal forces causing structure cracks. [ABSTRACT FROM AUTHOR]

The detection of contaminants in food products after packaging by a non-invasive technique is a serious need for companies operating in the food industry. In recent years, many technologies have been investigated and developed to overcome the intrinsic drawbacks of the currently employed techniques, such as X-rays and metal detector, and to offer more appropriate solutions with respect to techniques developed in the academic domain in terms of acquisition speed, cost, and the penetration depth (infrared, hyperspectral imaging). A new method based on MW sensing is proposed to increase the degree of production quality. In this paper, we are going to present a novel approach from measurements setup to a binary classification of food products as contaminated or uncontaminated. The work focuses on combining MW sensing technology and ML tools such as MLP and SVM in a complete workflow that can operate in real time in a food production line. A very good performance accuracy that reached 99.8% is achieved using the non-linear SVM algorithm, while the accuracy of the performance of the MLP classifier reached 99.3%. [ABSTRACT FROM AUTHOR]

The presence of aggressive agents (such as chloride ions brought by seawater) in reinforced concrete structures is responsible for the corrosion of the steel rebars. A Structural Health Monitoring technology is developed as a new passive preventive method that would allow for the detection of and for the ability to follow the presence of chloride ions in the cover concrete of reinforced concrete. This technology, referenced as Functional Magnetic Material (FMM), consists on the measurement with an external interrogator of a Magnetic Observable (MO), partially shielded by a patch and corrodible by chloride ions. This paper presents the results of a parametric experimental study, allowing the validation of the concept of this technology, by highlighting the variation of the MO while considering the geometry and the corrosion level of the patch (based on its Relative Mass Loss—RML), as well as the distance between the samples and the interrogator. The results show that the MO of the FMM significantly varies with the increase in the RML of the patch. A 10%-RML for the patch is sufficient for detecting a variation of the MO of the FMM, and the relative variations of the MO are strongly dependent on the distance between the FMM and the magnetometer, as well as the patch's thickness. [ABSTRACT FROM AUTHOR]

In recent years, experimental tests related to building components through laboratory facilities have relatively matured. The techniques are based on one-dimensional heat transfer by creating a permanent temperature difference over a specimen to control heat fluxes. The three main methods are the Guarded Hot Box (GHB) method, the Calibrated Hot Box (CHB) method, and the Heat-Flow Meter method (HFM). The HFM method is the most widely applied technique for measuring on-site U-values of building components and several scientific works stressed the need for high temperature differences between the environments, suggesting 10 °C or 15 °C. However, temperature stability and high temperature gradients are difficult to obtain, especially for Mediterranean climatic conditions. Starting from this, an experimental study was conducted through a GHB apparatus, setting temperature differences from 2 °C to 20 °C between the hot and cold chambers. Heat flow measurements were performed to compute the thermal conductance of a specimen characterized by a known stratigraphy, thus highlighting the effect of the low thermal gradient on data acquired by the heat flow sensor. It was found that, even for low temperature differences (2 °C) maintained by ensuring stable thermal conditions, the experimental results are comparable with those obtained for higher and usual temperature differences (20 °C). [ABSTRACT FROM AUTHOR]

The Leeb hardness test is a non-destructive and portable technique that can be used both in the laboratory and in-field applications. The main purpose of this study is to predict the dynamic elastic constants of the igneous and sedimentary rocks using Leeb dynamic hardness testing. For this purpose, three vital topics have been investigated and analyzed. First, the relationships between ultrasonic wave velocities and dynamic elastic constants with the Leeb hardness were investigated. Thereafter, by determining the rock quality index (IQ) using microscopic studies and by analyzing the quality index-porosity plot, the variation of the Leeb hardness values was studied. Eventually, the longitudinal waveform in rock samples with different quality indexes and Leeb hardness were analyzed. To achieve these outputs, 33 samples of igneous and sedimentary rocks with a wide range of physical, mechanical, and textural features were collected and tested. The results of the analyses show that in both igneous and sedimentary rocks, the dynamic modulus of elasticity (Ed) has a significant correlation with the Leeb hardness. Generally, based on the microscopic studies, it was observed that the existence of the porosity in sedimentary rocks and intercrystalline and intracrystalline fissures in igneous rocks sharply reduce the Leeb hardness and thus lead to changes in the form of the longitudinal waves. [ABSTRACT FROM AUTHOR]

Corrosion of reinforcement in concrete structures remains a durability issue in structural engineering with the increasing cost of repair and maintenance. The mechanism and factors influencing reinforcement corrosion in concrete with various electrochemical monitoring techniques including non-destructive and destructive techniques have been considered. Also, the roles of sensors have been reviewed to determine the monitoring technique that proved most effective in determining corrosion parameters and more practicable for the assessment of concrete durability. Electrochemical impedance spectroscopy and linear polarization resistance techniques showed great performance in evaluating corrosion kinetics and corrosion rate respectively while gravimetric weight loss technique provided accurate measurements. No single monitoring technique showed to be the ultimate technique, and this calls for more research work in the development of more dynamic monitoring tools capable of considering all possible corrosion factors in the corrosion monitoring process.

(1) The assessment of the mechanical properties of old timber is essential for the proper maintenance of wooden structures. (2) Among the non-destructive properties, the dynamic modulus of elasticity is one of the best predictors of the mechanical characteristics of the members, but it requires the determination of wood density to be determined. (3) Thus, wood density was estimated by drilling resistance measurements, developing species-specific prediction equations for silver fir, chestnut and poplar. (4) The estimated density was combined with the stress wave velocity propagating longitudinally through the wooden piece, and the dynamic modulus of elasticity was calculated. (5) Medium-high coefficient determinations (R2 from 0.79 to 0.94) were found for density estimation, and medium coefficient determinations (R2 from 0.53 to 0.60) were found for the estimation of the static modulus of elasticity using the dynamic modulus.

The toolbox for material characterization has never been richer than today. Great progress with all kinds of particles and interaction methods provide access to nearly all properties of an object under study. However, a tomographic analysis of the subsurface region remains still a challenge today. In this regard, the Muon Induced X-ray Emission (MIXE) technique has seen rebirth fueled by the availability of high intensity muon beams. We report here a study conducted at the Paul Scherrer Institute (PSI). It demonstrates that the absence of any beam time-structure leads to low pile-up events and a high signal-to-noise ratio (SNR) with less than one hour acquisition time per sample or data point. This performance creates the perspective to open this technique to a wider audience for the routine investigation of non-destructive and depth-sensitive elemental compositions, for example in rare and precious samples. Using a hetero-structured sample of known elements and thicknesses, we successfully detected the characteristic muonic X-rays, emitted during the capture of a negative muon by an atom, and the gamma-rays resulting from the nuclear capture of the muon, characterizing the capabilities of MIXE at PSI. This sample emphasizes the quality of a continuous beam, and the exceptional SNR at high rates. Such sensitivity will enable totally new statistically intense aspects in the field of MIXE, e.g., elemental 3D-tomography and chemical analysis. Therefore, we are currently advancing our proof-of-concept experiments with the goal of creating a full fledged permanently operated user station to make MIXE available to the wider scientific community as well as industry. [ABSTRACT FROM AUTHOR]

In the present study, geoelectric properties of sand-bentonite mixes (used widely as barriers and buffer material) are evaluated using electric resistivity technique(ERT) combined with conventional geotechnical characterization methods. This paper discusses an experimental study to enhance the reliability of ERT for characterizing compacted sand-bentonite mixes for a wide range of applications. The electrical resistivity tests were conducted using fabricated small-scale soil resistivity box conforming to ASTM G57-06. on individual soil types (fine sand, medium sand, bentonite), as well as fine sand and medium sand mixed with different bentonite content. Based on observations, electrical resistivity/conductivity of sand-bentonite mixes are found different for fine and medium sand because of difference in their surface conductivity due to presence of bentonite, which is also represented through the developed correlations. The influence of parameters such as volumetric water content, dry-density, void-ratio, bentonite content and temperature on electrical resistivity values are also studied. [ABSTRACT FROM AUTHOR]

Strawberry fruits are particularly appreciated by consumers for their sweet taste related to their soluble solids content (SSC). However, strawberries are characterized by a short shelf-life and high susceptibility to tissue infection, mainly by Botrytis cinerea. The SSC determination of strawberry fruit through traditional destructive techniques has some limitations related to the applicability, timing, and number of samples. The aims of this study are (i) to verify if any relation between SSC and B. cinerea susceptibility in the fruits of five strawberry cultivars occurs and (ii) to determine the SSC of strawberry fruits through near infrared spectroscopy (NIR). Principal component analysis was used to search for spectral differences among the strawberry genotypes. The partial least squares regression technique was computed in order to predict the SSC of the fruits collected during two harvesting seasons. Moreover, variable selection methods were tested in order to improve the models and get better predictions. The results demonstrated that there was a high correlation between SSC and B. cinerea susceptibility (R2 up to 0.87). The SSC was predicted with a standard error of 0.84 °Brix and R2p 0.75 (for the best model), which indicated the possibility to use the models for screening applications. NIR spectroscopy represents an important non-destructive alternative and finds remarkable applications in the agro-food market.

Structural identification is one of the most important steps when dealing with historic buildings. Knowledge of the parameters, which define the mechanical properties of these kinds of structures, is fundamental in preparing interventions aimed at their restoration and strengthening, especially if they have suffered damage due to strong events. In particular, by using non-destructive techniques it is possible to estimate the mechanical characteristics of load-bearing structures without compromising the artistic value of the monumental buildings. In this paper, after recalling the main theoretical aspects, the use of elastic waves propagation through impact tests for the characterization of the masonry walls of a monumental building is described. The impact test allowed us to estimate the elastic characteristics of the homogeneous solid equivalent to masonry material. This confirms the great potential of the non-destructive diagnostics suitable for analyzing important structural parameters without affecting the preservation of historical masonry structures. [ABSTRACT FROM AUTHOR]

Monitoring of corrosion extent is critical in bridge operation and civil buildings. Reducing the effective cross-sectional area of components can redistribute the stress state and increase the true stress in the critical parts of civil structures. Unexpected rupture of bodies can occur as a result of their over-stressing. However, it is not possible to take relevant samples from these structures for a laboratory analysis during their lifetime. For this reason, this study investigates the potential of magnetic Barkhausen noise analysis for real-time corrosion extent monitoring. This study demonstrates that Barkhausen noise emission drops down along with an increasing degree of corrosion of steel S460MC. Progressive and remarkable decrease of Barkhausen noise and alteration of extracted features result from increasing surface roughness and superimposing influence of the increasing thickness of the near-surface layers containing mainly iron oxides. Furthermore, it was found that corrosion on the surface also alters the relation between tensile stress and Barkhausen noise within the interaction volume. [ABSTRACT FROM AUTHOR]

Non-destructive testing (NDT) methods are a diagnostic tool for evaluating the risk of failure or the need for repair and renovation. In analyzing constructions of high historical value, destructive diagnostic methods should be avoided. This study is a comprehensive NDT investigation of the masonry tower topped with a steel dome, a remnant of the overhead telecommunications network from the end of the 19th century. Visual inspection and research made it possible to assess the degree of damage to the structure. Stress–strain state analysis showed the sufficient load-bearing capacity of the steel dome. In addition, calculations have shown that the masonry tower is subjected to significant horizontal forces causing structure cracks.

Physical fouling characteristics on silicon carbide (SiC) membranes induced by various organic matter compounds vary depending on the presence of calcium ions (Ca2+). Both destructive techniques (morphological surface analysis) and non-destructive techniques (fouling properties monitoring) were used to determine the fouling mechanisms and behavior during the membrane filtration systems. Destructive analysis and a modified Hermia model were employed to assess the fouling mechanisms. Fouling behavior was also analyzed through non-destructive monitoring techniques including optical coherence tomography (OCT) and three-dimensional laser scanning confocal microscopy (3D-LSM). At concentrations of 10, 30, and 100 mg/L without Ca2+, the flux decreased by 57–95% for humic acid (HA) and anionic polyacrylamide (APAM). APAM exhibited a notable removal rate of up to 56% without Ca2+. At concentration of 10, 30, and 100 mg/L in the absence of Ca2+, the flux decreased by 6–8% for sodium alginate (SA). However, the addition of Ca2+ led to a reduction in the flux for SA by up to 91% and resulted in a removal rate of 40%. Furthermore, addition of Ca2+ led to an alteration of the fouling characteristics of HA and SA. In the case of HA, higher concentrations resulted in elevated thickness and roughness with correlation coefficients of 0.991 and 0.992, respectively. For SA, increased SA concentration led to a thicker (correlation coefficient of 0.999) but smoother surfaces (correlation coefficients of 0.502). Monitoring of these physical characteristics of the fouling layer through non-destructive analysis is crucial for effective fouling management, optimization of the system performance and extending the lifespan of the membrane. By continuously assessing the fouling layer thickness and surface roughness, we expect to be able to provide insights on the fouling behavior, identify trends, that can help scientists and engineers to make informed decisions regarding fouling control strategies in future. [Display omitted] • The presence of Ca2+ lead to change in fouling behavior for HA, SA and APAM. • OCT and 3D-LSM enable analysis of fouling behavior using non-destructive methods. • Non-destructive monitoring provides insights of membrane fouling and behavior. • Non-destructive monitoring predicts the membrane cleaning and fouling control. [ABSTRACT FROM AUTHOR]

Origin identification is important for safeguarding food quality, as the content of various ingredients in a product's raw material depends on its origin. By combining near-infrared hyperspectral imaging (NIR-HSI) with machine learning, the classification model for lily of six various origins has been developed for origin traceability. Based on four algorithms, decision tree (DT), linear discriminant analysis (LDA), k-nearest neighbor (KNN) and support vector machine (SVM), all full-spectrum prediction models have an origin identification accuracy of over 90.0%. Meanwhile, the prediction models were further simplified with guaranteed accuracy by using three feature selection methods to improve the efficiency of detection in practical applications. The results show that the prediction accuracy of the model can reach 100% by the RF feature selection method. The LDA models show excellent discrimination accuracy with 100% in the origin identification. This study demonstrates the feasibility of using NIR-HSI combined with machine learning in the origin identification of lily. It provides a fast, accurate, non-destructive and comprehensive detection approach for the traceability and quality assessment of lily. In the future, the combination of NIR-HSI and algorithms promises to be a potential method for origin identification analysis, classification identification and quality evaluation in the food industry. [Display omitted] • NIR-HSI combined with machine learning to identify the origin of lily. • An accurate, rapid and non-destructive detection method for origin identification. • The LDA model demonstrates superior performance in origin identification. • Optimal feature selection model achieves 100% prediction accuracy. • The model based on repetitively selected wavelengths has an accuracy of over 90%. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of evaluating the air permeability of structural concrete in situ, using non-destructive techniques. Specifically, it aims to reconcile air permeability measurements made in the field with those made in the laboratory. Permeability measurements in the laboratory and in the field use both under pressure and in vacuum techniques in steady or unsteady states. Differences between the methods may arise from pressure or flow regime variations, or flow direction control. The study proposes a methodology for evaluating air permeability in the field using an existing vacuum technique and an original numerical inverse analysis. An experimental programme using different protocols of permeability measurement with vacuum techniques analysed by numerical modelling is used to understand the differences observed experimentally. [ABSTRACT FROM AUTHOR]

Pulsed thermography is an active non-destructive technique which uses optical excitation source to stimulate heating of the object under investigation. This work is devoted to the simulation of the pulsed thermography method in a steel plate with a ceramic coating containing artificial defects of various depths and sizes. The simulation has been carried out on the base of a model which takes into account complex heat exchange of the sample with the surrounding by convection, conduction and radiation. Comparison of the temperature contrast with the experimental data has shown that the results are in a good qualitative and quantitative agreement in all stages of the cooling process. Due to the fact that the temperature contrast is often susceptible to surface noise of various nature the Kalman-based signal processing technique was developed. The comparative analysis has shown that the proposed filtration technique provides a better signal-to-noise ratio in comparison to the considered well-known techniques of signal reconstruction when proper calibration of the filtration parameters is carried out

The presence of aggressive agents (such as chloride ions brought by seawater) in reinforced concrete structures is responsible for the corrosion of the steel rebars. A Structural Health Monitoring technology is developed as a new passive preventive method that would allow for the detection of and for the ability to follow the presence of chloride ions in the cover concrete of reinforced concrete. This technology, referenced as Functional Magnetic Material (FMM), consists on the measurement with an external interrogator of a Magnetic Observable (MO), partially shielded by a patch and corrodible by chloride ions. This paper presents the results of a parametric experimental study, allowing the validation of the concept of this technology, by highlighting the variation of the MO while considering the geometry and the corrosion level of the patch (based on its Relative Mass Loss—RML), as well as the distance between the samples and the interrogator. The results show that the MO of the FMM significantly varies with the increase in the RML of the patch. A 10%-RML for the patch is sufficient for detecting a variation of the MO of the FMM, and the relative variations of the MO are strongly dependent on the distance between the FMM and the magnetometer, as well as the patch’s thickness.

In recent years, experimental tests related to building components through laboratory facilities have relatively matured. The techniques are based on one-dimensional heat transfer by creating a permanent temperature difference over a specimen to control heat fluxes. The three main methods are the Guarded Hot Box (GHB) method, the Calibrated Hot Box (CHB) method, and the Heat-Flow Meter method (HFM). The HFM method is the most widely applied technique for measuring on-site U-values of building components and several scientific works stressed the need for high temperature differences between the environments, suggesting 10 °C or 15 °C. However, temperature stability and high temperature gradients are difficult to obtain, especially for Mediterranean climatic conditions. Starting from this, an experimental study was conducted through a GHB apparatus, setting temperature differences from 2 °C to 20 °C between the hot and cold chambers. Heat flow measurements were performed to compute the thermal conductance of a specimen characterized by a known stratigraphy, thus highlighting the effect of the low thermal gradient on data acquired by the heat flow sensor. It was found that, even for low temperature differences (2 °C) maintained by ensuring stable thermal conditions, the experimental results are comparable with those obtained for higher and usual temperature differences (20 °C).

Micro-X-ray fluorescence (μ-XRF) spectroscopy has been used for dendrochemistry as a high spatial resolution, multi-elemental and non-destructive technique. Furthermore, micro-X-ray absorption near edge structure (μ-XANES) spectroscopy has a potential application in environmental studies by adding speciation information under in-situ conditions. Both, μ-XRF and μ-XANES, techniques were used to assess the inter-ring and intra-ring chemical content variations of Avicennia schaueria mangrove tree from Southeastern Brazil. Ca, Cl, K, P, and S were the major chemical elements distributed in almost all tree-rings. Ca was the most abundant element and showed a decreasing trend from heartwood to sapwood tree-rings. Calcium also showed a characteristic distribution pattern along the growth layer, acting as a chemical marker of growth layers. Ca chemical species were distinct in sapwood (calcium oxalate and carbonate-rich) and heartwood tree-rings (calcium sulfate-rich), showing different physiological Ca roles on tree performance. The synchrotron radiation-based techniques at micrometer scale provided information on the distribution and speciation of chemical elements within and between tree-rings. We also present a µ-XANES procedure for data collection and processing for dendrochemical studies. Altogether, we opened opportunities for further studies aiming to understand which environmental or physiological variables are responsible for tree-ring chemical speciation and distribution changes in tree species.

Soil nailing system is widely used along highways in Brazil. Non-destructive techniques have been used to estimate the length of installed soil nails. Sonic Echo and Impulse Response techniques evaluate the bar length using sonic wave propagation. The methods are similar and consists in generate an impact with a hammer. Results may be affected by this non-standard procedure. This paper aims to propose an automated sound wave generation in order to carried out tests quickly, with reproducibility and without operator dependence. An electromagnetic launching system was developed, which uses a projectile in place of the hammer, and the electromagnetic force instead of human force. The sound data was received by a microphone and sent to the computer by a microcontroller, making the system compact and portable. Projectile output speed optimization and impact reproducibility were analyzed. Nails length were obtained by both time and frequency domain data analysis. Results showed high reproducibility under the same initial condition. Propagation velocity presented a relative error less than 1% for steel bar with length of 1 m. [ABSTRACT FROM AUTHOR]

The present work shows the application of a non-destructive technique as a method for the identification of internal defects in marupa trees (Simarouba amara Aubl.) Located in forest plantations in the Ucayali region. This technique contemplated the use of an acoustic tomograph (ArborSonic 3D) that allowed obtaining tomograms that made it possible to analyze the cross section of the evaluated trees, showing the internal state of their shafts taken at different heights (0 cm, 40 cm, 90 cm, 130 cm and 160 cm). The results obtained made it possible to identify that the marupa trees did not present significant internal defects along their stems and damage was only reported at the base level at 0 cm from the ground. These results show the potential of using this non-destructive technique in forest plantations, thus contributing to the need to characterize standing wood, especially for commercial plantations. [ABSTRACT FROM AUTHOR]

The toolbox for material characterization has never been richer than today. Great progress with all kinds of particles and interaction methods provide access to nearly all properties of an object under study. However, a tomographic analysis of the subsurface region remains still a challenge today. In this regard, the Muon Induced X-ray Emission (MIXE) technique has seen rebirth fueled by the availability of high intensity muon beams. We report here a study conducted at the Paul Scherrer Institute (PSI). It demonstrates that the absence of any beam time-structure leads to low pile-up events and a high signal-to-noise ratio (SNR) with less than one hour acquisition time per sample or data point. This performance creates the perspective to open this technique to a wider audience for the routine investigation of non-destructive and depth-sensitive elemental compositions, for example in rare and precious samples. Using a hetero-structured sample of known elements and thicknesses, we successfully detected the characteristic muonic X-rays, emitted during the capture of a negative muon by an atom, and the gamma-rays resulting from the nuclear capture of the muon, characterizing the capabilities of MIXE at PSI. This sample emphasizes the quality of a continuous beam, and the exceptional SNR at high rates. Such sensitivity will enable totally new statistically intense aspects in the field of MIXE, e.g., elemental 3D-tomography and chemical analysis. Therefore, we are currently advancing our proof-of-concept experiments with the goal of creating a full fledged permanently operated user station to make MIXE available to the wider scientific community as well as industry.

Ultrasonic vibrothermography is an effective and non-destructive method, which can be used for a quick detection of coating defects on large surfaces. Mechanical excitation of a layered structure induces inelastic deformation and, as a consequence, energy dissipation in the defective area and local heating. In this work, a three-dimensional numerical simulation of this process is applied to the bi-metallic layered structure with an edge crack. Two models of energy dissipation are considered (hysteretic damping model and visco-elastic Maxwell󓔒s model) for simulation of energy dissipation in a crack tip area under ultrasonic loading. The models allow us to study the effects of the loading frequency, loading direction and location of the coating defects on the heat dissipation and propose optimal regimes for the ultrasonic vibrothermography of bi-metallic layered structures.

Soil organic matter (SOM) is a major index of soil quality assessment because it is one of the key soil properties controlling nutrient budgets in agricultural production systems. The aim of the in situ near-infrared spectroscopy (NIRS) for SOM prediction in paddy area is evaluation of the potential of SOM and prediction of other soil properties. There are keys for soil fertility and soil quality assessments. A spectral reflectance of 130 soil samples was collected by field spectroradiometer in a region of near-infrared. Spectral reflectance collections were processed by the first derivative transformation with the Savitsky-Golay algorithms. Partial least square regression method was used to develop a calibration model between soil properties and spectral reflectance, which was used for prediction and validation processes. Finally, the results of this study demonstrate that NIRS is an effective method that can be used to predict SOM (R2 = 0.73, RPD (ratio of performance to deviation) = 1.82) and total nitrogen (R2 = 0.72, RPD = 1.78). Therefore, NIRS is a potential tool for soil properties predictions. The use of these techniques will facilitate the implementation of soil management with a decreasing cost and time of soil study in a large scale. However, further works are necessary to develop more accurate soil properties prediction and to apply this method to other areas.

Characterizing material strength in-situ for existing structures poses a major problem for a range of civil engineering applications including structural modelling for tunnelling-vulnerability assessment and pre-earthquake resiliency evaluation, especially for unreinforced masonry buildings. Present methods require expensive testing equipment often requiring access to the structure and possible destruction of historic material. This article introduces spectrometry as a non-destructive means for identifying different brick clays and their firing levels, both of which influence the masonry's mechanical behavior. The experiments herein considered bricks of 2 clay groups (red and yellow) fired at 3 kiln temperatures (700ºC, 950ºC, 1,060ºC). Samples were examined via spectrometry within the short-wave infrared range (1,300–2,200 nm). A Partial Least Square Discriminant Analysis (PLSDA) model was calibrated using 96 samples and tested on a set of 48 samples, resulting in a 98% success rate in classification of the two clay types and a 100% success rate for classification among the 3 firing levels. The ability of the PLSDA model to reliably distinguish well-fired bricks from other samples, irrespective of raw material configuration, shows the potential to use this approach as a new, non-destructive means for in-situ assessment of brick for architectural conservation, as well as for safety and serviceability assessments. [ABSTRACT FROM AUTHOR]

The pH of the concrete pore solution plays a vital role in protecting the reinforcing steel from corrosion. Here, we present results from embeddable pH sensors that permit the continuous, in-situ monitoring of the pH in the concrete pore solution. These are potentiometric sensors, based on thermally-oxidized iridium/iridium oxide (IrOx) electrodes. We propose an iterative calculation algorithm taking into account diffusion potentials arising from pH changes, thus permitting the reliable, non-destructive determination of the pore solution pH over time. This calculation algorithm forms an essential part of the method using IrOx electrodes. Mortar samples were exposed to accelerated carbonation and the pH was monitored at different depths over time. Comparative tests were also performed using thymolphthalein pH-indicator. The results from the pH sensors give insight in the carbonation process, and can, in contrast to thermodynamic modelling and titration experiments, give insight in kinetic processes such as transport and phases transformations. Additionally, it was found that the front at which the pH is decreased from initially 13-14 down to 12.5 can be significantly ahead of the common carbonation front corresponding to pH 9-10. This has major implications for laboratory testing and engineering practice.