Search Results (116)

Non-destructive and destructive test methods are applied to wood to characterize this heterogeneous natural material. There have been multiple studies to characterize and investigate the change after the treatment (impregnation, thermal modification, etc.). In terms of thermal modification, there have been few studies on thermo–vacuum treatment, which is performed in a continuous vacuum atmosphere. With this method, the objective was to attempt to reduce the strength decrease after the thermal treatment. The aim of this study was to estimate the flexural properties of thermo–vacuum-treated Scots pine wood with destructive and acoustic-based non-destructive test methods. Wood was treated at 180 °C and 360 mm Hg. Both treated and untreated samples were cut into small specimens to ensure they were free of defects and were tested with acoustic-based non-destructive (longitudinal vibration and stress wave) and static bending test methods. The results show a decrease in equilibrium moisture content, demonstrating the efficiency of the treatment. When the results were compared with destructive test results, higher correlations (R² > 0.858) were found when estimating the modulus of elasticity (MOE) for both the untreated and treated wood, while lower correlations (R² < 0.440) were found for the modulus of rupture (MOR). When an additional equation was developed, stronger correlations (R² > 0.8986) were obtained between the non-destructive and destructive test results. Full article

The accurate and non-invasive measurement of moisture content in wood is essential for the preservation of historical and artistic artifacts. This study presents the calibration of a planar Microwave Planar Capacitive Coupled Ring Resonator (MPCCRR) designed to indirectly and non-destructively assess the water content in wood samples. The method relies on analyzing shifts in the resonant frequencies and variations in the transmission parameter |S21| resulting from changes in the material’s dielectric permittivity. After preliminary characterization via parametric simulations (εr = 1–10) and validation with low-permittivity reference materials, the sensor was tested on three wood species (poplar, fir, beech), including measurements at two sensor positions and with different grain orientations. The results demonstrate a monotonic, repeatable response to increasing moisture content with frequency shifts up to ≈220 MHz and normalized sensitivities ranging from 3 to 9 MHz/% water content, depending on species and measurement position. Position 2 showed the greatest sensitivity due to stronger field–sample interaction, while Position 1 provided a quasi-isotropic response with excellent repeatability. Linear regression analyses revealed good correlations between the frequency shifts and the gravimetric water content (R² ≥ 0.85). The MPCCRR sensor therefore proves to be a promising tool for the non-invasive monitoring of wood moisture, which is particularly suitable for the low-moisture range encountered in cultural heritage conservation, with an estimated moisture uncertainty of 0.12–0.35% under controlled laboratory conditions. Full article

Wood vinegar (WV), a by-product of biomass pyrolysis rich in organic acids and phenolic compounds, has gained increasing attention as a sustainable input for crop production, mainly through foliar application. However, its high content of volatile organic compounds (VOCs) suggests that WV may (also) interact with plants through the gaseous phase, a pathway that has so far been overlooked. This study tested the hypothesis that WV can modulate plant physiological performance, metabolic status, and nutrient accumulation not only via direct foliar contact but also through exposure to WV-derived VOCs. Lettuce (Lactuca sativa L.) was used as a model crop and grown under controlled environmental conditions. Plants were subjected to weekly treatments consisting of either foliar spraying with a 0.2% (v/v) WV solution or exposure to VOCs released from the same solution in a sealed chamber, without direct contact between the liquid and plant tissues, and were compared with untreated controls. Notably, plants exposed exclusively to WV-derived VOCs showed responses similar to those observed following foliar application. Both treatments significantly increased fresh weight, the content of chlorophyll, total polyphenols and the accumulation of key macro- and micronutrients, including Ca, K, P, S, and Zn. For both treatments, the efficiency of photosystem II remained stable, indicating the absence of photochemical stress, while stomatal conductance, transpiration rate, intercellular CO₂ concentration, and net photosynthetic rate were markedly reduced, suggesting a regulated stomatal response. Physiological, biochemical, and mineral parameters were assessed using non-destructive optical techniques, gas exchange measurements, spectrophotometric assays, and X-ray fluorescence analysis. These findings indicate that exposure to the volatile fraction released from WV under the exposure conditions adopted in this study can elicit biostimulant-like responses comparable to those observed after foliar application. Full article

Thermally modified wood is primarily used in exterior applications due to its enhanced resistance to biotic degradation. However, reduced mechanical performance limits its structural use. This study investigates the structural potential of high-temperature-treated European beech timber (Fagus sylvatica, L.) and evaluates its mechanical properties and grading models for structural design. Timber from 32 beech logs was air-dried and divided into untreated (NoTMW) and thermally modified (TMW) groups. Thermal modification was carried out commercially in an oxidizing atmosphere at 190 °C. All specimens were visually graded according to DIN 4074-5 and assessed using acoustic non-destructive methods before testing in four-point bending following EN 408. Modulus of elasticity (MOE), modulus of rupture (MOR), and density were determined, and characteristic values were calculated according to EN 384. On average, TMW exhibited a 17% reduction in bending strength compared to untreated wood, while both static and dynamic MOE were not significantly affected. The multiple regression model only slightly improved bending strength prediction compared with single linear regression based on global modulus, as the R²-value increased from 17% to 19%. The prediction of stiffness of thermally treated beech timber was greatly improved by combining local and acoustic moduli, explaining 76% of the total variation. Full article

The conservation of heritage buildings requires non-invasive tools that can predict material performance while maintaining historical integrity and structural safety. This study introduces a multivariate statistical framework that integrates regression analysis, Principal Component Analysis (PCA), and Hierarchical Cluster Analysis (HCA) to classify seven traditional materials adobe, lime mortar, limestone, sandstone, marble, volcanic stone, and wood based on their mechanical, thermal, and moisture-related properties. This study aims to develop a validated multivariate framework for classifying traditional heritage materials based on their mechanical, thermal, and moisture-related properties to support sustainable restoration and retrofit design for classifying traditional materials based on their mechanical, thermal, and moisture-related properties to support sustainable restoration and retrofit design. Unlike prior research limited to single-material assessments, this study standardizes and analyzes data from fourteen peer-reviewed sources using regression models, Principal Component Analysis (PCA), and Hierarchical Cluster Analysis (HCA), Principal Component Analysis (PCA), and Hierarchical Cluster Analysis (HCA), complemented by pilot non-destructive validation tests on lime mortar, sandstone, limestone, and marble. The framework compiles and standardizes datasets from fourteen peer-reviewed sources into a unified predictive model. The framework was validated through pilot testing using non-invasive methods (density, ultrasonic pulse velocity, rebound hardness), which confirmed the statistical predictions of robustness versus moisture vulnerability. Advanced cluster solutions identified conservation-relevant subgroups, enabling engineers to distinguish between moisture-sensitive low-density materials and durable lithic stones, with direct implications for sustainable restoration and retrofit practices. The originality of this study lies in transforming fragmented datasets into a validated, decision-support tool that can be embedded into Historic Building Information Modeling (HBIM) platforms for predictive diagnostics, compatibility assessment, and energy-efficient retrofit planning in heritage structures. This study provides the first validated cross-material statistical framework linking traditional conservation materials with predictive digital-modeling tools. This framework further demonstrates that the application of regression, Principal Component Analysis (PCA), and Hierarchical Cluster Analysis (HCA) enables quantitative prediction of material performance through non-destructive parameters. The integration of these techniques provides interpretive value beyond descriptive classification, facilitating preventive diagnostics, compatibility assessments, and energy-oriented retrofit planning within HBIM systems. Full article

The objective of the study is to develop a methodology for strength characterization by vibrational analysis of a batch of wood from building demolitions with a view to its reuse. This approach is part of an experimental deconstruction/reconstruction project located in the Vosges “département” of France and led by the social housing landlord, VOSGELIS. The main constraint related to this intention of reuse is the obtention of the strength class of the elements, which is not recorded in the standards. The comparative study of different non-destructive technologies has shown that the values of the longitudinal dynamic modulus of elasticity obtained by the vibrational method are closer on average (15%) to the actual value obtained by the bending tests than those obtained by the ultrasonic method (35%). A portable measuring bench suitable for the deconstruction site was also developed during the study for the utilization of the vibrational method. The values of the dynamic modules of elasticity obtained on this bench are close, on average, to the values of the modulus of elasticity obtained by bending tests executed on a test slab (13%). This study made it possible to extend the use of the NF EN 14081-2+A1 standard to woods from building demolitions. However, this standard needs to be adapted for the classification of that typology of wood, with a reference batch constituted of 15 to 20 samples. Full article

Eucalyptus nitens (H. Deane & Maiden) Maiden is a widely planted hardwood species in Australia, particularly in Tasmania, where it occupies approximately 168,000 ha. Although primarily managed for pulp production, the species is attracting interest for sawn and engineered wood applications. Previous evaluations of its properties have relied on destructive testing; however, non-destructive evaluation (NDE) techniques provide a viable alternative for industrial applications. This study examined the use of acoustic-based NDE to assess plantation-grown E. nitens logs sourced from two Tasmanian harvesting sites, focusing on the relationship between dynamic modulus of elasticity (DMOE), static MOE, and modulus of rupture (MOR) across radial positions from pith to bark. The results indicated that core samples exhibited stronger correlations with static MOE and MOR compared with middle and outer samples. DMOE consistently overestimated static MOE by 10.81% and 24.66% at the two sites, with variation evident across radial positions. These findings demonstrate the effectiveness of acoustic NDE for evaluating wood stiffness, highlighting the importance of sample location within logs. Full article

Traditionally, mechanical bending tests are used to measure the stiffness of lumber, which is generally represented by the static modulus of elasticity (MoE). However, it is desirable to measure the stiffness of wood before it is processed into lumber. Acoustic nondestructive testing techniques are therefore the main techniques used by the wood industry to estimate the dynamic MoE of wood. The acoustic resonance technique is employed for measuring the MoE in felled logs and lumber. In contrast, the acoustic time-of-flight (ToF) technique is traditionally used for MoE measurements on standing trees and seedlings. However, the ToF technique overestimates stiffness compared to both resonance and static bending tests (considered the gold standard). In this work, a guided wave technique is used to measure the stiffness of wooden rods. This work is the first to compare the MoE values obtained using static bending tests (gold standard) with those obtained using acoustic resonance, ToF, and guided wave methods. Measurements were performed on 16 mm diameter radiata pine wooden rods. For comparison, measurements were also performed on acetal, aluminium, and steel rods of similar dimensions. The findings show that stiffness measurements obtained using the proposed guided wave method are more accurate than those obtained using the traditional ToF method and closely match those of the resonance method across all materials. The measurements from the ToF method were overestimated compared to resonance, guided wave, and static bending methods. The findings show the potential for the guided wave method to be used as an alternative method to provide more accurate stiffness measurements in juvenile trees/seedlings compared with the traditional ToF technique. Full article

This study proposes a novel LK-BP-AFPSO model for the nondestructive evaluation of wood mechanical properties, combining a backpropagation neural network (BP) with adaptive fractional-order particle swarm optimization (AFPSO) and Liang–Kleeman (LK) information flow theory. The model accurately predicts four key mechanical properties—longitudinal tensile strength (SPG), modulus of elasticity (MOE), bending strength (MOR), and longitudinal compressive strength (CSP)—using only nondestructive physical features. Tested across diverse wood types (fast-growing YKS, red-heart CSH/XXH, and iron-heart XXT), the framework demonstrates strong generalizability, achieving an average prediction accuracy (R²) of 0.986 and reducing mean absolute error (MAE) by 23.7% compared to conventional methods. A critical innovation is the integration of LK causal analysis, which quantifies feature–target relationships via information flow metrics, effectively eliminating 29.5% of spurious correlations inherent in traditional feature selection (e.g., PCA). Experimental results confirm the model’s robustness, particularly for heartwood variants, while its adaptive fractional-order optimization accelerates convergence by 2.1× relative to standard PSO. This work provides a reliable, interpretable tool for wood quality assessment, with direct implications for grading systems and processing optimization in the forestry industry. Full article

Acoustic tomography is a non-destructive technique used in the internal assessment of standing trees. Various researchers have focused on developing analytical tools using this technique, demonstrating that they can detect internal biodeterioration in cross-sections with good accuracy. This study evaluates the use of two ultrasonic wave devices with different frequencies (USLab and Sylvatest Duo) and a stress wave device (Microsecond Timer) to generate acoustic tomography using ImageWood VC1 software. The tests were carried out on 12 cross-sections of urban trees in the city of Madrid of the species Robinia pseudoacacia L., Platanus × hybrida Brot., Ulmus pumila L., and Populus alba L. Velocity measurements were made, forming a diffraction mesh in both standing trees and logs after cutting them down. An inspection was carried out with a perforation resistance drill (IML RESI F-400S) in the radial direction in each section, which allowed for more precise identification of defects and differentiating between holes and cracks. The various defects were determined with greater accuracy in the tomographic images taken with the higher-frequency equipment (45 kHz), and the combination of ultrasonic tomography and the use of the inspection drill can provide a more accurate representation of the defects. Full article

The behavior of structural elements, which is very important in structural engineering, can be determined non-destructively using ambient vibration tests. Composite elements used in structures can be formed by combining elements of different materials. It is much more difficult to predict the structural behavior of composite elements because they are made of different materials. Ambient vibration tests are one of the most important methods used to determine the dynamic characteristics of composite elements. In this study, composite cantilever beams were formed by combining wood and steel profiles in various combinations. The dynamic characteristics of these beams (natural frequency, mode shape, modal damping ratio) were determined by both the numerical method and operational modal analysis (OMA) method. Firstly, the initial analytical models of the beams were modeled using the finite element program. The natural frequencies and mode shapes of the models were determined using the modal analysis method. While creating the initial analytical model, the material properties of the beams were entered by taking into account the standard values in the literature. Then, the dynamic characteristics of the beams were determined using an experimental modal analysis method (operational modal analysis test). The dynamic characteristics obtained from tests and the analysis of the initial analytical models were compared. The analytical models were calibrated according to the test results. In this way, the modeled beams were provided with a more realistic dynamic behavior. Numerical models were modeled using the SAP2000 program. As a result of the analysis, the dynamic characteristics and structural properties of composite cantilever beams were compared. As the elasticity modules and cross-sections of the profiles used in the beams increase, the stiffness of the beams also increases. It was determined that the natural frequencies of the composite beams increase with the increase in their stiffness. When the frequencies of the first modes of the least rigid wood (W) beam and the most rigid steel–wood–steel (S-W-S) beam were compared, an increase of 47% was detected. Full article

This text provides a comprehensive overview of structural timber old buildings, from an in-depth analysis of construction processes to laboratory-based research aimed at establishing a pattern for estimating the density of wood in buildings. It is now widely recognised by society that historic buildings should be subject to conservation or rehabilitation. This article discusses the good technical knowledge that those involved in old buildings should have: the understanding of and respect for old construction techniques; rigorous inspections and diagnosis before a project; and the recognition of the properties of wooden structural elements, either visually or by means of non-destructive or semi-destructive testing methods (NDT/SDT). The final section of this article presents a laboratory study that correlates penetration resistance test results with wood density and verifies them in situ by direct analysis with wood core extraction. The aim of this study is to establish and verify a reliable pattern that allows the user to estimate the density of Scots pine in any structural member in service in an old building. The results obtained in the laboratory and of wood in service show that Equation (1) is a suitable pattern to obtain wood density through the wood penetration resistance test. Full article

The diversity of commercial tree planting sites, with their distinct environmental conditions, directly influences tree growth and consequently impacts the wood properties in various ways. However, there is limited research evaluating the impact of these variations in nondestructive testing. Therefore, this study aimed to investigate whether edaphoclimatic variations affect parameters obtained through nondestructive tests conducted on standing trees. To this end, 30 specimens were selected from 3 Eucalyptus sp. clones, aged 1, 3, and 4 years, grown in 2 regions, totaling 540 trees. Tree development was monitored quarterly over 12 months. The tests included ultrasound propagation, drilling resistance, and penetration resistance, and the trees were measured for diameter at breast height (DBH) and height. Among the edaphoclimatic factors evaluated, only temperature and soil water storage differed statistically between the two study regions. The higher temperature and lower soil water storage in region 2 promoted tree growth, with these trees showing greater drilling resistance and higher longitudinal wave velocities. In addition, the influence of climatic factors was evidenced by the variation of wave propagation velocity throughout the year. Periods of lower water availability resulted in higher velocities, while periods of greater precipitation were associated with lower velocities. The research results showed that temperature and soil water storage had significant effects on tree growth (DBH and height), as well as ultrasound wave propagation velocity and drilling resistance. Full article

Inferring forest properties is crucial for the timber industry, enabling efficient monitoring, predictive analysis, and optimized management. Nondestructive testing (NDT) methods have proven to be valuable tools for achieving these goals. Recent advancements in data analysis, driven by machine learning (ML) algorithms, have revolutionized this field. This study analyzed 492 eucalyptus trees (Eucalyptus sp.), aged 3 to 7 years, planted in São Paulo, Brazil. Data from forest inventories were combined with results from ultrasound, drilling resistance, sclerometric impact, and penetration resistance tests. Seven machine learning algorithms were evaluated to compare their generalization capabilities with conventional statistical methods for predicting basic wood density. Among the models, extreme gradient boosting (XGBoost) achieved the highest accuracy, with a coefficient of determination (R²) of 89% and a root mean square error (RMSE) of 10.6 kg·m⁻³. In contrast, the conventional statistical model, using the same parameters, yielded an R² of 33% and an RMSE of 26.4 kg·m⁻³. These findings highlight the superior performance of machine learning in the nondestructive inference of wood properties, paving the way for its broader application in forest management and the timber industry. Full article

Modulus of elasticity (MOE) and modulus of rupture (MOR) are crucial indicators for assessing the application value of wood. However, traditional physical testing methods for the mechanical properties of wood are typically destructive, costly, and time-consuming. To efficiently assess these properties, this study proposes a multi-input residual network (MIRN) model, which integrates microscopic images of wood with physical density data and leverages deep learning technology for rapid and accurate predictions. By using larger convolution kernels to enhance the receptive field, the model captures fine microstructural features in the images. Batch normalization layers were removed from the ResNet architecture to reduce the number of parameters and improve training stability. Shortcut connections were utilized to enable deeper network architectures and address the vanishing gradient problem. Two types of residual blocks, convolutional block and identity block, were defined based on input dimensional changes. The MIRN method, based on multi-input residual networks, is proposed for non-destructive testing of wood mechanical properties. The experimental results show that MIRN outperforms convolutional neural networks (CNNs) and ResNet-50 in predicting MOE and MOR, with an R² of 0.95 for MOE and RMSE reduced to 46.88, as well as an R² of 0.85 for MOR and an RMSE of 0.44. Thus, this method offers an efficient and cost-effective tool for wood processing and quality control. Full article