Effects of Climate on Exterior Wood Coating Performance: A Comparison of Three Industrial Coatings in a Warm-Summer Mediterranean and a Semi-Arid Climate in Oregon, USA

Wood exposed in exterior applications degrades and changes color due to weathering and fungal growth. Wood coatings can reduce the effects of weathering by reducing the damaging effects of ultraviolet light, reducing water absorption, and slowing fungal growth on the surface. Coating performance depends on the blend of resins, oils, and pigments and varies considerably among different wood species and conditions. Specific information describing expected service for different wood species and exposure conditions is not commonly available; certain combinations may work well in one climate or on one timber species, but underperform elsewhere. This study compared the performance of three industrial wood coatings on two wood species for two temperate climates under natural weathering conditions. Most of the coatings/species combinations lost their protective properties within 12 to 15 months; however, fungal growth was more prevalent at the wetter site than at the drier site for several combinations. Film-forming coatings often peeled and cracked, while penetrating coatings weathered and changed color relatively uniformly during the study. While no coating was completely effective, the results illustrate the benefits of using coatings that promote the development of natural, uniform-patinaed wood surfaces. The findings also guide coating maintenance programs for mass timber structures exposed to natural weathering conditions.

Improved Wood Species Identification Based On Multi-View Imagery of The Three Anatomical Planes

Background: The identification of tropical African wood species based on microscopic imagery is a challenging problem due to the heterogeneous nature of the composition of wood combined with the vast number of candidate species. Image classification methods that rely on machine learning can facilitate this identification, provided that sufficient training material is available. Despite the fact that the three main anatomical sections contain information that is relevant for species identification, current methods only rely on the transversal section. Additionally, commonly used procedures for evaluating the performance of these methods neglect the fact that multiple images often originate from the same tree, leading to an overly optimistic estimate of the performance. Results: We introduce a new image dataset containing microscopic images of the three main anatomical sections of 77 Congolese wood species. A dedicated multiview image classification method is developed and obtains an accuracy (computed using the naive but common approach) of 95%, outperforming the singleview methods by a large margin. An in-depth analysis shows that naive accuracy estimates can lead to a dramatic over-prediction, of up to 60%, of the accuracy. Conclusions: Additional images from the non-transversal sections can boost the performance of machine-learning-based wood species identification methods. Additionally, care should be taken when evaluating the performance of machine-learningbased wood species identification methods to avoid an overestimation of the performance.

Распределение механических свойств древесины лиственных пород в годовых кольцах, выявленных методом сканирующего наноиндентирования

The paper presents the results of mechanical properties scanning by means of nanoindentation across the annual growth rings of deciduous trees wood, small-leaved lime (Tilia cordata) and common oak (Quercus robur) in particular. Significant variations in microhardness H and Young’s modulus E radial dependencies have been found for any of the studied species. Results can be useful 1) to amend the understanding the nature of macromechanical properties of various wood species and to reveal the details of their formation depending upon microstructural characteristics, 2) to optimize the technologies of growing, reinforcement and subsequent usage of the wood, 3) to develop new independent methods in dendrochronology and dendroclimatology

The flexural strength of glue laminated timber beams based on deflection and strain rate with four point bending loading system

The development of utilization of low quality wood as construction material is needed to reduce the exploitation of natural forests. However, low quality wood species have disadvantages in terms of mechanical properties. The mechanical properties of Sengon wood are relatively low, so it does not qualify as a structural element. Therefore, the system glulam can be applied to overcome this problem. The system glulam can produce relatively light structural elements with adequate performance. This system has been extensively developed, even at the stage of applying external reinforcement, to improve the performance of structural laminated beams. On that basis, this study aims to determine the flexural strength of laminated beams of Sengon wood as a low quality wood species. In order to achieve this goal, the laminated beam was tested using method four point bending test method. Tests were carried out on long span laminated beams (L = 2750 mm) to observe flexural strength. There are five (5) laminated blocks tested, namely (BLS-1, BLS-2, BLS-3, BLS-4 and BLS-5). Each group has dimensions of 55 mm in width and 155 mm in height. Each specimen consists of six layers of wood boards with a density Falcata 0.3 g / cm3. The thickness of each layer was 26 mm and bonded with resin urea formaldehyde cold setting. Double-sided adhesive laying of 350 gr / m2 at a compressive force of 2 MPa. The analysis result shows that the load-deflection relationship between BS-L consists of linear and nonlinear phases. The load performance characteristics of the two types of laminated beams are expressed as the ratio of the proportional limit load to the maximum load. The ratio value is expressed in the form P eBL-s = 0.7P max BL-S andM eBL-s = 0.7M max BL-S. This form is similar to previous studies with a Pe to Pmax ratio of 0.80.9. In this case, the average flexural strength of the laminated beam is 17 MPa with a maximum strain of 0.004.

Using artificial neural networks to determine the prospects of using hybrid tree clones for plantation reforestation

Оценка перспективности использования клонов гибридных пород древесины является одной из актуальных задач для повышения эффективности плантационного лесовыращивания. Одним из перспективных путей решения данной задачи является применение искусственных нейронных сетей (ИНС). Настоящая научная работа является одной из немногих, где применяется ИНС для решения подобных задач в лесном хозяйстве. Для обучения нейронных сетей и определения перспективности использования клонов гибридных пород древесины для плантационного лесовыращивания были взяты биометрические данные клонов гибридной осины 2018 г. В ходе выполнения работы были построены две ИНС, где архитектура первой сети включает входной слой из 3 нейронов, 1 скрытый слой с 6 нейронами и выходной слой из 1 нейрона; архитектура второй сети включает в себя входной слой из 3 нейронов, 2 скрытых слоя по 6 нейронов и выходной слой из 1 нейрона, в которые были загружены нормализованные исходные биометрические данные для обучения определения перспективности использования клонов гибридных пород древесины для плантационного лесовыращивания. По результатам данного исследования была составлена сравнительная характеристика точности ИНС 1 и ИНС 2, которая показала, что ИНС 1 более точная, так как её отклонение на 3,49% меньше ИНС 2. Результаты настоящей работы подтвердили перспективность применения ИНС для оценки использования клонов гибридных пород древесины для плантационного лесовыращивания. По оценке расчётной перспективности ИНС 1 для плантационного лесовыращивания были выявлены клоны гибридных пород древесины VTI, ESCH3, ESCH5. Внедрение ИНС в отрасль лесного хозяйства упрощает оценку результатов биометрических показателей древесины, особенно для начинающих специалистов, что обеспечивает последующую точную оценку перспективности пород древесины. Assessing the prospects of using hybrid wood clones is one of the urgent tasks to improve the efficiency of plantation silviculture. One of the promising ways to solve this problem is the use of artificial neural networks (ANN). This research work is one of the few where ANN are used to solve such problems in forestry. Biometric data from 2018 hybrid aspen clones were taken to train neural networks and determine the potential use of hybrid wood clones for plantation silviculture. During this work, two ANNs were constructed where the architecture of the first network includes an input layer of 3 neurons, 1 hidden layer with 6 neurons and an output layer of 1 neuron, the architecture of the second network includes an input layer of 3 neurons, 2 hidden layers of 6 neurons and an output layer of 1 neuron, into which the normalized input biometric data were loaded for learning to determine the prospective use of hybrid wood species clones for plantation silviculture. Based on the results of this study, a comparison of the accuracy of ANN 1 and ANN 2 was made, which showed that ANN 1 was more accurate because its bias was 3,49% less than ANN 2. The results of this work confirmed the promise of using ANN to evaluate the use of hybrid wood clones for plantation reforestation. According to the evaluation of the calculated promisingness of ANN 1 for plantation silviculture, VTI, ESCH3 and ESCH5 hybrid wood clones were identified. The introduction of ANN in the forestry industry simplifies the evaluation of wood biometric results, especially for beginners, which provides a subsequent accurate assessment of the perspective of wood species.

Application of Activated Afzelia Africana and Acacia Albida Carbon for Removal of Pollutants in Textile Wastewater

Adsorption of contaminants in textile wastewater onto activated carbon derived from two wood species has been studied using batch-adsorption techniques. This study was carried out to examine the removal efficiency of the low-cost adsorbent (Afzelia africana) AFA and (Acacia albida) ACA for the removal of heavy metals and other organic contaminants from textile effluents. The influence of contact time and adsorbent dose kept constant on the adsorption process was also studied. Removal efficiency increased with increase in contact time. The two adsorbents had an average removal efficiency of 60% at 90mins contact time for Zn. The ACA had higher removal efficiency for chromium at all contact times than AFA except at 120mins contact time where there existed a slight difference in the removal efficiency between the two adsorbents. Removal efficiency of iron was high between 58.18- 70.52% and 72.75-75.86% for AFA and ACA carbon respectively. This showed that iron had high affinity to the adsorbents surface. It was observed that AFA exhibited highest removal efficiency for nitrate at all contact times as compared to ACA. Results indicated that the freely abundant, locally available, low-cost adsorbent derived from the two wood species could be treated as being economically viable for the removal of contaminants from textile effluents.