W?lder und Waldwirtschaft in Bulgarien

Ohne Zusammenfassung     

Der Hagelschlag vom 27. Juli 1829 in den oberbayerischen Staatsforsten

Ohne Zusammenfassung     

New splitting method for wood fracture characterization

Summary A testing procedure with a new and simple specimen shape is presented which is appropriate to characterize fracturing of inhomogeneous and complex materials like wood. With this, the fracture energy of spruce wood is determined in the TL and RL direction. The size effect, i.e. influences of specimen dimensions on K_IC and G_f (specific fracture energy) are investigated. Stress and deformation distribution in the newly developed specimens are analysed with FE methods. The measured load-displacement curves are approximated by bilinear softening diagrams and FE analysis. Based on these results, it is tried to interpret typical deviations from LEFM's behaviour by mechanisms like microcracking, crack branching or crack tip bridging.The authors thank Dr. A. Teischinger for supplying the testing material and Dipl. Ing M. Elser for preparation of the diagrams. Financial support of the Fonds zur Förderung der wissenschaftlichen Forschung, Wien is gratefully acknowledged.     

Stress wave sorting of red maple logs for structural quality

Existing log grading procedures in the United States make only visual assessments of log quality. These procedures do not incorporate estimates of the modulus of elasticity (MOE) of logs. It is questionable whether the visual grading procedures currently used for logs adequately assess the potential quality of structural products manufactured from them, especially those for which MOE is of primary concern. The purpose of this study was to investigate the use of stress wave nondestructive evaluation techniques to sort red maple logs for the potential quality of lumber obtained from them. Ninety-five red maple logs were nondestructively evaluated using longitudinal stress wave techniques and sorted into four stress wave grades. The logs were then sawn into cants and lumber. The same procedure was used to obtain stress wave times in the cants and lumber. The lumber specimens were then dried and graded using a transverse vibration technique. The results of this study showed that good relationships existed between stress wave times measured in logs, cants, and the lumber produced from the logs. It was found that log stress wave grades have positive relationships with the lumber grades. Logs with high stress wave grades produced high-grade lumber. These findings indicate that the longitudinal stress wave technique has potential in sorting logs and cants for the production of high MOE products.The Forest Products Laboratory is maintained in cooperation with the University of Wisconsin. This article was written and prepared by U.S. Government employees on official time, and it is therefore in the public domain and not subject to copyright.     

Utilization of Weather Data in Predicting Bread Loaf Volume by Neural Network Method

The unique ability of wheat (Triticum aestivum L.) flour to produce cohesive dough has helped to make it the most widely used cereal crop for bread and other baked food products. Measurement of end‐use qualities, such as loaf volume, is ideally carried out through assessing loaves of bread; however, this is resource intensive. Predictive testing methods are more often utilized to identify wheat genotypes with potentially acceptable loaf volume, although more accurate predictive methods would be beneficial. Our objectives were to study the influence of weather observations on bread loaf volume and flour and dough quality data, to use a neural network (NN) model to predict loaf volume with select input data, and to compare the best multiple regression models identified with their NN counterparts. Weather data collected at 20 days after heading (DAH) showed the highest correlations with bread loaf volume when compared with prior intervals. A NN model containing maximum, minimum, and nighttime temperatures produced the highest coefficient of determination for predicting loaf volume. Our results showed that the NN model explained up to 20% more loaf volume variation than a similar regression model. Weather parameters representing conditions at 20 DAH played a significant role in loaf volume prediction.