Use of Biomechanical Static Strength Models in Workspace Design

Author(s):  
Susan M. Evans
Alloy Digest ◽  
2015 ◽  
Vol 64 (4) ◽  

Abstract This producer has pioneered the development of the -T77 temper, a high strength corrosion resistant temper for Alloy 7150 plate and extrusions. Alloy 7150-T77 provides weight savings opportunities in structure governed by static strength requirements but where "overaged" condition corrosion resistance is required. This datasheet provides information on composition, tensile properties, and compressive strength. It also includes information on corrosion resistance as well as forming. Filing Code: Al-442. Producer or source: Alcoa Mill Products Inc..


A description is given of the experimental technique devised to apply the method outlined theoretically in part I to the measurement of the dynamic compressive yield strength of various steels, duralumin, copper, lead, iron and silver. A polished piece of armour steel was employed as a target, and cylindrical specimens were fired at it at various measured velocities from Service weapons. The distance between the weapon and target was made short to ensure normal impact, and apparatus was devised for the precise measurement of striking velocity over this short range. The dynamic compressive yield strength was computed from the density of the specimen, the striking velocity, and from measurements of the dimensions of the test piece before and after test. Details are given of the accuracy of the various measurements, and of their effect on the values of yield strength. The method was found to be inaccurate at low and high velocities. For instance, with mild steel, satisfactory results were only obtainable within the range 400 to 2500 ft. /sec. The range of velocities within which satisfactory results could be obtained varied with the quality of the material tested, soft metals giving results within a much lower range than that necessary for harder materials. Because of its failure at low velocities, the method could not be employed to bridge the gap between static and dynamic tests. The rate of strain employed in the dynamic tests could not be measured, but was estimated to be of the order of 10,000 in. /in. /sec. With the materials tested little change of dynamic strength occurred within the range of striking velocities employed, probably because the rate of strain did not vary to any great extent with the striking velocity. Within the range of weapons available, that is, from a 0·303 in. rifle up to a 13 pdr. gun (calibre 3·12 in.), little change of dynamic strength occurred with alteration of the initial dimensions of the specimens, probably because the corresponding change of rate of strain was not large. In general, the dynamic compressive yield strength S was greater than the static strength Y represented by the compressive stress giving 0·2% permanent strain. For steels of various types, regardless of chemical composition and heat treatment, there was a relation between S / Y and the static strength Y , the ratio decreasing from approximately 3 when Y was 20 tons/sq. in. to 1 when Y was 120 tons/sq. in. A similar relation occurred with duralumin, S / Y varying from 2·5 at Y = 8 tons/sq. in. to 1·4 at Y = 25 tons/sq. in. Dynamic compressive yield values were obtained for soft materials such as pure lead, copper and Armco iron, which, under static conditions, gave no definite yield values. A plot of the unstrained length of the specimen X , expressed as X / L (where L = initial overall length), versus the final overall length L 1 , expressed as L 1 / L , was made for the various materials. Any specified value of X / L was associated with greater values of L 1 / L for the more ductile materials, such as copper and lead, than for the brittle materials, such as armour plate and duralumin.


2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
De-Cheng Feng ◽  
Bo Fu

In this paper, an intelligent modeling approach is presented to predict the shear strength of the internal reinforced concrete (RC) beam-column joints and used to analyze the sensitivity of the influence factors on the shear strength. The proposed approach is established based on the famous boosting-family ensemble machine learning (ML) algorithms, i.e., gradient boosting regression tree (GBRT), which generates a strong predictive model by integrating several weak predictors, which are obtained by the well-known individual ML algorithms, e.g., DT, ANN, and SVM. The strong model is boosted as each weak predictor has its own weight in the final combination according to the performance. Compared with the conventional mechanical-driven shear strength models, e.g., the well-known modified compression field theory (MCFT), the proposed model can avoid the complicated derivation process of shear mechanism and calibration of the involved empirical parameters; thus, it provides a more convenient, fast, and robust alternative way for predicting the shear strength of the internal RC joints. To train and test the GBRT model, a total of 86 internal RC joint specimens are collected from the literatures, and four traditional ML models and the MCFT model are also employed as comparisons. The results indicate that the GBRT model is superior to both the traditional ML models and MCFT model, as its degree-of-fitting is the highest and the predicting dispersion is the lowest. Finally, the model is used to investigate the influences of different parameters on the shear strength of the internal RC joint, and the sensitivity and importance of the corresponding parameters are obtained.


Author(s):  
Faith McCreary ◽  
Ray Reaux ◽  
Roger Ehrich ◽  
Susan Hood ◽  
Keith Rowland

Computers and network connectivity in the classroom raise new challenges in workspace design. Unlike corporate or dedicated laboratory facilities, a technology-rich classroom plays multiple roles throughout its working day. Classroom design demands flexible and robust construction, particularly when applied in an elementary school setting. Using the PCs for Families project as a case study, this paper discusses design issues of a technology-rich networked classroom from ergonomic design to system support.


Sign in / Sign up

Export Citation Format

Share Document