Combined weakest link and random defect model for describing strength variability in fibres

1993 ◽  
Vol 28 (4) ◽  
pp. 931-941 ◽  
Author(s):  
W. F. Knoff
Keyword(s):  
Defect Model ◽  
Weakest Link ◽  
Strength Variability ◽  
Random Defect

Influence of Yield Strength Variability over Cross-Section to Steel Beam Load-Carrying Capacity

2005 ◽  
Vol 10 (2) ◽  
pp. 151-160 ◽  
Author(s):  
J. Kala ◽  
Z. Kala
Keyword(s):  
Yield Strength ◽  
Cross Section ◽  
Carrying Capacity ◽  
Bending Moment ◽  
Statistical Characteristics ◽  
Load Carrying Capacity ◽  
Load Carrying ◽  
Strength Variability ◽  
Hot Rolled ◽  
Hot Rolled Steel

Authors of article analysed influence of variability of yield strength over cross-section of hot rolled steel member to its load-carrying capacity. In calculation models, the yield strength is usually taken as constant. But yield strength of a steel hot-rolled beam is generally a random quantity. Not only the whole beam but also its parts have slightly different material characteristics. According to the results of more accurate measurements, the statistical characteristics of the material taken from various cross-section points (e.g. from a web and a flange) are, however, more or less different. This variation is described by one dimensional random field. The load-carrying capacity of the beam IPE300 under bending moment at its ends with the lateral buckling influence included is analysed, nondimensional slenderness according to EC3 is λ¯ = 0.6. For this relatively low slender beam the influence of the yield strength on the load-carrying capacity is large. Also the influence of all the other imperfections as accurately as possible, the load-carrying capacity was determined by geometrically and materially nonlinear solution of very accurate FEM model by the ANSYS programme.

A Comment on Random Versus Non-Random Defect Placement in Polyacetylane

1988 ◽  
Author(s):  
Stuart B. Clough ◽  
Sukant Tripathy ◽  
Xiao-Fang Sun ◽  
Bradley J. Orchard ◽  
Gary Wnek
Keyword(s):  
Random Defect

scholarly journals Effective Demand Forecasting in Health Supply Chains: Emerging Trend, Enablers, and Blockers

Logistics ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 12
Author(s):  
Lakshmy Subramanian
Keyword(s):  
Supply Chains ◽  
Demand Forecasting ◽  
Weak Links ◽  
Key Factors ◽  
Scarce Resources ◽  
Weakest Link ◽  
Emerging Trends ◽  
A Chain ◽  
Health Commodities ◽  
The Impact

Health supply chains aim to improve access to healthcare, and this can be attained only when health commodities appropriate to the health needs of the global population are developed, manufactured, and made available when and where needed. The weak links in the health supply chains are hindering the access of essential healthcare resulting in inefficient use of scarce resources and loss of lives. A chain is only as strong as its weakest link, and demand forecasting is one of the weakest links of health supply chains. Also, many of the existing bottlenecks in supply chains and health systems impede the accurate forecasting of demand, and without the ability to forecast demand with certainty, the stakeholders cannot plan and make commitments for the future. Forecasts are an important feeder for budgeting and logistics planning. Under this backdrop, the study examines how improved forecasting can lead to better short-term and long-term access to health commodities and outlines market-related risks. It explores further how incentives are misaligned creating an uneven distribution of risks, leading to the inability to match demand and supply. For this purpose, a systematic literature review was performed, analyzing 71 articles from a descriptive and content approach. Findings indicate the emerging trends in global health and the consequences of inaccurate demand forecasting for health supply chains. The content analysis identifies key factors that can pose a varying degree of risks for the health supply chain stakeholders. The study highlights how the key factors emerge as enablers and blockers, depending on the impact on the overall health supply chains. The study also provides recommendations for actions for reducing these risks. Consequently, limitations of this work are presented, and opportunities are identified for future lines of research. Finally, the conclusion confirms that by adopting a combination of approaches, stakeholders can ensure better information sharing, identify avenues of diversifying risks, and understand the implications.

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