Where bolts are solidly cast into concrete - i.e. are set to Non-Adjustable - the bolts can be relied upon to resist shear.
In this case, the design may be based on effective bearing length in concrete of 3d and an average bearing stress of 2 fcu. For more about this see the related article linked at the bottom of this article.
Where bolts are not solidly cast into concrete i.e. set to are Adjustable, some allowance can be made for a reduction of the bearing on the bolts capacity via the factors "Factor * d" , which adjusts the bolt bearing length, and "Factor * fcu" which adjusts the concrete bearing stress. The default value of both factors is 1.0.
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The values to use for your connection is a matter for your engineering judgement - we cannot provide specific advice on the values to be used.
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Evan ChanSlides from my talk at MinneAnalytics - June 7, https://datatech.sched.com/event/1eO0m/time-state-analytics-a-new-paradigm Across many domains, we see a growing need for complex analytics to track precise metrics at Internet scale to detect issues, identify mitigations, and analyze patterns. Think about delays in airlines (Logistics), food delivery tracking (Apps), detect fraudulent transactions (Fintech), flagging computers for intrusion (Cybersecurity), device health (IoT), and many more. For instance, at Conviva, our customers want to analyze the buffering that users on some types of devices suffer, when using a specific CDN. We refer to such problems as Multidimensional Time-State Analytics. Time-State here refers to the stateful context-sensitive analysis over event streams needed to capture metrics of interest, in contrast to simple aggregations. Multidimensional refers to the need to run ad hoc queries to drill down into subpopulations of interest. Furthermore, we need both real-time streaming and offline retrospective analysis capabilities. In this talk, we will share our experiences to explain why state-of-art systems offer poor abstractions to tackle such workloads and why they suffer from poor cost-performance tradeoffs and significant complexity. We will also describe Conviva&#;s architectural and algorithmic efforts to tackle these challenges. We present early evidence on how raising the level of abstraction can reduce developer effort, bugs, and cloud costs by (up to) an order of magnitude, and offer a unified framework to support both streaming and retrospective analysis. We will also discuss how our ideas can be plugged into existing pipelines and how our new ``visual'' abstraction can democratize analytics across many domains and to non-programmers.
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