Post-tensioning and its effect on multi-level formwork load distribution

Multi-level concrete buildings requrre substantial temporary formwork structures to support the slabs during construction. The primary function of this formwork is to safely disperse the applied loads so that the slab being constructed, or the portion of the permanent structure already constructed, is not overloaded. Multi-level formwork is a procedure in which a limited number of formwork and shoring sets are cycled up the building as construction progresses. In this process, each new slab is supported by a number of lower level slabs. The new slab load is, essentially, distributed to these supporting slabs in direct proportion to their relative stiffness. When a slab is post-tensioned using draped tendons, slab lift occurs as a portion of the slab self-weight is balanced. The formwork and shores supporting that slab are unloaded by an amount equivalent to the load balanced by the post-tensioning. This produces a load distribution inherently different from that of a conventionally reinforced slab. Through , theoretical modelling and extensive on-site shore load measurement, this research examines the effects of post-tensioning on multilevel formwork load distribution. The research demonstrates that the load distribution process for post-tensioned slabs allows for improvements to current construction practice. These enhancements include a shortening of the construction period; an improvement in the safety of multi-level form work operations; and a reduction in the quantity of form work materials required for a project. These enhancements are achieved through the general improvement in safety offered by post-tensioning during the various formwork operations. The research demonstrates that there is generally a significant improvement in the factors of safety over those for conventionally reinforced slabs. This improvement in the factor of safety occurs at all stages of the multi-level formwork operation. The general improvement in the factors of safety with post-tensioned slabs allows for a shortening of the slab construction cycle time. Further, the low level of load redistribution that occurs during the stripping operations makes post-tensioned slabs ideally suited to reshoring procedures. Provided the overall number of interconnected levels remains unaltered, it is possible to increase the number of reshored levels while reducing the number of undisturbed shoring levels without altering the factors of safety, thereby, reducing the overall quantity of formwork and shoring materials.

Discovery & effective use of quality association rules in multi-level datasets

In today’s electronic world vast amounts of knowledge is stored within many datasets and databases. Often the default format of this data means that the knowledge within is not immediately accessible, but rather has to be mined and extracted. This requires automated tools and they need to be effective and efficient. Association rule mining is one approach to obtaining knowledge stored with datasets / databases which includes frequent patterns and association rules between the items / attributes of a dataset with varying levels of strength. However, this is also association rule mining’s downside; the number of rules that can be found is usually very big. In order to effectively use the association rules (and the knowledge within) the number of rules needs to be kept manageable, thus it is necessary to have a method to reduce the number of association rules. However, we do not want to lose knowledge through this process. Thus the idea of non-redundant association rule mining was born. A second issue with association rule mining is determining which ones are interesting. The standard approach has been to use support and confidence. But they have their limitations. Approaches which use information about the dataset’s structure to measure association rules are limited, but could yield useful association rules if tapped. Finally, while it is important to be able to get interesting association rules from a dataset in a manageable size, it is equally as important to be able to apply them in a practical way, where the knowledge they contain can be taken advantage of. Association rules show items / attributes that appear together frequently. Recommendation systems also look at patterns and items / attributes that occur together frequently in order to make a recommendation to a person. It should therefore be possible to bring the two together. In this thesis we look at these three issues and propose approaches to help. For discovering non-redundant rules we propose enhanced approaches to rule mining in multi-level datasets that will allow hierarchically redundant association rules to be identified and removed, without information loss. When it comes to discovering interesting association rules based on the dataset’s structure we propose three measures for use in multi-level datasets. Lastly, we propose and demonstrate an approach that allows for association rules to be practically and effectively used in a recommender system, while at the same time improving the recommender system’s performance. This especially becomes evident when looking at the user cold-start problem for a recommender system. In fact our proposal helps to solve this serious problem facing recommender systems.

Charting the system : the integrated master schedule as a multi-level and poly-temporal boundary object in complex projects

This paper investigates the use of visual artifacts to represent a complex adaptive system (CAS). The integrated master schedule (IMS) is one of those visuals widely used in complex projects for scheduling, budgeting, and project management. In this paper, we discuss how the IMS outperforms the traditional timelines and acts as a ‘multi-level and poly-temporal boundary object’ that visually represents the CAS. We report the findings of a case study project on the way the IMS mapped interactions, interdependencies, constraints and fractal patterns in a complex project. Finally, we discuss how the IMS was utilised as a complex boundary object by eliciting commitment and development of shared mental models, and facilitating negotiation through the layers of multiple interpretations from stakeholders.

A multi-level model of leadership in complex project management

Management (or perceived mismanagement) of large-scale, complex projects poses special problems and often results in spectacular failures, cost overruns, time blowouts and stakeholder dissatisfaction. While traditional project management responds with increasingly administrative constraints, we argue that leaders of such projects also need to display adaptive and enabling behaviours to foster adaptive processes, such as opportunity recognition, which requires an interaction of cognitive and affective processes of individual, project, and team leader attributes and behaviours. At the core of this model we propose is an interaction of cognitive flexibility, affect and emotional intelligence. The result of this interaction is enhanced leader opportunity recognition that, in turn, facilitates multilevel outcomes.

Multi-level information fusion for environment aware robotic navigation

This thesis develops the hardware and software framework for an integrated navigation system. Dynamic data fusion algorithms are used to develop a system with a high level of resistance to the typical problems that affect standard navigation systems.

Interestingness Measures for Multi-Level Association Rules

Association rule mining is one technique that is widely used when querying databases, especially those that are transactional, in order to obtain useful associations or correlations among sets of items. Much work has been done focusing on efficiency, effectiveness and redundancy. There has also been a focusing on the quality of rules from single level datasets with many interestingness measures proposed. However, with multi-level datasets now being common there is a lack of interestingness measures developed for multi-level and cross-level rules. Single level measures do not take into account the hierarchy found in a multi-level dataset. This leaves the Support-Confidence approach, which does not consider the hierarchy anyway and has other drawbacks, as one of the few measures available. In this chapter we propose two approaches which measure multi-level association rules to help evaluate their interestingness by considering the database’s underlying taxonomy. These measures of diversity and peculiarity can be used to help identify those rules from multi-level datasets that are potentially useful.

Fault symptoms of rolling element bearings under variable operating conditions - a multi domain analysis

Diagnostics of rolling element bearings have been traditionally developed for constant operating conditions, and sophisticated techniques, like Spectral Kurtosis or Envelope Analysis, have proven their effectiveness by means of experimental tests, mainly conducted in small-scale laboratory test-rigs. Algorithms have been developed for the digital signal processing of data collected at constant speed and bearing load, with a few exceptions, allowing only small fluctuations of these quantities. Owing to the spreading of condition based maintenance in many industrial fields, in the last years a need for more flexible algorithms emerged, asking for compatibility with highly variable operating conditions, such as acceleration/deceleration transients. This paper analyzes the problems related with significant speed and load variability, discussing in detail the effect that they have on bearing damage symptoms, and propose solutions to adapt existing algorithms to cope with this new challenge. In particular, the paper will i) discuss the implication of variable speed on the applicability of diagnostic techniques, ii) address quantitatively the effects of load on the characteristic frequencies of damaged bearings and iii) finally present a new approach for bearing diagnostics in variable conditions, based on envelope analysis. The research is based on experimental data obtained by using artificially damaged bearings installed on a full scale test-rig, equipped with actual train traction system and reproducing the operation on a real track, including all the environmental noise, owing to track irregularity and electrical disturbances of such a harsh application.

A multi-genome analysis approach enables tracking of the invasion of a single Russian wheat aphid (Diuraphis noxia) clone throughout the New World

This study investigated the population genetics, demographic history and pathway of invasion of the Russian wheat aphid (RWA) from its native range in Central Asia, the Middle East and Europe to South Africa and the Americas. We screened microsatellite markers, mitochondrial DNA and endosymbiont genes in 504 RWA clones from nineteen populations worldwide. Following pathway analyses of microsatellite and endosymbiont data, we postulate that Turkey and Syria were the most likely sources of invasion to Kenya and South Africa, respectively. Furthermore, we found that one clone transferred between South Africa and the Americas was most likely responsible for the New World invasion. Finally, endosymbiont DNA was found to be a high resolution population genetic marker, extremely useful for studies of invasion over a relatively short evolutionary history time frame. This study has provided valuable insights into the factors that may have facilitated the recent global invasion by this damaging pest.

Team change climate : a group-level analysis of the relationships among change information and change participation, role stressors, and well-being

We explored whether teams develop shared perceptions regarding the quantity and quality of information and the extent of participation in decision making provided in an environment of continuous change. In addition, we examined whether change climate strength moderated relationships between change climate level and team outcomes. We examined relationships among aggregated change information and change participation and aggregated team outcomes, including two role stressors (i.e., role ambiguity and role overload) and two indicators of well-being (i.e., quality of worklife and distress). Questionnaires were distributed in an Australian law enforcement agency and data were used from 178 teams. Structural equation modelling analyses, controlling for a marker variable, were conducted to examine the main effects of aggregated change information and aggregated change participation on aggregated team outcomes. Results provided support for a model that included method effects due to a marker variable. In this model, change information climate was significantly negatively associated with role ambiguity, role overload, and distress, and significantly positively associated with quality of worklife. Change participation climate was significantly positively associated with quality of worklife. Change climate strength did not moderate relationships among change climate level and team outcomes.

The relevance of shared experiences : a multi-level study of collective efficacy as a moderator of job control in the stressor-strain relationship

Karasek's Job Demand-Control model proposes that control mitigates the positive effects of work stressors on employee strain. Evidence to date remains mixed and, although a number of individual-level moderators have been examined, the role of broader, contextual, group factors has been largely overlooked. In this study, the extent to which control buffered or exacerbated the effects of demands on strain at the individual level was hypothesized to be influenced by perceptions of collective efficacy at the group level. Data from 544 employees in Australian organizations, nested within 23 workgroups, revealed significant three-way cross-level interactions among demands, control and collective efficacy on anxiety and job satisfaction. When the group perceived high levels of collective efficacy, high control buffered the negative consequences of high demands on anxiety and satisfaction. Conversely, when the group perceived low levels of collective efficacy, high control exacerbated the negative consequences of high demands on anxiety, but not satisfaction. In addition, a stress-exacerbating effect for high demands on anxiety and satisfaction was found when there was a mismatch between collective efficacy and control (i.e. combined high collective efficacy and low control). These results provide support for the notion that the stressor-strain relationship is moderated by both individual- and group-level factors.

The location patterns of artistic clusters : a metro- and neighborhood-level analysis

Analysing census and industry data at the metro and neighbourhood levels, this paper seeks to identify the location characteristics associated with artistic clusters and determine how these characteristics vary across different places. We find that the arts cannot be taken overall as an urban panacea, but rather that their impact is place-specific and policy ought to reflect these nuances. However, our work also finds that, paradoxically, the arts’ role in developing metro economies is as highly underestimated as it is overgeneralised. While arts clusters exhibit unique industry, scale and place-specific attributes, we also find evidence that they cluster in ‘innovation districts’, suggesting they can play a larger role in economic development. To this end, our results raise important questions and point toward new approaches for arts-based urban development policy that look beyond a focus on the arts as amenities to consider the localised dynamics between the arts and other industries.