Improved design rules for hollow flange sections subject to lateral distortional buckling

The LiteSteel Beam (LSB) is a new hollow flange section with a unique geometry consisting of torsionally rigid rectangular hollow flanges and a relatively slender web. It is subjected to lateral distortional buckling when used as flexural members, which reduces its member moment capacity. An investigation into the flexural behaviour of LSBs using experiments and numerical analyses led to the development of new design rules for LSBs subject to lateral distortional buckling. However, the comparison of moment capacity results with the new design rules showed that they were conservative for some LSB sections while slightly unconservative for others due to the effects of section geometry. It is also unknown whether these design rules are applicable to other hollow flange sections such as hollow flange beams (HFB). This paper presents the details of a study into the lateral distortional buckling behaviour of hollow flange sections such as LSBs, HFBs and their variations. A geometrical parameter defined as the ratio of flange torsional rigidity to the major axis flexural rigidity of the web (GJf/EIxweb) was found to be a critical parameter in evaluating the lateral distortional buckling behaviour and moment capacities of hollow flange sections. New design rules were therefore developed by using a member slenderness parameter modified by K, where K is a function of GJf/EIxweb. The new design rules based on the modified slenderness parameter were found to be accurate in calculating the moment capacities of not only LSBs and HFBs, but also other types of hollow flange sections.

Improved design rules for fixed ended cold-formed steel columns subject to flexural-torsional buckling

This paper has presented the details of an investigation into the flexural and flexuraltorsional buckling behaviour of cold-formed structural steel columns with pinned and fixed ends. Current design rules for the member capacities of cold-formed steel columns are based on the same non-dimensional strength curve for both fixed and pinned-ended columns. This research has reviewed the accuracy of the current design rules in AS/NZS 4600 and the North American Specification in determining the member capacities of cold-formed steel columns using the results from detailed finite element analyses and an experimental study of lipped channel columns. It was found that the current Australian and American design rules accurately predicted the member capacities of pin ended lipped channel columns undergoing flexural and flexural torsional buckling. However, for fixed ended columns with warping fixity undergoing flexural-torsional buckling, it was found that the current design rules significantly underestimated the column capacities as they disregard the beneficial effect of warping fixity. This paper has therefore proposed improved design rules and verified their accuracy using finite element analysis and test results of cold-formed lipped channel columns made of three cross-sections and five different steel grades and thicknesses.

“I would have lost the respect of my friends and family if they knew I had bent the road rules” : parents, peers, and the perilous behaviour of young drivers

Young novice drivers are at considerable risk of injury on the road. Their behaviour appears vulnerable to the social influence of their parents and friends. The nature and mechanisms of parent and peer influence on young novice driver (16–25 years) behaviour was explored via small group interviews (n = 21) and two surveys (n1 = 1170, n2 = 390) to inform more effective young driver countermeasures. Parental and peer influence occurred in preLicence, Learner, and Provisional (intermediate) periods. Pre-Licence and unsupervised Learner drivers reported their parents were less likely to punish risky driving (e.g., speeding). These drivers were more likely to imitate their parents and reported their parents were also risky drivers. Young novice drivers who experienced or expected social punishments from peers, including ‘being told off’ for risky driving, reported less riskiness. Conversely drivers who experienced or expected social rewards such as being ‘cheered on’ by friends – who were also more risky drivers – reported more risky driving including crashes and offences. Interventions enhancing positive influence and curtailing negative influence may improve road safety outcomes not only for young novice drivers, but for all persons who share the road with them. Parent-specific interventions warrant further development and evaluation including: modelling safe driving behaviour by parents; active monitoring of driving during novice licensure; and sharing the family vehicle during the intermediate phase. Peer-targeted interventions including modelling of safe driving behaviour and attitudes; minimisation of social reinforcement and promotion of social sanctions for risky driving also need further development and evaluation.

A Reliable Basis for Approximate Association Rules

For most of the work done in developing association rule mining, the primary focus has been on the efficiency of the approach and to a lesser extent the quality of the derived rules has been emphasized. Often for a dataset, a huge number of rules can be derived, but many of them can be redundant to other rules and thus are useless in practice. The extremely large number of rules makes it difficult for the end users to comprehend and therefore effectively use the discovered rules and thus significantly reduces the effectiveness of rule mining algorithms. If the extracted knowledge can’t be effectively used in solving real world problems, the effort of extracting the knowledge is worth little. This is a serious problem but not yet solved satisfactorily. In this paper, we propose a concise representation called Reliable Approximate basis for representing non-redundant approximate association rules. We prove that the redundancy elimination based on the proposed basis does not reduce the belief to the extracted rules. We also prove that all approximate association rules can be deduced from the Reliable Approximate basis. Therefore the basis is a lossless representation of approximate association rules.

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 paper we propose two approaches which measure multi-level association rules to help evaluate their interestingness. These measures of diversity and peculiarity can be used to help identify those rules from multi-level datasets that are potentially useful.

Concise representations for association rules in multi-level datasets

Association rule mining has made many advances in the area of knowledge discovery. However, the quality of the discovered association rules is a big concern and has drawn more and more attention recently. One problem with the quality of the discovered association rules is the huge size of the extracted rule set. Often for a dataset, a huge number of rules can be extracted, but many of them can be redundant to other rules and thus useless in practice. Mining non-redundant rules is a promising approach to solve this problem. In this paper, we firstly propose a definition for redundancy; then we propose a concise representation called Reliable basis for representing non-redundant association rules for both exact rules and approximate rules. An important contribution of this paper is that we propose to use the certainty factor as the criteria to measure the strength of the discovered association rules. With the criteria, we can determine the boundary between redundancy and non-redundancy to ensure eliminating as many redundant rules as possible without reducing the inference capacity of and the belief to the remaining extracted non-redundant rules. We prove that the redundancy elimination based on the proposed Reliable basis does not reduce the belief to the extracted rules. We also prove that all association rules can be deduced from the Reliable basis. Therefore the Reliable basis is a lossless representation of association rules. Experimental results show that the proposed Reliable basis can significantly reduce the number of extracted rules.

Investigating the use of association rules in improving recommender systems

Recommender systems are widely used online to help users find other products, items etc that they may be interested in based on what is known about that user in their profile. Often however user profiles may be short on information and thus when there is not sufficient knowledge on a user it is difficult for a recommender system to make quality recommendations. This problem is often referred to as the cold-start problem. Here we investigate whether association rules can be used as a source of information to expand a user profile and thus avoid this problem, leading to improved recommendations to users. Our pilot study shows that indeed it is possible to use association rules to improve the performance of a recommender system. This we believe can lead to further work in utilising appropriate association rules to lessen the impact of the cold-start problem.