Implementing value management as a decision-making tool in the design stages of design and build construction projects : a methodology for improved cost optimization

Value Management (VM) has been proven to provide a structured framework, together with other supporting tools and techniques, that facilitate effective decision-making in many types of projects, thus achieving ‘best value’ for clients. One of the major success factors of VM in achieving better project objectives for clients is through the provision of beneficial input by multi-disciplinary team members being involved in critical decision-making discussions during the early stage of construction projects. This paper describes a doctoral research proposal based on the application of VM in design and build construction projects, especially focusing on the design stage. The research aims to study the effects of implementing VM in design and build construction projects, in particular how well the methodology addresses issues related to cost overruns resulting from poor coordination and overlooking of critical constructability issues amongst team members in construction projects in Malaysia. It is proposed that through contractors’ early involvement during the design stage, combined with the use of the VM methodology, particularly as a decision-making tool, better optimization of construction cost can be achieved, thus promoting more efficient and effective constructability. The main methods used in this research involve a thorough literature study, semi-structured interviews, and a survey of major stakeholders, a detailed case study and a VM workshop and focus group discussions involving construction professionals in order to explore and possibly develop a framework and a specific methodology for the facilitating successful application of VM within design and build construction projects.

Bayesian methodology incorporating expert judgment for ranking countermeasure effectiveness under uncertainty: Example applied to at grade railroad crossings in Korea

This paper describes the formalization and application of a methodology to evaluate the safety benefit of countermeasures in the face of uncertainty. To illustrate the methodology, 18 countermeasures for improving safety of at grade railroad crossings (AGRXs) in the Republic of Korea are considered. Akin to “stated preference” methods in travel survey research, the methodology applies random selection and laws of large numbers to derive accident modification factor (AMF) densities from expert opinions. In a full Bayesian analysis framework, the collective opinions in the form of AMF densities (data likelihood) are combined with prior knowledge (AMF density priors) for the 18 countermeasures to obtain ‘best’ estimates of AMFs (AMF posterior credible intervals). The countermeasures are then compared and recommended based on the largest safety returns with minimum risk (uncertainty). To the author's knowledge the complete methodology is new and has not previously been applied or reported in the literature. The results demonstrate that the methodology is able to discern anticipated safety benefit differences across candidate countermeasures. For the 18 at grade railroad crossings considered in this analysis, it was found that the top three performing countermeasures for reducing crashes are in-vehicle warning systems, obstacle detection systems, and constant warning time systems.

An evaluation methodology for the tangible and intangible assets of city-regions : the 6K1C framework

Together with hard and soft networks tangible and intangible regional assets play an important role in the knowledge-based development of competing city-regions. The aim of this paper, therefore, is to investigate the best ways of managing invaluable tangible and intangible assets of city-regions. The paper explores the importance of asset management of city-regions by giving special emphasis on their knowledge asset base. This paper develops and introduces a theoretical framework to conceptualise a new approach to articulate the strategic planning mechanism, so called the 6K1C framework. The 6K1C framework is part of the strategic planning process of continuous improvement of overall public sector performance. The framework provides a proactive check-list approach integrated for managing and harnessing tangible and intangible assets of the post-industrial city-regions.

Building a methodology for context-aware business processes: insights from an exploratory case study

This paper describes the findings derived from an exploratory case study into the business processes at a leading Australian insurance provider. The business processes are frequently subjected to changes and deviations due to contextual events such as weather, financial conditions and others. In this study, we examine how context impacts business processes and how resulting business process changes are enacted. From our analysis, we suggest a methodological framework to guide organisations in the complex challenge of linking changing contextual factors with internal process design.

Influence of historical landscapes, drainage evolution and ecological traits on patterns of genetic diversity in Southeast Asian freshwater snakehead fishes

Snakehead fishes in the family Channidae are obligate freshwater fishes represented by two extant genera, the African Parachannna and the Asian Channa. These species prefer still or slow flowing water bodies, where they are top predators that exercise high levels of parental care, have the ability to breathe air, can tolerate poor water quality, and interestingly, can aestivate or traverse terrestrial habitat in response to seasonal changes in freshwater habitat availability. These attributes suggest that snakehead fishes may possess high dispersal potential, irrespective of the terrestrial barriers that would otherwise constrain the distribution of most freshwater fishes. A number of biogeographical hypotheses have been developed to account for the modern distributions of snakehead fishes across two continents, including ancient vicariance during Gondwanan break-up, or recent colonisation tracking the formation of suitable climatic conditions. Taxonomic uncertainty also surrounds some members of the Channa genus, as geographical distributions for some taxa across southern and Southeast (SE) Asia are very large, and in one case is highly disjunct. The current study adopted a molecular genetics approach to gain an understanding of the evolution of this group of fishes, and in particular how the phylogeography of two Asian species may have been influenced by contemporary versus historical levels of dispersal and vicariance. First, a molecular phylogeny was constructed based on multiple DNA loci and calibrated with fossil evidence to provide a dated chronology of divergence events among extant species, and also within species with widespread geographical distributions. The data provide strong evidence that trans-continental distribution of the Channidae arose as a result of dispersal out of Asia and into Africa in the mid–Eocene. Among Asian Channa, deep divergence among lineages indicates that the Oligocene-Miocene boundary was a time of significant species radiation, potentially associated with historical changes in climate and drainage geomorphology. Mid-Miocene divergence among lineages suggests that a taxonomic revision is warranted for two taxa. Deep intra-specific divergence (~8Mya) was also detected between C. striata lineages that occur sympatrically in the Mekong River Basin. The study then examined the phylogeography and population structure of two major taxa, Channa striata (the chevron snakehead) and the C. micropeltes (the giant snakehead), across SE Asia. Species specific microsatellite loci were developed and used in addition to a mitochondrial DNA marker (Cyt b) to screen neutral genetic variation within and among wild populations. C. striata individuals were sampled across SE Asia (n=988), with the major focus being the Mekong Basin, which is the largest drainage basin in the region. The distributions of two divergent lineages were identified and admixture analysis showed that where they co-occur they are interbreeding, indicating that after long periods of evolution in isolation, divergence has not resulted in reproductive isolation. One lineage is predominantly confined to upland areas of northern Lao PDR to the north of the Khorat Plateau, while the other, which is more closely related to individuals from southern India, has a widespread distribution across mainland SE Asian and Sumatra. The phylogeographical pattern recovered is associated with past river networks, and high diversity and divergence among all populations sampled reveal that contemporary dispersal is very low for this taxon, even where populations occur in contiguous freshwater habitats. C. micropeltes (n=280) were also sampled from across the Mekong River Basin, focusing on the lower basin where it constitutes an important wild fishery resource. In comparison with C. striata, allelic diversity and genetic divergence among populations were extremely low, suggesting very recent colonisation of the greater Mekong region. Populations were significantly structured into at least three discrete populations in the lower Mekong. Results of this study have implications for establishing effective conservation plans for managing both species, that represent economically important wild fishery resources for the region. For C. micropeltes, it is likely that a single fisheries stock in the Tonle Sap Great Lake is being exploited by multiple fisheries operations, and future management initiatives for this species in this region will need to account for this. For C. striata, conservation of natural levels of genetic variation will require management initiatives designed to promote population persistence at very localised spatial scales, as the high level of population structuring uncovered for this species indicates that significant unique diversity is present at this fine spatial scale.

An intelligent data mining technique for product quality improvement

Advances in data mining have provided techniques for automatically discovering underlying knowledge and extracting useful information from large volumes of data. Data mining offers tools for quick discovery of relationships, patterns and knowledge in large complex databases. Application of data mining to manufacturing is relatively limited mainly because of complexity of manufacturing data. Growing self organizing map (GSOM) algorithm has been proven to be an efficient algorithm to analyze unsupervised DNA data. However, it produced unsatisfactory clustering when used on some large manufacturing data. In this paper a data mining methodology has been proposed using a GSOM tool which was developed using a modified GSOM algorithm. The proposed method is used to generate clusters for good and faulty products from a manufacturing dataset. The clustering quality (CQ) measure proposed in the paper is used to evaluate the performance of the cluster maps. The paper also proposed an automatic identification of variables to find the most probable causative factor(s) that discriminate between good and faulty product by quickly examining the historical manufacturing data. The proposed method offers the manufacturers to smoothen the production flow and improve the quality of the products. Simulation results on small and large manufacturing data show the effectiveness of the proposed method.

A comparative review of quantitative VFM methodology for PPP infrastructure project in Indonesia and Australia

The Government of Indonesia (GoI) increasingly relies on the private sector financing to build and operate infrastructures through public private partnership (PPP) schemes. However, PPP does not automatically provide the solution for the financing scheme due to value for money (VFM) issues. The procurement authority must show whether a PPP proposal is the optimal solution that provides best VFM outcome. The paper presents a literature review of comparing quantitative VFM methodology for PPP infrastructure project procurement in Indonesia and Australia. Public Sector Comparator (PSC) is used to assess the potential project VFM quantitatively in Australia. In Indonesia, the PSC has not been applied, where the PPP procurement authority tends to utilize a common project evaluation method that ignores the issues of risk. Unlike the conventional price bid evaluation, the PSC enables a financial comparison including costs/gains and risks. Since the construction of PSC is primarily on risk management approach, it can facilitate risk negotiation processes between the involved parties. The study indicates that the quantitative VFM methodology of PSC is potentially applicable in Indonesia for water supply sector. Various supporting regulations are available that emphasize the importance of VFM and risk management in infrastructure investment. However, the study also reveals a number of challenges that need to be anticipated, such as the need of a more comprehensive PPP policy at both central and local government level, a more specific legal instrument for bidding evaluation method and the issue of institutional capacity development in PPP Units at the local level.

Ways of telling : research methodology for fashion textiles design

Research methodology in the discipline of Art & Design has been a topic for much debate in the academic community. The result of such avid and ongoing discussion appears to be a disciplinary obsession with research methodologies and a culture of adopting and adapting existing methodologies from more established disciplines. This has eventuated as a means of coping with academic criticism and as an attempt to elevate Art & Design to a ‘real academic status’. Whilst this adoption has had some effect in tempering the opinion of Art & Design research from more ‘serious’ academics the practice may be concealing a deeper problem for this discipline. Namely, that knowledge transfer within creative practice, particularly in fashion textiles design practice, is largely tacit in nature and not best suited to dissemination through traditional means of academic writing and publication. ----- ----- There is an opportunity to shift the academic debate away from appropriate (or inappropriate) use of methodologies and theories to demonstrate the existence (or absence) of rigor in creative practice research. In particular, the changing paradigms for the definitions of research to support new models for research quality assessment (such as the RAE in the United Kingdom and ERA in Australia) require a re-examination of the traditions of academic writing and publication in relation to this form of research. It is now appropriate to test the limits of tacit knowledge. It has been almost half a century since Michael Polanyi wrote “we know more than we can tell” (Polanyi, 1967 p.4) at a time when the only means of ‘telling’ was through academic writing and publishing in hardcopy format. ----- ----- This paper examines the academic debate surrounding research methodologies for fashion textiles design through auto-ethnographic case study and object analysis. The author argues that, while this debate is interesting, the focus should be to ask: are there more effective ways for creative practitioner researchers to disseminate their research? The aim of this research is to examine the possibilities of developing different, more effective methods of ‘telling’ to support the transfer of tacit knowledge inherent in the discipline of Fashion Textiles Design.

Queer cops in Queensland : exploring LGBT narrative histories in the Queensland Police Service

This chapter reports on a narrative project recording the experiences of LGBT former and current police officers in the Queensland Police Service (QPS), Australia. It begins by examining the historical and research contexts of LGBT police officers, followed by a discussion of the methodology employed for the project. The chapter then examines and analyzes key themes emerging from the data about coming out, macho police culture, and the double life syndrome often experienced by LGBT police officers. Finally, it suggests that further research might uncover a more widespread application of these findings.

Identifying relationship between skid resistance, road characteristics and crashes using probability-based risk approach

Road accidents are of great concerns for road and transport departments around world, which cause tremendous loss and dangers for public. Reducing accident rates and crash severity are imperative goals that governments, road and transport authorities, and researchers are aimed to achieve. In Australia, road crash trauma costs the nation A$ 15 billion annually. Five people are killed, and 550 are injured every day. Each fatality costs the taxpayer A$1.7 million. Serious injury cases can cost the taxpayer many times the cost of a fatality. Crashes are in general uncontrolled events and are dependent on a number of interrelated factors such as driver behaviour, traffic conditions, travel speed, road geometry and condition, and vehicle characteristics (e.g. tyre type pressure and condition, and suspension type and condition). Skid resistance is considered one of the most important surface characteristics as it has a direct impact on traffic safety. Attempts have been made worldwide to study the relationship between skid resistance and road crashes. Most of these studies used the statistical regression and correlation methods in analysing the relationships between skid resistance and road crashes. The outcomes from these studies provided mix results and not conclusive. The objective of this paper is to present a probability-based method of an ongoing study in identifying the relationship between skid resistance and road crashes. Historical skid resistance and crash data of a road network located in the tropical east coast of Queensland were analysed using the probability-based method. Analysis methodology and results of the relationships between skid resistance, road characteristics and crashes are presented.

A methodology for object identification and tracking in construction based on spatial modeling and image matching techniques

Understanding the motion characteristics of on-site objects is desirable for the analysis of construction work zones, especially in problems related to safety and productivity studies. This article presents a methodology for rapid object identification and tracking. The proposed methodology contains algorithms for spatial modeling and image matching. A high-frame-rate range sensor was utilized for spatial data acquisition. The experimental results indicated that an occupancy grid spatial modeling algorithm could quickly build a suitable work zone model from the acquired data. The results also showed that an image matching algorithm is able to find the most similar object from a model database and from spatial models obtained from previous scans. It is then possible to use the matched information to successfully identify and track objects.

Machine prognostics based on health state probability estimation

The ability to accurately predict the remaining useful life of machine components is critical for machine continuous operation and can also improve productivity and enhance system’s safety. In condition-based maintenance (CBM), maintenance is performed based on information collected through condition monitoring and assessment of the machine health. Effective diagnostics and prognostics are important aspects of CBM for maintenance engineers to schedule a repair and to acquire replacement components before the components actually fail. Although a variety of prognostic methodologies have been reported recently, their application in industry is still relatively new and mostly focused on the prediction of specific component degradations. Furthermore, they required significant and sufficient number of fault indicators to accurately prognose the component faults. Hence, sufficient usage of health indicators in prognostics for the effective interpretation of machine degradation process is still required. Major challenges for accurate longterm prediction of remaining useful life (RUL) still remain to be addressed. Therefore, continuous development and improvement of a machine health management system and accurate long-term prediction of machine remnant life is required in real industry application. This thesis presents an integrated diagnostics and prognostics framework based on health state probability estimation for accurate and long-term prediction of machine remnant life. In the proposed model, prior empirical (historical) knowledge is embedded in the integrated diagnostics and prognostics system for classification of impending faults in machine system and accurate probability estimation of discrete degradation stages (health states). The methodology assumes that machine degradation consists of a series of degraded states (health states) which effectively represent the dynamic and stochastic process of machine failure. The estimation of discrete health state probability for the prediction of machine remnant life is performed using the ability of classification algorithms. To employ the appropriate classifier for health state probability estimation in the proposed model, comparative intelligent diagnostic tests were conducted using five different classifiers applied to the progressive fault data of three different faults in a high pressure liquefied natural gas (HP-LNG) pump. As a result of this comparison study, SVMs were employed in heath state probability estimation for the prediction of machine failure in this research. The proposed prognostic methodology has been successfully tested and validated using a number of case studies from simulation tests to real industry applications. The results from two actual failure case studies using simulations and experiments indicate that accurate estimation of health states is achievable and the proposed method provides accurate long-term prediction of machine remnant life. In addition, the results of experimental tests show that the proposed model has the capability of providing early warning of abnormal machine operating conditions by identifying the transitional states of machine fault conditions. Finally, the proposed prognostic model is validated through two industrial case studies. The optimal number of health states which can minimise the model training error without significant decrease of prediction accuracy was also examined through several health states of bearing failure. The results were very encouraging and show that the proposed prognostic model based on health state probability estimation has the potential to be used as a generic and scalable asset health estimation tool in industrial machinery.

An obsession with storytelling : conducting oral history interviews for creative writing

Anna Hirsch and Clare Dixon (2008, 190) state that creative writers’ ‘obsession with storytelling…might serve as an interdisciplinary tool for evaluating oral histories.’ This paper enters a dialogue with Hirsch and Dixon’s statement by documenting an interview methodology for a practice-led PhD project, The Artful Life Story: Oral History and Fiction, which investigates the fictionalising of oral history. ----- ----- Alistair Thomson (2007, 62) notes the interdisciplinary nature of oral history scholarship from the 1980s onwards. As a result, oral histories are being used and understood in a variety of arts-based settings. In such contexts, oral histories are not valued so much for their factual content but as sources that are at once dynamic, emotionally authentic and open to a multiplicity of interpretations. How can creative writers design and conduct interviews that reflect this emphasis? ----- ----- The paper briefly maps the growing trend of using oral histories in fiction and ethnographic novels, in order to establish the need to design interviews for arts-based contexts. I describe how I initially designed the interviews to suit the aims of my practice. Once in the field, however, I found that my original methods did not account for my experiences. I conclude with the resulting reflection and understanding that emerged from these problematic encounters, focusing on the technique of steered monologue (Scagliola 2010), sometimes referred to as the Biographic Narrative Interpretative Method (Wengraf 2001, Jones 2006).

An assessment of timber dwellings typical of the Queenslander era, and constraints associated with their relocation, component and adaptive reuse when considering 'regeneration'

This paper presents an explanation of why the reuse of building components after demolition or deconstruction is critical to the future of the construction industry. An examination of the historical cause and response to climate change sets the scene as to why governance is becoming increasingly focused on the built environment as a mechanism to controlling waste generation associated with the process of demolition, construction and operation. Through an annotated description to the evolving design and construction methodology of a range of timber dwellings (typically 'Queenslanders' during the eras of 1880-1900, 1900-1920 & 1920-1940) the paper offers an evaluation to the variety of materials, which can be used advantageously by those wishing to 'regenerate' a Queenslander. This analysis of 'regeneration' details the constraints when considering relocation and/ or reuse by adaption including deconstruction of building components against the legislative framework requirements of the Queensland Building Act 1975 and the Queensland Sustainable Planning Act 2009, with a specific examination to those of the Building Codes of Australia. The paper concludes with a discussion of these constraints, their impacts on 'regeneration' and the need for further research to seek greater understanding of the practicalities and drivers of relocation, adaptive and building components suitability for reuse after deconstruction.