The automatic layout model in the sequencing of construction activities of multi-objective diaphragm wall unit by using self-learning neural network

Foundation construction process has been an important key point in a successful construction engineering. The frequency of using diaphragm wall construction method among many deep excavation construction methods in Taiwan is the highest in the world. The traditional view of managing diaphragm wall unit in the sequencing of construction activities is to establish each phase of the sequencing of construction activities by heuristics. However, it conflicts final phase of engineering construction with unit construction and effects planning construction time. In order to avoid this kind of situation, we use management of science in the study of diaphragm wall unit construction to formulate multi-objective combinational optimization problem. Because the characteristic (belong to NP-Complete problem) of problem mathematic model is multi-objective and combining explosive, it is advised that using the 2-type Self-Learning Neural Network (SLNN) to solve the N=12, 24, 36 of diaphragm wall unit in the sequencing of construction activities program problem. In order to compare the liability of the results, this study will use random researching method in comparison with the SLNN. It is found that the testing result of SLNN is superior to random researching method in whether solution-quality or Solving-efficiency.

The future of lean construction: a brave new world

Lean construction is considered from a human resource management (HRM) perspective. It is contended that the UK construction sector is characterised by an institutionalised regressive approach to HRM. In the face of rapidly declining recruitment rates for built environment courses, the dominant HRM philosophy of utilitarian instrumentalism does little to attract the intelligent and creative young people that the industry so badly needs. Given this broader context, there is a danger that an uncritical acceptance of lean construction will exacerbate the industry's reputation for unrewarding jobs. Construction academics have strangely ignored the extensive literature that equates lean production to a HRM regime of control, exploitation and surveillance. The emphasis of lean thinking on eliminating waste and improving efficiency makes it easy to absorb into the best practice agenda because it conforms to the existing dominant way of thinking. 'Best practice' is seemingly judged by the extent to which it serves the interests of the industry's technocratic elite. Hence it acts as a conservative force in favour of maintaining the status quo. In this respect, lean construction is the latest manifestation of a long established trend. In common with countless other improvement initiatives, the rhetoric is heavy in the machine metaphor whilst exhorting others to be more efficient. If current trends in lean construction are extrapolated into the future the ultimate destination may be uncomfortably close to Aldous Huxley's apocalyptic vision of a Brave New World. In the face of these trends, the lean construction research community pleads neutrality whilst confining its attention to the rational high ground. The future of lean construction is not yet predetermined. Many choices remain to be made. The challenge for the research community is to improve practice whilst avoiding the dehumanising tendencies of high utilitarianism.

Exploring the construction of social class in educational discourse: the rational order of the nation state vs. global uncertainties

This article aims to create intellectual space in which issues of social inequality and education can be analyzed and discussed in relation to the multifaceted and multi-levelled complexities of the modern world. It is divided into three sections. Section One locates the concept of social class in the context of the modern nation state during the period after the Second World War. Focusing particularly on the impact of ‘Fordism’ on social organization and cultural relations, it revisits the articulation of social justice issues in the United Kingdom, and the structures put into place at the time to alleviate educational and social inequalities. Section Two problematizes the traditional concept of social class in relation to economic, technological and sociocultural changes that have taken place around the world since the mid-1980s. In particular, it charts some of the changes to the international labour market and global patterns of consumption, and their collective impact on the re-constitution of class boundaries in ‘developed countries’. This is juxtaposed with some of the major social effects of neo-classical economic policies in recent years on the sociocultural base in developing countries. It discusses some of the ways these inequalities are reflected in education. Section Three explores tensions between the educational ideals of the ‘knowledge economy’ and the discursive range of social inequalities that are emerging within and beyond the nation state. Drawing on key motifs identified throughout, the article concludes with a reassessment of the concept of social class within the global cultural economy. This is discussed in relation to some of the major equity and human rights issues in education today.

PDM 2006: Proceedings of the International Workshop on Data Mining

Recently major processor manufacturers have announced a dramatic shift in their paradigm to increase computing power over the coming years. Instead of focusing on faster clock speeds and more powerful single core CPUs, the trend clearly goes towards multi core systems. This will also result in a paradigm shift for the development of algorithms for computationally expensive tasks, such as data mining applications. Obviously, work on parallel algorithms is not new per se but concentrated efforts in the many application domains are still missing. Multi-core systems, but also clusters of workstations and even large-scale distributed computing infrastructures provide new opportunities and pose new challenges for the design of parallel and distributed algorithms. Since data mining and machine learning systems rely on high performance computing systems, research on the corresponding algorithms must be on the forefront of parallel algorithm research in order to keep pushing data mining and machine learning applications to be more powerful and, especially for the former, interactive. To bring together researchers and practitioners working in this exciting field, a workshop on parallel data mining was organized as part of PKDD/ECML 2006 (Berlin, Germany). The six contributions selected for the program describe various aspects of data mining and machine learning approaches featuring low to high degrees of parallelism: The first contribution focuses the classic problem of distributed association rule mining and focuses on communication efficiency to improve the state of the art. After this a parallelization technique for speeding up decision tree construction by means of thread-level parallelism for shared memory systems is presented. The next paper discusses the design of a parallel approach for dis- tributed memory systems of the frequent subgraphs mining problem. This approach is based on a hierarchical communication topology to solve issues related to multi-domain computational envi- ronments. The forth paper describes the combined use and the customization of software packages to facilitate a top down parallelism in the tuning of Support Vector Machines (SVM) and the next contribution presents an interesting idea concerning parallel training of Conditional Random Fields (CRFs) and motivates their use in labeling sequential data. The last contribution finally focuses on very efficient feature selection. It describes a parallel algorithm for feature selection from random subsets. Selecting the papers included in this volume would not have been possible without the help of an international Program Committee that has provided detailed reviews for each paper. We would like to also thank Matthew Otey who helped with publicity for the workshop.

Do national cultural differences affect the nature and characteristics of HRM practices? Evidence from Australian and Hong Kong construction firms on remuneration and job autonomy

Human resource management (HRM) plays a pivotal role in attracting and retaining talents. However, there is growing recognition in international HRM literature that the adoption of the widely accepted US/Harvard-inspired HRM model ignores the influences of cultural contexts on HRM practices in different countries. This notion has not been empirically investigated in the construction industry. Based on survey responses from 604 construction professionals from Australia and Hong Kong, this study examines whether: (i) national cultural differences influence individuals’ preference for types of remuneration and job autonomy, (ii) actual organizational HRM practices reflect such preferences and (iii) gaps between individuals’ preferences and actual organizational HRM practices affect job satisfaction. Results showed significant difference in HRM preferences between Australian and Hong Kong respondents and these are reflected in the distinct types of HRM practices adopted by construction firms in the two countries. Findings further indicated that the gap between individuals’ preferences and actual organizational HRM practices is associated with job satisfaction. The results support existing mainstream research and highlight the deficiency of the acultural treatment of HRM that is still apparent in construction management literature. An uncritical literature in the area not only hinders theory development but also potentially undermines the ability of construction firms to attract, recruit, and retain scarce talents.

Corporate social responsibility in construction and engineering firms: key elements of effective strategy

Through multiple case studies of firms we argue that firms that have developed corporate responsibility strategies, albeit informally at first, do so by making intentional, informed and collective choices about CSR initiatives. More precisely, we point to the importance of considering corporate identity in making these choices and to the process of adaptive coordination, which includes both responding to and influencing the CSR environment. We conclude that CSR strategic landscape are determined more and more by the astute and careful management of a network of cooperative and competitive stakeholder interests which possess both tangible and intangible value to a firm.