Automatic nonlinear predictive model-construction algorithm using forward regression and the PRESS statistic

An automatic nonlinear predictive model-construction algorithm is introduced based on forward regression and the predicted-residual-sums-of-squares (PRESS) statistic. The proposed algorithm is based on the fundamental concept of evaluating a model's generalisation capability through crossvalidation. This is achieved by using the PRESS statistic as a cost function to optimise model structure. In particular, the proposed algorithm is developed with the aim of achieving computational efficiency, such that the computational effort, which would usually be extensive in the computation of the PRESS statistic, is reduced or minimised. The computation of PRESS is simplified by avoiding a matrix inversion through the use of the orthogonalisation procedure inherent in forward regression, and is further reduced significantly by the introduction of a forward-recursive formula. Based on the properties of the PRESS statistic, the proposed algorithm can achieve a fully automated procedure without resort to any other validation data set for iterative model evaluation. Numerical examples are used to demonstrate the efficacy of the algorithm.

Understanding UK construction professional services exports: definitions and characteristics

Services are very important to the UK balance of trade; a surplus has been recorded for trade in services every year since 1966. Construction professional services exports (CPS), which cover architecture, engineering and surveying (AES), have also increased, contributing over £3bn to the UK trade balance in 2007. The changing environment of construction professional services exports complicates the validity of the characteristics and definitions of services as described in the research literature and official export statistics. Through semi-structured interviews undertaken with large consulting engineers and a round-table discussion with industry and government representatives, the research found that the impact of globalisation and the changes in the construction business environment, such as increasing foreign ownership and changing forms of procurement, are not fully reflected in the official statistics. There have also been rapid changes in technology, procurement and methods of delivery which have impacted exporting AES firms and a more appropriate set of characteristics is needed to better reflect the project-specific and knowledge-intensive nature of AES firms.

A mixture of experts network structure construction algorithm for modelling and control

This paper introduces a new fast, effective and practical model structure construction algorithm for a mixture of experts network system utilising only process data. The algorithm is based on a novel forward constrained regression procedure. Given a full set of the experts as potential model bases, the structure construction algorithm, formed on the forward constrained regression procedure, selects the most significant model base one by one so as to minimise the overall system approximation error at each iteration, while the gate parameters in the mixture of experts network system are accordingly adjusted so as to satisfy the convex constraints required in the derivation of the forward constrained regression procedure. The procedure continues until a proper system model is constructed that utilises some or all of the experts. A pruning algorithm of the consequent mixture of experts network system is also derived to generate an overall parsimonious construction algorithm. Numerical examples are provided to demonstrate the effectiveness of the new algorithms. The mixture of experts network framework can be applied to a wide variety of applications ranging from multiple model controller synthesis to multi-sensor data fusion.

Neurofuzzy design and model construction of nonlinear dynamical processes from data

A common problem in many data based modelling algorithms such as associative memory networks is the problem of the curse of dimensionality. In this paper, a new two-stage neurofuzzy system design and construction algorithm (NeuDeC) for nonlinear dynamical processes is introduced to effectively tackle this problem. A new simple preprocessing method is initially derived and applied to reduce the rule base, followed by a fine model detection process based on the reduced rule set by using forward orthogonal least squares model structure detection. In both stages, new A-optimality experimental design-based criteria we used. In the preprocessing stage, a lower bound of the A-optimality design criterion is derived and applied as a subset selection metric, but in the later stage, the A-optimality design criterion is incorporated into a new composite cost function that minimises model prediction error as well as penalises the model parameter variance. The utilisation of NeuDeC leads to unbiased model parameters with low parameter variance and the additional benefit of a parsimonious model structure. Numerical examples are included to demonstrate the effectiveness of this new modelling approach for high dimensional inputs.

Neurofuzzy network model construction using Bézier-Bernstein polynomial functions

Neurofuzzy modelling systems combine fuzzy logic with quantitative artificial neural networks via a concept of fuzzification by using a fuzzy membership function usually based on B-splines and algebraic operators for inference, etc. The paper introduces a neurofuzzy model construction algorithm using Bezier-Bernstein polynomial functions as basis functions. The new network maintains most of the properties of the B-spline expansion based neurofuzzy system, such as the non-negativity of the basis functions, and unity of support but with the additional advantages of structural parsimony and Delaunay input space partitioning, avoiding the inherent computational problems of lattice networks. This new modelling network is based on the idea that an input vector can be mapped into barycentric co-ordinates with respect to a set of predetermined knots as vertices of a polygon (a set of tiled Delaunay triangles) over the input space. The network is expressed as the Bezier-Bernstein polynomial function of barycentric co-ordinates of the input vector. An inverse de Casteljau procedure using backpropagation is developed to obtain the input vector's barycentric co-ordinates that form the basis functions. Extension of the Bezier-Bernstein neurofuzzy algorithm to n-dimensional inputs is discussed followed by numerical examples to demonstrate the effectiveness of this new data based modelling approach.

The changing nature of UK construction professional service firms

Professional services firms (PSFs) have been the subject of much attention in the literature in recent years, ranging across a number of distinct but related disciplines including economics, sociology, organization and management studies. Analysis has tended to concentrate on law and accounting firms in particular, and although there is a growing academic interest in construction/built environment professional services firms (CPSFs), these have received much less scrutiny. However, many of the changes taking place among PSFs – in particular, growth in firm size, moves towards external ownership and greater service/geographical diversification – are also taking place among the larger CPSFs. The CPSF sector is not especially well documented and there is little understanding of the motives for, and implications of, these changes in the firms, their clients and wider society. CPSFs are reviewed in the context of the more general PSF literature and a set of questions is posed for future research on CPSFs. These questions include the need to understand the implications of firm type on performance, the form of ownership that might confer the greatest financial benefits for different stakeholder groups, and the wider societal consequences of continuing growth in size and diversification of CPSFs.

Sparse model construction using coordinate descent optimization

We propose a new sparse model construction method aimed at maximizing a model’s generalisation capability for a large class of linear-in-the-parameters models. The coordinate descent optimization algorithm is employed with a modified l1- penalized least squares cost function in order to estimate a single parameter and its regularization parameter simultaneously based on the leave one out mean square error (LOOMSE). Our original contribution is to derive a closed form of optimal LOOMSE regularization parameter for a single term model, for which we show that the LOOMSE can be analytically computed without actually splitting the data set leading to a very simple parameter estimation method. We then integrate the new results within the coordinate descent optimization algorithm to update model parameters one at the time for linear-in-the-parameters models. Consequently a fully automated procedure is achieved without resort to any other validation data set for iterative model evaluation. Illustrative examples are included to demonstrate the effectiveness of the new approaches.

Performance-based contracting in the construction sector

Construction procurement is complex and there is a very wide range of options available to procurers. Inappropriate choices about how to procure may limit practical opportunities for innovation. In particular, traditional approaches to construction procurement set up many obstacles for technology suppliers to provide innovative solutions. This is because they are often employed as sub-contractors simply to provide and install equipment to specifications developed before the point at which they become involved in a project. A research team at the University of Reading has developed a procurement framework that comprehensively defines the various options open to procurers in a more fine-grained way than has been known in the past. This enables informed decisions that can establish tailor-made procurement approaches that take into account the needs of specific clients. It enables risk and reward structures to be aligned so that contracts and payment mechanisms are aligned precisely with what a client seeks to achieve. This is not a “one-size-fits-all” approach. Rather, it is an approach that enables informed decisions about how to organize individual procurements that are appropriate to particular circumstances, acknowledging that they differ for each client and for each procurement exercise. Within this context, performance-based contracting (PBC) is explored in terms of the different ways in which technology suppliers within constructed facilities might be encouraged and rewarded for the kinds of innovation sought by the ultimate clients. Examples from various industry sectors are presented, from public sector and from private sector, with a commentary about what they sought to achieve and the extent to which they were successful. The lessons from these examples are presented in terms of feasibility in relation to financial issues, governance, economics, strategic issues, contractual issues and cash flow issues for clients and for contractors. Further background documents and more detailed readings are provided in an appendix for those who wish to find out more.

Unified framework for construction of rule based classification systems

Automatic generation of classification rules has been an increasingly popular technique in commercial applications such as Big Data analytics, rule based expert systems and decision making systems. However, a principal problem that arises with most methods for generation of classification rules is the overfit-ting of training data. When Big Data is dealt with, this may result in the generation of a large number of complex rules. This may not only increase computational cost but also lower the accuracy in predicting further unseen instances. This has led to the necessity of developing pruning methods for the simplification of rules. In addition, classification rules are used further to make predictions after the completion of their generation. As efficiency is concerned, it is expected to find the first rule that fires as soon as possible by searching through a rule set. Thus a suit-able structure is required to represent the rule set effectively. In this chapter, the authors introduce a unified framework for construction of rule based classification systems consisting of three operations on Big Data: rule generation, rule simplification and rule representation. The authors also review some existing methods and techniques used for each of the three operations and highlight their limitations. They introduce some novel methods and techniques developed by them recently. These methods and techniques are also discussed in comparison to existing ones with respect to efficient processing of Big Data.

Categorization and construction of rule based systems

Expert systems have been increasingly popular for commercial importance. A rule based system is a special type of an expert system, which consists of a set of ‘if-then‘ rules and can be applied as a decision support system in many areas such as healthcare, transportation and security. Rule based systems can be constructed based on both expert knowledge and data. This paper aims to introduce the theory of rule based systems especially on categorization and construction of such systems from a conceptual point of view. This paper also introduces rule based systems for classification tasks in detail.

Construction of the transcomplex numbers from the complex numbers

A geometrical construction of the transcomplex numbers was given elsewhere. Here we simplify the transcomplex plane and construct the set of transcomplex numbers from the set of complex numbers. Thus transcomplex numbers and their arithmetic arise as consequences of their construction, not by an axiomatic development. This simplifes transcom- plex arithmetic, compared to the previous treatment, but retains totality so that every arithmetical operation can be applied to any transcomplex number(s) such that the result is a transcomplex number. Our proof establishes the consistency of transcomplex and transreal arithmetic and establishes the expected containment relationships amongst transcomplex, complex, transreal and real numbers. We discuss some of the advantages the transarithmetics have over their partial counterparts.

Investigating the level of awareness of building assessment tools in the construction industry of Botswana

Environmental building assessment tools have been developed to measure how well or poorly a building is performing, or likely to perform, against a declared set of criteria, or environmental considerations, in order to achieve sustainability principles. Knowledge of environmental building assessment tools is therefore important for successful design and construction of environmentally friendly buildings for countries. The purpose of the research is to investigate the knowledge and level of awareness of environmental building assessment tools among industry practitioners in Botswana. One hundred and seven paper-based questionnaires were delivered to industry practitioners, including architects, engineers, quantity surveyors, real estate developers and academics. Users were asked what they know about building assessment, whether they have used any building assessment tool in the past, and what they perceive as possible barriers to the implementation of environmental building assessment tools in Botswana. Sixty five were returned and statistical analysis, using IBM SPSS V19 software, was used for analysis. Almost 85 per cent of respondents indicate that they are extremely or moderately aware of environmental design. Furthermore, the results indicate that 32 per cent of respondents have gone through formal training, which suggests ‘reasonable knowledge’. This however does not correspond with the use of the tools on the ground as 69 per cent of practitioners report never to have used any environmental building assessment tool in any project. The study highlights the need to develop an assessment tool for Botswana to enhance knowledge and further improve the level of awareness of environmental issues relating to building design and construction.

Faith and protection: the construction of hope by parents of children with leukemia and their oncologists

Background. Oncologists are criticized for fostering unrealistic hope in patients and families, but criticisms reflect a perspective that is oversimplified and “expert” guidance that is ambiguous or impractical. Our aim was to understand how pediatric oncologists manage parents' hope in practice and to evaluate how they address parents' needs. Methods. Participants were 53 parents and 12 oncologists whom they consulted across six U.K. centers. We audio recorded consultations approximately 1–2, 6, and 12 months after diagnosis. Parents were interviewed after each consultation to elicit their perspectives on the consultation and clinical relationship. Transcripts of consultations and interviews were analyzed qualitatively. Results. Parents needed hope in order to function effectively in the face of despair, and all wanted the oncologists to help them be hopeful. Most parents focused hope on the short term. They therefore needed oncologists to be authoritative in taking responsibility for the child's long-term survival while cushioning parents from information about longer-term uncertainties and being positive in providing information about short-term progress. A few parents who could not fully trust their oncologist were unable to hope. Conclusion. Oncologists' pivotal role in sustaining hope was one that parents gave them. Most parents' “faith” in the oncologist allowed them to set aside, rather than deny, their fears about survival while investing their hopes in short-term milestones. Oncologists' behavior generally matched parents' needs, contradicting common criticisms of oncologists. Nevertheless, oncologists need to identify and address the difficulty that some parents have in fully trusting the oncologist and, consequently, being hopeful.

Perspectives on the specification of Building Integrated Photovoltaic (BIPV) technology in construction projects

Innovative, low carbon technologies are already available for use in the construction of buildings, but the impact of their specification on construction projects is unclear. This exploratory research identifies issues which arise following the specification of BIPV in non-residential construction projects. Rather than treating the inclusion of a new technology as a technical problem, the research explores the issue from a socio-technical perspective to understand the accommodations which the project team makes and their effect on the building and the technology. The paper is part of a larger research project which uses a Social Construction of Technology Approach (SCOT) to explore the accommodations made to working practices and design when Building Integrated PhotoVoltaic (BIPV) technology is introduced. The approach explores how the requirements of the technology from different groups of actors (Relevant Social Groups or RSG's) give rise to problems and create solutions. As such it rejects the notion of a rational linear view of innovation diffusion; instead it suggests that the variety and composition of the Relevant Social Groups set the agenda for problem solving and solutions as the project progresses. The research explores the experiences of three people who have extensive histories of involvement with BIPV in construction, looks at how SCOT can inform our understanding of the issues involved and identifies themes and issues in the specification of BIPV on construction projects. A key finding concerns the alignment of inflection points at which interviewees have found themselves changing from one RSG to another as new problems and solutions are identified. The points at which they change RSG often occurred at points which mirror conventional construction categories (in terms of project specification, tender, design and construction).

Interactive learning in UK construction practice: examining the role of BIM process standards

From a construction innovation systems perspective, firms acquire knowledge from suppliers, clients, universities and institutional environment. Building information modelling (BIM) involves these firms using new process standards. To understand the implications on interactive learning using BIM process standards, a case study is conducted with the UK operations of a multinational construction firm. Data is drawn from: a) two workshops involving the firm and a wider industry group, b) observations of practice in the BIM core team and in three ongoing projects, c) 12 semi-structured interviews; and d) secondary publications. The firm uses a set of BIM process standards (IFC, PAS 1192, Uniclass, COBie) in its construction activities. It is also involved in a pilot to implement the COBie standard, supported by technical and management standards for BIM, such as Uniclass and PAS1192. Analyses suggest that such BIM process standards unconsciously shapes the firm's internal and external interactive learning processes. Internally standards allow engineers to learn from each through visualising 3D information and talking around designs with operatives to address problems during construction. Externally, the firm participates in trial and pilot projects involving other construction firms, government agencies, universities and suppliers to learn about the standard and access knowledge to solve its specific design problems. Through its BIM manager, the firm provides feedback to standards developers and information technology suppliers. The research contributes by articulating how BIM process standards unconsciously change interactive learning processes in construction practice. Further research could investigate these findings in the wider UK construction innovation system.