Modelling the construction projects using plans of work

An overview of organization in the construction industry is identified from plans of work published in the UK. This provides a basis for identifying the essential steps through which any construction project must pass. It is shown that all construction projects pass through a set of stages of work, consisting of inception, feasibility, scheme design, detail design, contract formation, construction and commissioning. Although there may be changes to the sequence and importance of these stages, their identification helps in making judgements about organizational structure on construction projects.

UK construction orders and output: A comparison of government and commercially available data 1995-2001

Because of the importance and potential usefulness of construction market statistics to firms and government, consistency between different sources of data is examined with a view to building a predictive model of construction output using construction data alone. However, a comparison of Department of Trade and Industry (DTI) and Office for National Statistics (ONS) series shows that the correlation coefcient (used as a measure of consistency) of the DTI output and DTI orders data and the correlation coefficient of the DTI output and ONS output data are low. It is not possible to derive a predictive model of DTI output based on DTI orders data alone. The question arises whether or not an alternative independent source of data may be used to predict DTI output data. Independent data produced by Emap Glenigan (EG), based on planning applications, potentially offers such a source of information. The EG data records the value of planning applications and their planned start and finish dates. However, as this data is ex ante and is not correlated with DTI output it is not possible to use this data to describe the volume of actual construction output. Nor is it possible to use the EG planning data to predict DTI construc-tion orders data. Further consideration of the issues raised reveal that it is not practically possible to develop a consistent predictive model of construction output using construction statistics gathered at different stages in the development process.

Emerging hybrid practices in construction design work: role of mixed media

Information technologies are used across all stages of the construction process, and are crucial in the delivery of large projects. Drawing on detailed research on a construction megaproject, we take a practice-based approach to examining the practical and theoretical tensions between existing ways of working and the introduction of new coordination tools in this paper. We analyze the new hybrid practices that emerge, using insights from actor-network theory to articulate the delegation of actions to material and digital objects within ecologies of practice. The three vignettes that we discuss highlight this delegation of actions, the “plugging” and “patching” of ecologies occurring across media and the continual iterations of working practices between different types of media. By shifting the focus from tools to these wider ecologies of practice, the approach has important managerial mplications for the stabilization of new technologies and practices and for managing technological change on large construction projects. We conclude with a discussion of new directions for research, oriented to further elaborating on the importance of the material in understanding change.

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.

Understanding the early stages of the innovation diffusion process: awareness, influence and communication networks

The themes of awareness and influence within the innovation diffusion process are addressed. The innovation diffusion process is typically represented as stages, yet awareness and influence are somewhat under-represented in the literature. Awareness and influence are situated within the contextual setting of individual actors but also within the broader institutional forces. Understanding how actors become aware of an innovation and then how their opinion is influenced is important for creating a more innovation-active UK construction sector. Social network analysis is proposed as one technique for mapping how awareness and influence occur and what they look like as a network. Empirical data are gathered using two modes of enquiry. This is done through a pilot study consisting of chartered professionals and then through a case study organization as it attempted to diffuse an innovation. The analysis demonstrates significant variations across actors’ awareness and influence networks. It is argued that social network analysis can complement other research methods in order to present a richer picture of how actors become aware of innovations and where they draw their influences regarding adopting innovations. In summarizing the findings, a framework for understanding awareness and influence associated with innovation within the UK construction sector is presented. Finally, with the UK construction sector continually being encouraged to be innovative, understanding and managing an actor’s awareness and influence network will be beneficial. The overarching conclusion thus describes the need not only to build research capacity in this area but also to push the boundaries related to the research methods employed.

Understanding absorptive capacity in Malaysian small and medium sized (SME) construction companies

Purpose – The construction industry is a very important part of the Malaysian economy. The government's aim is to make the industry more productive, efficient and safe. Small to medium-sized enterprises (SMEs) are at the core of the Malaysian construction industry and account for about 90 per cent of companies undertaking construction work. One of the main challenges faced by the Malaysian construction industry is the ability to absorb new knowledge and technology and to implement it in the construction phase. The purpose of this paper is to consider absorptive capacity in Malaysian construction SMEs in rural areas. Design/methodology/approach – The research was conducted in three stages: first, understanding the Malaysian construction industry; second, a literature review on the issues related to absorptive capacity and discussions with the Construction Industry Development Board (CIDB); and third, multiple case studies in five construction SMEs operating in a rural area to validate the factors influencing absorptive capacity. Findings – Nine key factors were identified influencing absorptive capacity in Malaysian construction SMEs operating in rural areas. These factors involved: cost and affordability; availability and supply; demand; infrastructure; policies and regulations; labour readiness; workforce attitude and motivation; communication and sources of new knowledge and; culture. Originality/value – The key factors influencing absorptive capacity presented in this paper are based on validation from the case studies in five construction SMEs in Malaysia. The research focuses on how they operate in rural areas; however, the research results have wider application than just Malaysia. The key factors identified as influencing absorptive capacity can serve as a basis for considering knowledge absorption in the wider context by SMEs in other developing countries.

Construction of neurofuzzy models for imbalanced data classification

We propose a new class of neurofuzzy construction algorithms with the aim of maximizing generalization capability specifically for imbalanced data classification problems based on leave-one-out (LOO) cross validation. The algorithms are in two stages, first an initial rule base is constructed based on estimating the Gaussian mixture model with analysis of variance decomposition from input data; the second stage carries out the joint weighted least squares parameter estimation and rule selection using orthogonal forward subspace selection (OFSS)procedure. We show how different LOO based rule selection criteria can be incorporated with OFSS, and advocate either maximizing the leave-one-out area under curve of the receiver operating characteristics, or maximizing the leave-one-out Fmeasure if the data sets exhibit imbalanced class distribution. Extensive comparative simulations illustrate the effectiveness of the proposed algorithms.