Understanding construction consortia: Theory, practice and opinions

Firms form consortia in order to win contracts. Once a project has been awarded to a consortium each member then concentrates on his or her own contract with the client. Therefore, consortia are marketing devices, which present the impression of teamworking, but the production process is just as fragmented as under conventional procurement methods. In this way, the consortium forms a barrier between the client and the actual construction production process. Firms form consortia, not as a simple development of normal ways of working, but because the circumstances for specific projects make it a necessary vehicle. These circumstances include projects that are too large or too complex to undertake alone or projects that require on-going services which cannot be provided by the individual firms inhouse. It is not a preferred way of working, because participants carry extra risk in the form of liability for the actions of their partners in the consortium. The behaviour of members of consortia is determined by their relative power, based on several factors, including financial commitment and ease of replacement. The level of supply chain visibility to the public sector client and to the industry is reduced by the existence of a consortium because the consortium forms an additional obstacle between the client and the firms undertaking the actual construction work. Supply chain visibility matters to the client who otherwise loses control over the process of construction or service provision, while remaining accountable for cost overruns. To overcome this separation there is a convincing argument in favour of adopting the approach put forward in the Project Partnering Contract 2000 (PPC2000) Agreement. Members of consortia do not necessarily go on to work in the same consortia again because members need to respond flexibly to opportunities as and when they arise. Decision-making processes within consortia tend to be on an ad hoc basis. Construction risk is taken by the contractor and the construction supply chain but the reputational risk is carried by all the firms associated with a consortium. There is a wide variation in the manner that consortia are formed, determined by the individual circumstances of each project; its requirements, size and complexity, and the attitude of individual project leaders. However, there are a number of close working relationships based on generic models of consortia-like arrangements for the purpose of building production, such as the Housing Corporation Guidance Notes and the PPC2000.

A sparse kernel density estimation algorithm using forward constrained regression

Using the classical Parzen window (PW) estimate as the target function, the sparse kernel density estimator is constructed in a forward constrained regression manner. The leave-one-out (LOO) test score is used for kernel selection. The jackknife parameter estimator subject to positivity constraint check is used for the parameter estimation of a single parameter at each forward step. As such the proposed approach is simple to implement and the associated computational cost is very low. An illustrative example is employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with comparable accuracy to that of the classical Parzen window estimate.

Probability density function estimation using orthogonal forward regression

Using the classical Parzen window estimate as the target function, the kernel density estimation is formulated as a regression problem and the orthogonal forward regression technique is adopted to construct sparse kernel density estimates. The proposed algorithm incrementally minimises a leave-one-out test error score to select a sparse kernel model, and a local regularisation method is incorporated into the density construction process to further enforce sparsity. The kernel weights are finally updated using the multiplicative nonnegative quadratic programming algorithm, which has the ability to reduce the model size further. Except for the kernel width, the proposed algorithm has no other parameters that need tuning, and the user is not required to specify any additional criterion to terminate the density construction procedure. Two examples are used to demonstrate the ability of this regression-based approach to effectively construct a sparse kernel density estimate with comparable accuracy to that of the full-sample optimised Parzen window density estimate.

Kernel density construction using orthogonal forward regression

An automatic algorithm is derived for constructing kernel density estimates based on a regression approach that directly optimizes generalization capability. Computational efficiency of the density construction is ensured using an orthogonal forward regression, and the algorithm incrementally minimizes the leave-one-out test score. Local regularization is incorporated into the density construction process to further enforce sparsity. Examples are included to demonstrate the ability of the proposed algorithm to effectively construct a very sparse kernel density estimate with comparable accuracy to that of the full sample Parzen window density estimate.

Sparse kernel density construction using orthogonal forward regression with leave-one-out test score and local regularization

This paper presents an efficient construction algorithm for obtaining sparse kernel density estimates based on a regression approach that directly optimizes model generalization capability. Computational efficiency of the density construction is ensured using an orthogonal forward regression, and the algorithm incrementally minimizes the leave-one-out test score. A local regularization method is incorporated naturally into the density construction process to further enforce sparsity. An additional advantage of the proposed algorithm is that it is fully automatic and the user is not required to specify any criterion to terminate the density construction procedure. This is in contrast to an existing state-of-art kernel density estimation method using the support vector machine (SVM), where the user is required to specify some critical algorithm parameter. Several examples are included to demonstrate the ability of the proposed algorithm to effectively construct a very sparse kernel density estimate with comparable accuracy to that of the full sample optimized Parzen window density estimate. Our experimental results also demonstrate that the proposed algorithm compares favorably with the SVM method, in terms of both test accuracy and sparsity, for constructing kernel density estimates.

Orthogonal forward selection for constructing the radial basis function network with tunable nodes

An orthogonal forward selection (OFS) algorithm based on the leave-one-out (LOO) criterion is proposed for the construction of radial basis function (RBF) networks with tunable nodes. This OFS-LOO algorithm is computationally efficient and is capable of identifying parsimonious RBF networks that generalise well. Moreover, the proposed algorithm is fully automatic and the user does not need to specify a termination criterion for the construction process.