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The Wiener-Hammerstein nonlinear system is made up of two dynamic linear subsystems in series with a static nonlinear subsystem, and it is widely used in electrical, mechanical, aerospace and other fields. This paper considers the parameter estimation of the Wiener-Hammerstein output error moving average (OEMA) system.

The idea of multi-population and parameter self-adaptive identification is introduced, and a multi-population self-adaptive differential evolution (MPSADE) algorithm is proposed. In order to confirm the feasibility of the above method, the differential evolution (DE), the self-adaptive differential evolution (SADE), the MPSADE and the gradient iterative (GI) algorithms are derived to identify the Wiener-Hammerstein OEMA system, respectively.

From the simulation results, the authors find that the estimation errors under the four algorithms stabilize after 120, 30, 20 and 300 iterations, respectively, and the estimation errors of the four algorithms converge to 5.0%, 3.6%, 2.7% and 7.3%, which show that all four algorithms can identify the Wiener-Hammerstein OEMA system.

Compared with DE, SADE and GI algorithm, the MPSADE algorithm not only has higher parameter estimation accuracy but also has a faster convergence speed. Finally, the input–output relationship of laser welding system is described and identified by the MPSADE algorithm. The simulation results show that the MPSADE algorithm can effectively identify parameters of the laser welding system.

This study aims to investigate clients' attributes, their key decision variables and causal relationships between the decision variables. In addition, the purpose of the study is to map-out from these analyses, the attributes of project clients that motivate contractors' bid decision.

A total of 50 responses were obtained from a questionnaire survey. 50% of participants are contractors. 44% are claims consultants, whilst 6% are manufacturers and clients. Beyond measures of central tendencies, analysis focussed on causal relationships by way of correlation, analysis of variance and reductionism.

All 20 factors considered have significant correlations with at least one other factor. Findings also show the factors can be clustered into six: reputation, financial strength, relationship with the bidder, organisational attributes, history with project attributes and project organisation.

Evidence suggests stakeholders have often struggled to consider the many decision factors reported in normative literature, numbering hundreds. By clustering the factors into sub-themes, bid decisioning has been made more efficient. The study also explains how client attributes could determine project success and contractor participation. Different stakeholders can use findings of this study for training and further studies.

Previous studies have considered bid decisioning indexically – factors were long, analyses were largely inconclusive, and causal relationships are orthogonal. Findings in this study have shown depth: 20 originating client-specific factors were clustered into six sub-themes, and correlations were established. The methodology used for the study is confirmatory and conclusive.

The purpose of this paper is to elicit the activities in geometric 3D computer-aided design (CAD) estimating. Construction estimators usually target the structural integrity of data underlying project designs while measuring quantities and developing estimates. However, there are different ways to this. There is considerable evidence to suggest substantial distinction between data structuring in geometric and parametric CAD (building information modelling). Each of these platforms also appeals to estimators in the various practice domains differently. Regardless, the developments in the use of geometric and parametric CAD for design and management purposes have been rapid.

The study focuses on the various perspectives within the different construction business domains. Interviews, focus group discussions and direct observation methods were used to explore data on estimating activities in 3D CAD from two public organizations, two large contracting firms, two quantity surveying consulting practices, two specialist-project companies and four software development and vending firms. These involved 17 middle-top management estimators who have had extensive experience in the industry. As the activities were elicited, participants were able to ascribe relative importance to each of the activities, and these were logically compared across the different practice domains.

Thirty-one activities were identified as the components of estimators’ procedures leading to reliable outcomes in estimating 3D CAD designs. Logical correlations were discussed through extant literature towards forming a centroid model which could be used for numerous industry applications, including software development, knowledge transfer between organizations, employees’ hands-on training, curriculum design for academic institutions and as a policy framework for professional institutions on estimating practice. Further areas of research were also highlighted.

This work is an original piece. It is neither published nor under consideration elsewhere.

This paper aims to examine the influence of big data (BD) on human resource management (HRM). It defines how these data can be a useful tool in the decision-making process of companies’ human resources to obtain a sustainable competitive advantage.

This paper emphasizes the need to develop a holistic approach to emphasize these relations. Starting from these observations, the document proposes empirical research employing Eurostat data to test the benefits of BD in HRM decisions that optimize the relationship between training, productivity, and well-being.

The findings estimate HRM decisions and their impact in a broader macroeconomic and microeconomic perspective.

BD research is emerging as a crucial discipline in human resources. To overcome this problem, the paper develops an analysis of the literature on cleaner production and sustainability context; it creates a conceptual framework to clarify whether the existing studies consider the growing intensity of BD on human resources.