Purpose Despite long-term, sustained research and industry practice, predicting construction labour productivity (CLP) using existing factor and activity modelling approaches remains a challenge. The purpose of this paper is to first demonstrate the limited usefulness of activity models and then to propose a system model approach that integrates factor and activity models for better prediction of CLP. Design/methodology/approach The system model parameters – comprising factors and practices – and work sampling proportions (WSPs) were identified from literature. Field data were collected from 11 projects over a span of 29 months. Activity models based on the relationship between CLP and WSPs were created, and their validity was tested using regression analysis for eight activities in the concreting, electrical and shutdown categories. The proposed system model was developed for concreting activity using the key influencing parameters in conjunction with WSPs. Findings The results of the regression analysis indicate that WSPs, like direct work, are not significantly correlated to CLP and fail to explain its variance. Evaluation of the system model approach for the concreting activity showed improved CLP prediction as compared to existing approaches. Research limitations/implications The system model was tested for concreting activity using data collected from six projects; however, further investigation into the model’s accuracy and efficacy using data collected from other labour-intensive activities is suggested. Originality/value This research establishes the role of WSPs in CLP modelling, and develops a system modelling approach to assist researchers and practitioners in the analysis of productivity-influencing parameters together with WSPs.

Research and development (R&D) partnerships involve investigative activities that may result in new discoveries and innovations that are critical for the technological advancement of the engineering domain. While demonstrating the value of these partnerships is essential for encouraging investment, the engineering domain lacks a formal evaluation framework. In this paper, a methodology and framework for evaluating R&D partnerships is introduced. The effectiveness of the developed framework is tested using a case study that focuses on the role of the university within the Natural Sciences and Engineering Research Council of Canada (NSERC) Industrial Research Chair (IRC) program. Using correlation analysis, the activities and investment areas that lead to the desired outcomes for the university research are identified. By using the developed framework over time and applying it to different research programs and industries, key activities and investment areas can be established and improved R&D policies and implementation plans developed.

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Recent trends indicate that fuzzy techniques (fuzzy set theory, fuzzy logic, and fuzzy hybrid models) have found increased application in the construction domain, even more so in the last half decade. This paper presents the application of fuzzy expert models and fuzzy hybrid concepts in modeling construction labour productivity, which is critical information for scheduling and estimating construction projects. The fuzzy expert model addresses both subjective and objective factors affecting labour productivity of two common industrial construction processes: rigging and welding pipe. The resulting model matched highly with respect to linguistic terms; however, the numerical match was low, indicating the need to have fuzzy hybrid models to improve the predictive ability of the fuzzy expert model. Further research is underway to combine the strengths of fuzzy logic in addressing subjective and linguistic evaluations of labourer performance with the strengths of other artificial intelligence methods, such as neural networks, in training and calibrating the fuzzy model to properly address the context variables, as well as the principal variables. Keywords-fuzzy set theory; fuzzy and hybrid fuzzy expert models; construction industry; labour productivityI.

Construction labor productivity (CLP) is one of the most studied areas in the construction research field, and several contextspecific predictive models have been developed. However, CLP model development remains a challenge, as the complex impact of multiple subjective and objective influencing variables have to be examined in various project contexts while dealing with limited data availability. On the other hand, lack of a framework for adapting existing or original models from one context to other contexts limits the possibility of reusing existing models. Such challenges are addressed in this paper through the development of a context adaptation framework. The framework is used to transfer the knowledge represented in fuzzy inference (FIS) based CLP models from one context to another, by using linear and nonlinear evolutionary based transformation of the membership functions combined with sensitivity analysis of fuzzy operators and defuzzification methods. Using four context-specific CLP models developed for concreting activity under industrial, warehouse, high-rise, and institutional building project contexts, the framework was implemented, and the prediction capability of the adapted models was evaluated based on their prediction similarity with the original models. The results showed that linearly adapted CLP models for industrial and institutional contexts and nonlinearly adapted CLP models for warehouse and high-rise contexts provide a similar prediction capability with the original models. The proposed context adaptation framework and findings from this paper address the limitations in past context adaptation research by examining a practical context-sensitive application problem and further examining the role of fuzzy operators and defuzzification methods. The findings assist researchers and industry practitioners to take full advantage of existing FIS-based models in the study of new contexts, for which data availability might be limited.