Construction engineering and management ͑CEM͒ researchers often rely on alternative research techniques when traditional methods fail. For example, surveys, interviews, and group-brainstorming techniques may not be appropriate for research that involves confounding factors and requires access to sensitive data. In such an environment, the Delphi technique allows researchers to obtain highly reliable data from certified experts through the use of strategically designed surveys. At present, the Delphi method has not seen widespread use in CEM research. This is likely due to variation among studies that implement Delphi in CEM research and ambiguity in literature that provides guidance for the specific parameters associated with the method. Using the guidance in this paper, the reader may: ͑1͒ understand the merits, appropriate application, and appropriate procedure of the traditional Delphi process; ͑2͒ identify and qualify potential expert panelists according to objective guidelines; ͑3͒ select the appropriate parameters of the study such as the number of panelists, number of rounds, type of feedback, and measure of consensus; ͑4͒ identify potential biases that may negatively impact the quality of the results; and ͑5͒ appropriately structure the surveys and conduct the process in such a way that bias is minimized or eliminated.

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Innovation is vital to successful, long-term company performance in the construction industry. Understanding the innovation process, how innovation can be enhanced and how it can be measured are key steps to managing and enhancing innovation. The factors that affect innovation on a project were identified, as well as how these factors can be used to measure the level of innovation on a project, and the practices and processes that encourage and facilitate innovative changes. Case studies of construction projects in the United States revealed three necessary components of innovation: idea generation, opportunity and diffusion. A variety of practices are used to optimize each component including support and commitment from the owner/client and firm upper management, workforce and project team integration and diversity. Applying the practices identified in the research leads to enhanced innovation through better communication among project team members, integration of the design and construction disciplines, more efficient designs, development of unique ways of completing work and sharing of the lessons learned. The end result of innovation will be projects that successfully meet and exceed cost, quality, schedule and safety goals.Integrated team, innovation, organizational culture, organizational behaviour, project management,

a b s t r a c tThe needs to ground construction safety-related decisions under uncertainty on knowledge extracted from objective, empirical data are pressing. Although construction research has considered machine learning (ML) for more than two decades, it had yet to be applied to safety concerns. We applied two state-of-the-art ML models, Random Forest (RF) and Stochastic Gradient Tree Boosting (SGTB), to a data set of carefully featured attributes and categorical safety outcomes, extracted from a large pool of textual construction injury reports via a highly accurate Natural Language Processing (NLP) tool developed by past research. The models can predict injury type, energy type, and body part with high skill (0.236 b RPSS b 0.436), outperforming the parametric models found in the literature. The high predictive skill reached suggests that injuries do not occur at random, and that therefore construction safety should be studied empirically and quantitatively rather than strictly being approached through the analysis of subjective data, expert opinion, and with a regulatory and managerial perspective. This opens the gate to a new research field, where construction safety is considered an empirically grounded quantitative science. Finally, the absence of predictive skill for the output variable injury severity suggests that unlike other safety outcomes, injury severity is mainly random, or that extra layers of predictive information should be used in making predictions, like the energy level in the environment. In the context of construction safety analysis, this study makes important strides in that the results provide reliable probabilistic forecasts of likely outcomes should an accident occur, and show great potential for integration with building information modeling and work packaging due to the binary and physical nature of the input variables. Such data-driven predictions had been absent from the field since its inception.