Exploring information generation and propagation from the point of installation on construction jobsites: An SNA/ABM hybrid approach

Construction requires the knowledge and experience of craftsmen. The knowledge and experience is gained through local coordination and local adaption at points of installation, which classifies the work as complex production. Information generated at these points of installation can be valuable in understanding how and why workers make decisions, especially at points of improvisation when they encounter obstacles. However, this information often goes unrecognized at least in explicit form by the workers themselves, and is therefore not documented and not captured.;The author's experience and observations on construction jobsites, combined with literature review, show that the information generated from the point of installation has not been extensively studied or modeled in construction. However, other industries characterized as complex production have seen improvements by studying and modeling the information generated and propagated from the point where the knowledge and experience of skilled craftsmen do their work. The research postulate studied herein is that if this information is modeled and understood, the impact it has on jobsites would be known and work performance could improve.;The literature shows that observation-based studies of information generation at the worker and crew level have only recently been conducted, and not from the standpoint of information propagation. In addition, the literature led to identification of agent based modeling (ABM) and social network analysis (SNA) as sound approaches for modeling the information generation and propagation from the point of installation. The research method included developing a conceptual information model, evaluating and improving the model based on pilot jobsite observations; data collection and observations used to develop a representative model of the information generation and propagation from points of installation. A unique approach of an SNA-informed ABM was used to simulate the final model, and test the result with select jobsite scenarios.;The data collected showed that information is indeed generated and often not captured or reported from the point of installation. Workers encounter obstacles in 89% of their scheduled activities, and information about those obstacles only propagates beyond the crew level 50% of the time. The simulated SNA-informed ABM based on the data collection and conceptual model showed that there are differences in how workers handle obstacles in early vs. later stages of the jobsite, and also on large vs. small jobsites. Workers are more influenced by others in the early stages of a job and on jobsites with fewer workers.;In conclusion, by studying the information generation and propagation from the point of installation with empirical data collection, observations, and conceptual model building, a simulation showed how this information could impact work performance. In addition, explorations of the simulated model showed how the information available from the point of installation propagates and influences jobsite outcomes.