Predicting Communication Quality in Construction Projects: A Fully-connected Deep Neural Network Approach

Rahimian, A., Hosseini, M. R., Martek, I., Taroun, Abdulmaten, Alvanchi, A. and Odeh, I. (2022) Predicting Communication Quality in Construction Projects: A Fully-connected Deep Neural Network Approach. Automation in Construction, 139 (104268). ISSN 0926-5805

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Abstract

Establishing high-quality communication in construction projects is essential to securing successful collaboration and maintaining understanding among project stakeholders. Indeed, poor communication results in low productivity, poor efficiency, and substandard deliverables. While high-quality communication is recognized as contingent on the interpersonal skills of workers, the impacts of communication quality on job performance remain unknown. This study addresses this deficiency by developing a method to evaluate construction workers' communication quality. A literature review is undertaken to capture salient interpersonal skills. Leadership style, listening, team building, and clarifying expectations are identified. A questionnaire survey is drafted to capture construction practitioners' perception of these skills' effects on communication quality, returning 180 responses. Next, an artificial neural network model, or communication quality predictor (CQP), is developed, able to predict the quality of workers' interpersonal communication. The model accuracy on training is 87%; for testing, 79%. Finally, CQP is deployed in a real-time context in order to validate the reliability, returning an 80% prediction accuracy. This study is the first of its kind in offering a quantified, predictive model associating interpersonal skills with quality of communications in the context of the construction sector. In practical terms, the CQP can flag interpersonal conflicts before they escalate, while also guiding construction managers in the design of interpersonal skills training

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