System Engineering and Productivity

System Engineering and Productivity

A Hybrid SEM–Machine Learning Framework for Improving Communication Management in Construction Projects (Case Study: Zanjan City)

Articles in Press

Document Type : Research Paper

Authors
Hadi Shakibazahed 1
Naser Ahmadi 2
1 Corresponding author: Assistant Professor, Department of Civil Engineering, Faculty of Engineering, Hakim Sabzevari University, Sabzevar, Iran
2 M.Sc., Department of Civil Engineering, Faculty of Engineering, Hakim Sabzevari University, Sabzevar, Iran
10.22034/sep.2025.2075517.1415
Abstract
The construction industry in Iran faces challenges such as delays, cost increases, and quality decline, a significant portion of which stems from weaknesses in communication management among project stakeholders. The aim of this research is to identify key factors affecting communication weaknesses and to present a hybrid model for analyzing and predicting their impact on the performance of construction projects. The present study employs a mixed exploratory-explanatory approach. In the qualitative phase, indicators related to communication weaknesses were extracted through a systematic review of sources and content analysis of documents. In the quantitative phase, data from 114 valid questionnaires from employees of construction projects in Zanjan city were analyzed using Structural Equation Modeling (SEM) and Random Forest (RF) algorithm. The SEM model results showed that information flow, project management, and alignment of human resources with organizational strategy have the greatest direct impact on project performance. The machine learning model also identified these three factors as the strongest predictive variables for communication weaknesses and achieved a prediction accuracy of 0.87 and an AUC value of 0.93. The combination of the two methods provided the possibility of simultaneous analysis of theoretical relationships and empirical prediction. The findings indicate that strengthening the project information management system, clarifying the managerial structure, and aligning human resources with organizational goals can significantly reduce the risk of poor communications. The main innovation of the research is the application of the hybrid SEM-ML approach in the context of national projects in Iran and presenting a model that, in addition to analyzing causal relationships, has the ability to rank and data-driven predict key factors.

Highlights

  • Identification of key communication weaknesses
  • Innovative hybrid model of structural equation modeling and machine learning
  • Prediction of key communication factors using machine learning
  • Practical tool for national projects

Keywords
Poor communication
Structural Equation Modeling (SEM)
Machine Learning (ML)
Information flow
Project performance
Subjects

Copyright © Hadi Shakibazahed, Naser Ahmadi

 

License

This article is released under the Creative Commons Attribution (CC BY 4.0) license. Anyone is free to copy, share, translate, and adapt this article for any purpose, whether commercial or non-commercial, as long as proper citation is given to the authors and original publication.

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System Engineering and Productivity

Articles in Press, Accepted Manuscript
Available Online from 03 December 2025

  • Receive Date 12 October 2025
  • Revise Date 20 November 2025
  • Accept Date 03 December 2025
  • First Publish Date 03 December 2025
  • Publish Date 03 December 2025