An Approach to Twinning and Mining Collaborative Network of Construction Projects

TL;DR

This paper presents a novel data-driven framework for analyzing collaborative networks in construction projects, enabling automatic identification of key players, patterns, and communication flows to improve project management.

Contribution

It introduces ISF and association rule mining into a comprehensive framework for mapping and analyzing construction collaboration networks automatically.

Findings

Successfully identified key players, FCUs, and information flow patterns.

Demonstrated the framework's effectiveness in a large-scale project.

Revealed that small groups tend to handle specific tasks more efficiently.

Abstract

Understanding complex collaboration processes is essential for the success of construction projects. However, there is still a lack of efficient methods for timely collection and analysis of collaborative networks. Therefore, an integrated framework consisting three parts, namely, system updating for data collection, data preprocessing, and social network analysis, is proposed for the twinning and mining collaborative network of a construction project. First, a system updating strategy for automatic data collection is introduced. Centrality measures are then utilized to identify key players, including hubs and brokers. Meanwhile, information sharing frequency (ISF) and association rule mining are introduced to discover collaborative patterns, that is, frequently collaborating users (FCUs) and associations between information flows and task levels. Finally, the proposed framework is validated and demonstrated in a large-scale project. The results show that key players, FCUs, and associations between information flows and task levels were successfully discovered, providing a deep understanding of collaboration and communication for decision-making processes. This research contributes to the body of knowledge by: 1) introducing ISF and Apriori-based association mining algorithm to identify FCUs and information flow patterns in collaboration; 2) establishing a new data-driven framework to map and analyze fine-grained collaborative networks automatically. It is also shown that people tend to form small groups to handle certain levels or types of tasks more efficiently. Other researchers and industrial practitioners may use this work as a foundation to further improve the efficiency of collaboration and communication.