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# Research on the causal mechanism of prefabricated building accidents: a comprehensive framework integrating association rule mining and graph neural network

## Details

**Authors** Liu et al.

**Year** 2026

**Publisher** Engineering, Construction and Architectural Management

**Discipline** Engineering

[Read it at the publisher](https://doi.org/10.1108/ecam-06-2025-0950) 
10.1108/ecam-06-2025-0950

## In authors' words

### What they found (results)

Analyzing 267 accident cases with association-rule mining and a graph neural network, the study finds the causal network exhibits small-world, heavy-tailed structure, with root and direct causes accounting for about 75% of the most influential nodes and a small number of recurrent causal chains driving propagation.

## Commentary

### In short

The finding is incomplete without both a relational reading -- causal chains and bridging factors that propagate risk from one accident factor to another -- and a systems reading, since the causal network's whole-level small-world structure is not reducible to any single factor.

**Patterns it shows** S, R

**Added** 2026-08-24

**How to cite this** Liu et al. (2026). Research on the causal mechanism of prefabricated building accidents: a comprehensive framework integrating association rule mining and graph neural network. Engineering, Construction and Architectural Management.