# Relational Graph Convolutional Network for Text-Mining-Based Accident Causal Classification
###### Abstract  
Accident investigation reports are text documents that systematically review and analyze the cause and process of accidents after accidents have occurred and have been widely used in the fields such as transportation, construction and aerospace. With the aid of accident investigation reports, the cause of the accident can be clearly identified, which provides an important basis for accident prevention and reliability assessment. However, since accident record reports are mostly composed of unstructured data such as text, the analysis of accident causes inevitably relies on a lot of expert experience and statistical analyses also require a lot of manual classification. Although, in recent years, with the development of natural language processing technology, there have been many efforts to automatically analyze and classify text. However, the existing methods either rely on large corpus and data preprocessing methods, which are cumbersome, or extract text information based on bidirectional encoder representation from transformers (BERT), but the computational cost is extremely high. These shortcomings make it still a great challenge to automatically analyze accident investigation reports and extract the information therein. To address the aforementioned problems, this study proposes a text-mining-based accident causal classification method based on a relational graph convolutional network (R-GCN) and pre-trained BERT. On the one hand, the proposed method avoids preprocessing such as stop word removal and word segmentation, which not only preserves the information of accident investigation reports to the greatest extent, but also avoids tedious operations. On the other hand, with the help of R-GCN to process the semantic features obtained by BERT representation, the dependence of BERT retraining on computing resources can be avoided. accident causal classification; accident investigation reports; text mining; R-GCN; BERT 2022 2  
Although experts have been investigating the cause of the accident as soon as possible after the accident, unfortunately, on 10 March 2019, another Ethiopian Boeing 737 MAX8 with 157 passengers and crew on board suffered the same accident [6]. If the causes of some accidents can be identified as early as possible, for example, the cause of the accident can be preliminary determined based on the records of the accident and it is possible to take appropriate measures in advance to avoid the occurrence of the accident [7]. Although the possible causes of the accident are hidden in the accident investigation report, analyzing the possible accident causes from textual records is extremely challenging and usually requires an analysis performed by an expert team composed of scholars, engineers, designers, etc., which, to a certain extent, leads to the long process of accident cause analysis [8]. Therefore, a naive idea is to build an expert system to automatically analyze textual records in accident investigation reports, which is essentially a text classification problem, that is, by constructing suitable models to mine the information in the text and classify the text into different categories [9]. Text mining is the process of extracting effective, novel, useful, understandable, valuable knowledge scattered in text documents and using this knowledge to better organize information [10]. The rapid development of artificial intelligence technology [11; 12; 13], especially natural language processing (NLP) and text mining technology, makes it possible to analyze accident investigation reports on a large scale and automatically [14]. With the help of these emerging technologies, time consumption and human error in determining the accident causes would be minimized [15] and the efficiency of analyzing would be significantly improved. A great deal of work has been conducted in existing studies to apply different models to the accident causal classification.