CONSTRUCTION SITE ACCIDENT ANALYSIS USING TEXT MINING AND NATURAL LANGUAGE PROCESSING TECHNIQUES

Abstract

Workplace safety is a major concern in many countries. Among various industries,
construction sector is identified as the most hazardous work place. Construction
accidents not only cause human sufferings but also result in huge financial loss. To
prevent reoccurrence of similar accidents in the future and make scientific risk control
plans, analysis of accidents is essential. In construction industry, fatality and
catastrophe investigation summary reports are available for the past accidents. In this
study, text mining and natural language process (NLP) techniques are applied to
analyze the construction accident reports. To be more specific, five baseline models,
support vector machine (SVM), linear regression (LR), K-nearest neighbor (KNN),
decision tree (DT), Naive Bayes (NB) and an ensemble model are proposed to classify
the causes of the accidents. Besides, Sequential Quadratic Programming (SQP)
algorithm is utilized to optimize weight of each classifier involved in the ensemble
model. Experiment results show that the optimized ensemble model outperforms rest
models considered in this study in terms of average weighted F1 score. The result also
shows that the proposed approach is more robust to cases of low support. Moreover,
an unsupervised chunking approach is proposed to extract common objects which
cause the accidents based on grammar rules identified in the reports. As harmful
objects are one of the major factors leading to construction accidents, identifying such
objects is extremely helpful to mitigate potential risks. Certain limitations of the
proposed methods are discussed and suggestions and future improvements are
provided.