Text Mining-based Construction Site Accident Classification Using Hybrid Supervised Machine Learning Model

• Main Author: Denny Kusoemo
• Other Authors: Min-Yuan Cheng
• Format: Others
• Language: en_US
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/bt87dn

碩士 === 國立臺灣科技大學 === 營建工程系 === 107 === Construction project safety performance is a major concern in the construction industry. Accidents in the construction project not only caused severe health issues but also led to huge financial losses. These accidents are usually documented in a form of accident narratives that consist of accident summary and cause classification. While documenting hundreds of these accident narratives may need vast resources and efforts, the implementation of AI model is considered as one favorable solution to this particular classification problem. Nevertheless, previously implemented models still have a room of improvement in terms of model performance. For instance, Decision Tree, KNN, Naïve Bayesian, SVM, and LR are categorized as weak learner where they display a substantial error rate. In this regard, this study proposed a hybrid model between Gated Recurrent Unit (GRU) and Symbiotic Organisms Search (SOS), named Symbiotic Gated Recurrent Unit (SGRU). SOS algorithm searches GRU best parameters to ensure the optimal performance of the corresponding model. Furthermore, the proposed model is applied and evaluated on real construction project accident narrative as a case study. The experimental results in this study demonstrated a promising performance of SGRU on classifying accidents causes. By providing notable classification performance as well as outperforming other applied AI model, SGRU demonstrated the capability to aid prevention strategies development for future use in the construction industry.