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Text mining-based construction site accident classification using hybrid supervised machine learning
Abstract Safety is one key consideration in the monitoring of construction projects by engineers. Accidents in the project can potentially cause issues, such as workers' injury and progress delay, which lead to financial losses. Generally, accident narratives store all summaries and causes of the related events. Since documentations rapidly use large quantities of resources, the implementation of Artificial Intelligence (AI) begins to seek attention. Nevertheless, in current models, there are still drawbacks, such as weak learning performance and substantial error rate. In this regard, this study develops a hybrid model incorporating Gated Recurrent Unit (GRU) and Symbiotic Organisms Search (SOS), named Symbiotic Gated Recurrent Unit (SGRU). SOS searches the best parameters of GRU to ensure optimal performance. Furthermore, Natural Language Processing is applied to pre-process the text data prior classification process. The experimental result in this study showcases SGRU as the best classification model among other AI models. Therefore, SGRU shares the capability to aid the safety assessments of construction projects.
Highlights A hybrid supervised machine learning model is proposed for construction site accident classification. Natural Language Processing is adopted to pre-process the text data prior classification process. A searching algorithm is integrated to optimize the parameters of the machine learning model. Classification results can be used to aid safety assessments of construction projects.
Text mining-based construction site accident classification using hybrid supervised machine learning
Abstract Safety is one key consideration in the monitoring of construction projects by engineers. Accidents in the project can potentially cause issues, such as workers' injury and progress delay, which lead to financial losses. Generally, accident narratives store all summaries and causes of the related events. Since documentations rapidly use large quantities of resources, the implementation of Artificial Intelligence (AI) begins to seek attention. Nevertheless, in current models, there are still drawbacks, such as weak learning performance and substantial error rate. In this regard, this study develops a hybrid model incorporating Gated Recurrent Unit (GRU) and Symbiotic Organisms Search (SOS), named Symbiotic Gated Recurrent Unit (SGRU). SOS searches the best parameters of GRU to ensure optimal performance. Furthermore, Natural Language Processing is applied to pre-process the text data prior classification process. The experimental result in this study showcases SGRU as the best classification model among other AI models. Therefore, SGRU shares the capability to aid the safety assessments of construction projects.
Highlights A hybrid supervised machine learning model is proposed for construction site accident classification. Natural Language Processing is adopted to pre-process the text data prior classification process. A searching algorithm is integrated to optimize the parameters of the machine learning model. Classification results can be used to aid safety assessments of construction projects.
Text mining-based construction site accident classification using hybrid supervised machine learning
Cheng, Min-Yuan (author) / Kusoemo, Denny (author) / Gosno, Richard Antoni (author)
2020-05-10
Article (Journal)
Electronic Resource
English
Construction site accident analysis using text mining and natural language processing techniques
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