Text mining-based construction site accident classification using hybrid supervised machine learning

Cheng, Min‐Yuan; Kusoemo, Denny; Gosno, Richard Antoni

doi:10.1016/j.autcon.2020.103265

Cited by 132 publications

(64 citation statements)

References 17 publications

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“…In [24], a hybrid model is proposed by using Gated Recurrent Unit (GRU) and Symbiotic Organisms Search (SOS), named Symbiotic Gated Recurrent Unit (SGRU). Text mining and Natural language processing techniques are used to pre-process the construction site accidents data.…”

Section: Literature Reviewmentioning

confidence: 99%

Using a Hybrid-Classification Method to Analyze Twitter Data During Critical Events

Alhashmi

Khedr²,

Arif

et al. 2021

IEEE Access

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In this paper, sentiment analysis of two critical events is presented using machine learning (ML) techniques. COVID-19 has put immense pressure across the globe and sentiment analysis of data from Twitter using ML techniques has become a hot topic. We extract the COVID-19 and Expo2020 data from twitter. First, we evaluate the Twitter data of these two significant events for sentiment analysis and then use the classification algorithm to find out the usefulness of the proposed methodology. A hybrid approach that uses supervised learning model Support Vector Machine (SVM) combined with Bayes Factor Tree Augmented Naive Bayes (BFTAN) technique is proposed to accurately classify the input tweet while keeping in mind the different challenges of sentiment analysis. Our study has four main contributions: a) hybrid classification techniques are thoroughly explored for sentiment analysis, b) a novel hybrid classification approach is proposed for sentiment analysis, c) a new Twitter dataset related to COVID-19 that can be used for future research, d) empirical study to show that the hybrid-classification approach can achieve comparable performance in improving accuracy, identifying the polarity of comparative sentences, distinguishing the intensity of opinion words, considering negative words, and handling sarcasm as well. The experimental results show that the proposed approach is robust in producing correct classification results with the tradeoff of poor time efficiency. Also, the accuracy of the proposed model is comparable to other classifiers, which is encouraging. Class distribution of each dataset demonstrates that more than 60% of tweets are negative.

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Section: Literature Reviewmentioning

confidence: 99%

Using a Hybrid-Classification Method to Analyze Twitter Data During Critical Events

Alhashmi

Khedr²,

Arif

et al. 2021

IEEE Access

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“…Previous scholars have performed statistical analyses of construction accidents. Cheng et al classified construction site accidents using hybrid supervised machine learning [ 14 ]. Ayhan and Tokdemir developed a novel model to predict construction incident outcomes using latent class clustering analysis and artificial neural networks, and they proposed necessary preventative actions [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Characteristics of Fatal Accidents in the Construction Industry in China Based on Statistical Data

2021

IJERPH

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Background: Construction activities not only provide the necessary conditions for citizens to live, but also cause fatal accidents. Methods: This study aimed to reveal the characteristics of fatal accidents in the construction industry in China based on statistical data. From 2010 to 2019, there were 6005 fatal accidents in China’s construction industry causing 7275 deaths. The important features of these fatal accidents, such as the type, time of occurrence, site location, severity, and geographical region of the accident, were carefully analyzed. Results: There were 258 major and severe construction accidents causing 1037 deaths, accounting for 4.3% and 14.25% of the total number of construction accidents and deaths in this period, respectively. As an important finding, more deaths occurred in August and on Mondays. The greatest number of construction accidents took place along openings and edges, accounting for 22.9% of all fatal accidents. Taking into account their economic development level and number of employees, Qinghai and Hainan experienced a higher mortality rate than Jiangsu. Falls from a high place were the dominant type of construction accident, accounting for 51.66% of all accidents. However, collapses were the primary type of major and severe construction accident, accounting for 60.09% of such accidents. The predicted number of construction deaths in 2020 is 887 according to the GM(1,1) model. Corresponding safety measures should be adopted to improve the working environment of the construction industry. Implications: The implications of these results with respect to the characteristics of construction accidents can be regarded as the foundation for accident prevention in practice.

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“…Cheng et al [22] show a comparison of different algorithms, based on NLP techniques, for extracting knowledge from construction accident reports and classifying the narratives. Arteaga et al [23] address the issue of analyzing reports on the severity of traffic crashes and extracting meaningful information for developing safety countermeasures.…”

Section: Introductionmentioning

confidence: 99%

Near Miss Archive: a Challenge to Share Knowledge Among Inspectors and Improve Seveso Inspections

Ansaldi¹,

Agnello²,

Pirone³

et al. 2021

Preprint

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In European Seveso Legislation for the control of the hazard of major accidents (Directive 2015/12/UE), the Safety Management System SMS is an essential obligation for managers and the authorities are required to periodically verify its adequateness through periodical inspections at Seveso sites. One of the pillars of the SMS is the collection and analysis of documents on accidents, near misses and possibly anomalies, in order to identify weaknesses and implement continuous improvement. In Italy, for a few years, the documents, gathered from all Italian Seveso sites by the inspectors, have been archived and used for research purposes. The archive currently contains some 4000 reports, collected in five years by some 100 inspectors throughout Italy. The paper discusses in the detail the challenges faced to extract the knowledge hidden in the documents and make it usable through the design of a robust model. For this aim, Machine Learning techniques have been used as a preprocessing of the reports for extracting the concepts and their relations, organized into an entity-relation model. The effectiveness of this methodology and its potentiality are pointed out by investigating a few hot topics, exploiting the information contained in the repository.

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Text mining-based construction site accident classification using hybrid supervised machine learning

Cited by 132 publications

References 17 publications

Using a Hybrid-Classification Method to Analyze Twitter Data During Critical Events

Using a Hybrid-Classification Method to Analyze Twitter Data During Critical Events

Analysis of the Characteristics of Fatal Accidents in the Construction Industry in China Based on Statistical Data

Near Miss Archive: a Challenge to Share Knowledge Among Inspectors and Improve Seveso Inspections

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