Our proposed architecture is based on the joint fine tuning of large pretrained language and image neural network models. An observation consists of a short issue description, written by the SOs, accompanied with images where the issue is shown, relevant metadata and a priority score. For this, we utilize a new multimodal dataset, Safety4All, which contains 5344 safety-related observations created by 86 SOs in 486 sites. In this work, we develop a multimodal machine-learning architecture for the analysis and categorization of safety observations, given textual descriptions and images taken from the location sites. Safety officers (SOs) are engineers, who perform site audits to businesses, record observations regarding possible safety issues and make appropriate recommendations. Modern businesses are obligated to conform to regulations to prevent physical injuries and ill health for anyone present on a site under their responsibility, such as customers, employees and visitors. That is to say, a monetary penalty may not necessarily improve the effectiveness of the companyвЂ™s measurements. Third, we need to consider the role of economic punishment dialectically, while it is not a significant feature for successful enforcement results. Second, it is noted that counties are also one of the essential features that matter the enforcement results.

Therefore, policymakers and practitioners should pay more attention to those two types of accidents.

Through LDA (Latent Dirichlet Allocation) analysis combined with the decision tree model, our research attained the following conclusions: first, we find that the LDA themes of violations as вЂњErosion and sedimentвЂќ and вЂњWater pollutionвЂќ are critical factors for вЂњFailedвЂќ enforcement results. In this respect, we utilized the вЂњOil and Gas Compliance ReportвЂќ from the Pennsylvania Department of Environmental Protection from 2000 to 2019, a total of 5737 violation records, to dig out the historical violation patterns. To support the regulation improvement and early-warning system building, it is of great need to learn the regular patterns in recurrent violations both for practitioners and governments. With unconventional oil and gas booming in commercial development, its inevitable environmental damage has aroused the publicвЂ™s vigilance. According to these sectoral patterns, managers and policy makers in the fields of safety take a look at the management issues related to the industry, source, activity, and accident result, considering respective characteristics of industrial sites. As a result of the case study for the Occupational Safety and Health Administration (OSHA) in the United States, the five sectoral patterns of accident process are identified: scale-intensive, facility-intensive, supplier-dominated, market-dominated, and service-dominated patterns. For this, the textmining and latent Dirichlet allocation (LDA) algorithms are used to extract topics of accidents and their main factors, matched with class of industries.

In this respect, this study aims to identify the sectoral patterns of accident process using narrative texts information contained in accident reports. To present similarity and difference of accident process by categorizing those multiple accident factors shared across industries, identifying sectoral patterns of accidents are useful. In particular, narrative texts allow finding multiple accident factors and types of accident process including industry, hazard, work activity, and accident result. The narrative text analytics has recently focused on identifying an accident process in the various fields of safety such as manufacturing, construction, chemicals, and service.

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