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Application of Artificial Intelligence (AI) and Digital Transformation (DT) to Sustainable Engineering Project Management

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Products and Services".

Deadline for manuscript submissions: closed (25 December 2024) | Viewed by 4966

Special Issue Editors


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Guest Editor
Graduate Institute of Ferrous and Eco Material Technology & Department of Industrial Management and Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea
Interests: engineering project management (EPM); infrastructure and construction management; digital transformation; digital twin; building information modeling (BIM); advanced work packaging (AWP); artificial intelligence (AI) and smart-engineering; contract and risk management; engineering economics and project finance
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Graduate Institute of Ferrous and Eco Material Technology, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea
Interests: artificial intelligence (AI); big-data driven engineering machine learning applications (MLA); natural language processing (NLP); ChatGPT applications in engineering; EPC contracts and contract risk management; digital twin; BIM; advanced work package (AWP); smart construction and digitalization; engineering project management; engineering economics and project finance
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Digital technology is fundamentally transforming virtually every industry, and like many other fields, the engineering field has witnessed disruptive changes in recent years. Consequently, industrial plant engineering (such as power plants and petro-chemical plants) is rapidly undergoing technological innovations to keep pace with the fourth industrial revolution and leverage digital transformation for sustainable growth.

In line with the rapidly changing times, plant engineering projects are pursuing transformation via digital methods throughout their productive lifecycles to boost sustainability, including the planning, design, construction, and operation and maintenance stages, by integrating intelligent technologies via digital convergence.

Key technologies commonly mentioned in recent discourse in this field include artificial intelligence (AI), digital twins and digital transformation, robotics, the metaverse, drones, 3D printing, wearable and mobility technology, augmented reality (AR), virtual reality (VR), cloud-based collaboration tools, the integration of building information modelling (BIM) and geographic information system (GIS) data, etc.

The combination of these technologies is expected to enhance operational efficiency, reduce project costs, and improve safety, ultimately contributing to the sustainability of the plant engineering industries.

In this Special Issue, original research articles and reviews are welcome. Research areas may include, but are not limited to, the following subjects:

  • Artificial intelligence (AI) and machine learning applications for sustainable engineering management;
  • Automation in capital projects, with a particular focus on mega-infrastructure projects, onshore and offshore drilling, power plants, industrial plants, and other engineering procurement construction (EPC) projects;
  • Digitalization in the engineering and construction industries for sustainable infrastructure development;
  • Building information modeling (3D-4D-5D BIM) and their applications;
  • Advanced work packaging (AWP) with BIM;
  • BIM-based Maintenance Repair and Operation (MRO) systems;
  • Digital Twin or Digital Transformation for industrial plants;
  • Natural language processing (NLP) application and text mining in construction project documents, such as technical specifications and contracts;
  • chatGPT Applications in the engineering and construction field;
  • Cost engineering digitalization (estimating and control);
  • Digital contract management (bidding and execution).

We look forward to receiving your contributions.

Prof. Dr. Eul-Bum Lee
Dr. So-Won Choi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • digitization
  • digital transformation
  • digital twin
  • BIM
  • AWP
  • MRO
  • AI
  • big data
  • NLP
  • machine learning

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Published Papers (1 paper)

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Research

28 pages, 8087 KiB  
Article
Hazard Identification and Risk Assessment During Simultaneous Operations in Industrial Plant Maintenance Based on Job Safety Analysis
by Sung-Jin Kwon, So-Won Choi and Eul-Bum Lee
Sustainability 2024, 16(21), 9277; https://doi.org/10.3390/su16219277 - 25 Oct 2024
Cited by 3 | Viewed by 4225
Abstract
The risk of accidents during simultaneous operations (SIMOPS) in plant maintenance has been increasing. However, research on methods to prevent such accidents has been limited. This study aims to develop a novel framework, hazard identification and risk assessment of simultaneous operations (HIRAS), for [...] Read more.
The risk of accidents during simultaneous operations (SIMOPS) in plant maintenance has been increasing. However, research on methods to prevent such accidents has been limited. This study aims to develop a novel framework, hazard identification and risk assessment of simultaneous operations (HIRAS), for identifying and evaluating potential hazards during concurrent tasks. The framework developed herein is expected to be an effective safety management tool that can help prevent accidents during these operations. To this end, the job location and hazard information in job safety analysis (JSA) were standardized into four attributes. The standardized information was then synchronized spatially and temporally to develop a HIRAS model that identifies and assesses the impact of hazards between operations. The model was tested using 40 JSA documents corresponding to maintenance operations at Company P, a South Korean steel-making company. The model was tested in two scenarios: one with planned operations and the other with unplanned operations in addition to planned operations. The performance evaluation results of the first scenario showed an F1-score of 98.33%. In this case, a recall of 97.52% means that the model identified 97.52% of the hazard-inducing factors. The second scenario was compared with the results of a review by six subject matter experts (SMEs). The comparison of the results identified by the SMEs and the model showed an accuracy of 89.3%. This study demonstrates the potential of JSA, which incorporates the domain knowledge of workers and can be used not only for individual tasks but also as a safety management tool for surrounding operations. Furthermore, by improving the plant maintenance work environment, it is expected to prevent accidents, protect workers’ lives and health, and contribute to the long-term sustainable management of companies. Full article
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