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Digital Transformation Applications in Construction and Engineering
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Digital Transformation Applications in Construction and Engineering

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Green Building".

Deadline for manuscript submissions: closed (20 May 2023) | Viewed by 9392

Special Issue Editor

Prof. Dr. Eul-Bum Lee

E-Mail Website
Guest Editor
Graduate Institute of Ferrous and Eco Material Technology and Department of Industrial Management and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea
Interests: construction and engineering it; digital twin and digital transformation; building information modeling (3D-4D-5D BIM); advanced work packaging (AWP); artificial intelligence (AI) and smart engineering; engineering project management; natural language processing (NLP); contract and risk management; engineering economics and project finance; infrastructure; construction management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The presence of digital transformation is gradually expanding in all industries and resulting in increased productivity. The construction industry, based on its strong presence in the traditional market, has considerable growth potential through the use of technologies such as the Internet of Things, big data, advanced manufacturing, robotics, 3D printing, block chain technology, and artificial intelligence (AI). Since the construction industry can be huge and complex, its processes are not optimized, so there is significant potential for growth in terms of the development of new construction solutions and applications. Successful achievement of increased quality and competitiveness in the construction field will depend on how digital transformation is applied to construction engineering in the future.

Innovation-driven engineering and management strategies and solutions are highly desired in responding to the rapidly changing business environment. Various digital technologies that have been developed in the last decade may create significant opportunities for innovation as well as improvements in the productivity and safety associated with the engineering and management of capital projects. The technologies of relevance include but are not limited to a) unmanned arial vehicles for surveying, quality assessment, and project progress monitoring; b) remote sensing methods such as light detection and ranging for practical surveying; c) point cloud-based surveying data creation; d) building information modeling (BIM)-based design and engineering; e) various sensing technologies to improve job site safety; f) artificial intelligence (AI)-based project risk detection; g) automated schedule monitoring and progress evaluation based on a digitalized planned schedule; h) text mining and natural language processing (NLP)-based project document review, evaluation, and compliance checking; i) digitalized design and engineering data-based project workflow reengineering, etc. 

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

  • Machine learning and artificial intelligence (AI) for engineering and project management applications for capital projects
  • Automation in construction with a special focus on mega-infrastructure projects both onshore and offshore, power plants, industrial plants, and other engineering procurement construction (EPC) projects
  • Computer-aided engineering application and interface such as 3D- building information modeling (BIM), 4D-BIM, 5D-BIM, and BIM for digital twin
  • Big data/AI based intelligent project management systems
  • Big data-based feasibility analysis systems
  • Big data integration system and applications for claims and delay analysis
  • Intelligent equipment traceability system for real-time construction management based on BIM/Internet of Things (IoT)
  • Machine learning and its applications in project management information systems
  • Natural language processing (NLP) application to construction contracts
  • Text-mining and contextual analysis on construction projects
  • Advanced work packaging (AWP) with BIM—3D BIM-based engineering and construction collaboration system
  • 4D BIM-based safety management systems
  • BIM-based integrated cost engineering and management
  • BIM-based maintenance repair and operation (MRO) systems
  • 3D digital twin for industrial plants
  • Web-based 4D visualization

I look forward to receiving your contributions.

Prof. Dr. Eul-Bum Lee
Guest Editor

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
  • AI
  • big data
  • machine learning
  • NLP
  • BIM
  • AWP
  • MRO

Published Papers (3 papers)

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Research

37 pages, 3765 KiB  
Article
Knowledge Retrieval Model Based on a Graph Database for Semantic Search in Equipment Purchase Order Specifications for Steel Plants
by Ho-Jin Cha, So-Won Choi, Eul-Bum Lee and Duk-Man Lee
Sustainability 2023, 15(7), 6319; https://doi.org/10.3390/su15076319 - 06 Apr 2023
Cited by 1 | Viewed by 2003
Abstract
The complexity and age of industrial plants have prompted a rapid increase in equipment maintenance and replacement activities in recent years. Consequently, plant owners are challenged to reduce the process and review time of equipment purchase order (PO) documents. Currently, traditional keyword-based document [...] Read more.
The complexity and age of industrial plants have prompted a rapid increase in equipment maintenance and replacement activities in recent years. Consequently, plant owners are challenged to reduce the process and review time of equipment purchase order (PO) documents. Currently, traditional keyword-based document search technology generates unintentional errors and omissions, which results in inaccurate search results when processing PO documents of equipment suppliers. In this study, a purchase order knowledge retrieval model (POKREM) was designed to apply knowledge graph (KG) technology to PO documents of steel plant equipment. Four data domains were defined and developed in the POKREM: (1) factory hierarchy, (2) document hierarchy, (3) equipment classification hierarchy, and (4) PO data. The information for each domain was created in a graph database through three subprocesses: (a) defined in a hierarchical structure, (b) classified into nodes and relationships, and (c) written in triples. Ten comma-separated value (CSV) files were created and imported into the graph database for data preprocessing to create multiple nodes. Finally, rule-based reasoning technology was applied to enhance the model’s contextual search performance. The POKREM was developed and implemented by converting the Neo4j open-source graph DB into a cloud platform on the web. The accuracy, precision, recall, and F1 score of the POKREM were 99.7%, 91.7%, 100%, and 95.7%, respectively. A validation study showed that the POKREM could retrieve accurate answers to fact-related queries in most cases; some incorrect answers were retrieved for reasoning-related queries. An expert survey of PO practitioners indicated that the PO document review time with the POKREM was reduced by approximately 40% compared with that of the previous manual process. The proposed model can contribute to the work efficiency of engineers by improving document search time and accuracy; moreover, it may be expandable to other plant engineering documents, such as contracts and drawings. Full article
(This article belongs to the Special Issue Digital Transformation Applications in Construction and Engineering)
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27 pages, 3509 KiB  
Article
An AI-Based Automatic Risks Detection Solution for Plant Owner’s Technical Requirements in Equipment Purchase Order
by Chae-Yeon Kim, Jong-Gwan Jeong, So-Won Choi and Eul-Bum Lee
Sustainability 2022, 14(16), 10010; https://doi.org/10.3390/su141610010 - 12 Aug 2022
Cited by 3 | Viewed by 1612
Abstract
Maintenance activities to replace, repair, and revamp equipment in the industrial plant sector are gradually needed for sustainability during the plant’s life cycle. In order to carry out these revamping activities, the plant owners exchange many purchase orders (POs) with equipment suppliers, including [...] Read more.
Maintenance activities to replace, repair, and revamp equipment in the industrial plant sector are gradually needed for sustainability during the plant’s life cycle. In order to carry out these revamping activities, the plant owners exchange many purchase orders (POs) with equipment suppliers, including technical and specification documents and commercial procurement content. As POs are written in various formats with large volumes and complexities, it is often time-consuming for the owner’s engineer to review them and it may lead to errors and omissions. This study proposed the purchase order recognition and analysis system (PORAS), which automatically detects and compares risk clauses between plant owners’ and suppliers’ POs by utilizing artificial intelligence (AI). The PORAS is a comprehensive framework consisting of two independent modules and four model components that accurately reflect on the added value of the PORAS. The table recognition and comparison (TRC) module is utilized for risk clauses in POs written in tables with its two components, the table comparison (TRC-C) and table recognition (TRC-R) models. The critical terms in general conditions (CTGC) module analyzes the patterns of risk clauses in general texts, then extracts them with a rule-based algorithm and compares them through entity matching. In the TRC-C model using machine learning (Ditto model), a few errors occurred due to insufficient training data, resulting in an accuracy of 87.8%, whereas in the TRC-R model, a rule-based algorithm, errors occurred in only some exceptional cases; thus, its F1 score was evaluated to be 96.9%. The CTGC module’s F2 score for automatic extraction performance was evaluated as 79.1% due to some data’s bias. Overall, the validation study shows that while a human review of the risk clauses in a PO manually took hours, it took only an average of 10 min with the PORAS. Therefore, this time saving can significantly reduce the owner engineer’s PO workload. In essence, this study contributes to achieving sustainable engineering processes through the intelligence and automation of document and risk management in the plant industry. Full article
(This article belongs to the Special Issue Digital Transformation Applications in Construction and Engineering)
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32 pages, 16092 KiB  
Article
Contractor’s Risk Analysis of Engineering Procurement and Construction (EPC) Contracts Using Ontological Semantic Model and Bi-Long Short-Term Memory (LSTM) Technology
by So-Won Choi and Eul-Bum Lee
Sustainability 2022, 14(11), 6938; https://doi.org/10.3390/su14116938 - 06 Jun 2022
Cited by 6 | Viewed by 5135
Abstract
The development of intelligent information technology in the era of the fourth industrial revolution requires the EPC (engineering, procurement, and construction) industry to increase productivity through a digital transformation. This study aims to automatically analyze the critical risk clauses in the invitation to [...] Read more.
The development of intelligent information technology in the era of the fourth industrial revolution requires the EPC (engineering, procurement, and construction) industry to increase productivity through a digital transformation. This study aims to automatically analyze the critical risk clauses in the invitation to bid (ITB) at the bidding stage to strengthen their competitiveness for the EPC contractors. To this end, we developed an automated analysis technology that effectively analyzes a large amount of ITB documents in a short time by applying natural language processing (NLP) and bi-directional long short-term memory (bi-LSTM) algorithms. This study proposes two models. First, the semantic analysis (SA) model is a rule-based approach that applies NLP to extract key risk clauses. Second, the risk level ranking (RLR) model is a train-based approach that ranks the risk impact for each clause by applying bi-LSTM. After developing and training an artificial intelligent (AI)-based ITB analysis model, its performance was evaluated through the actual project data. As a result of validation, the SA model showed an F1 score of 86.4 percent, and the RLR model showed an accuracy of 46.8 percent. The RLR model displayed relatively low performance because the ITB used in the evaluation test included the contract clauses that did not exist in the training dataset. Therefore, this study illustrated that the rule-based approach performed superior to the training-based method. The authors suggest that EPC contractors should apply both the SA and RLR modes in the ITB analysis, as one supplements the other. The two models were embedded in the Engineering Machine-learning Automation Platform (EMAP), a cloud-based platform developed by the authors. Rapid analysis through applying both the rule-based and AI-based automatic ITB analysis technology can contribute to securing timeliness for risk response and supplement possible human mistakes in the bidding stage. Full article
(This article belongs to the Special Issue Digital Transformation Applications in Construction and Engineering)
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