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Special Issue "Applications of Engineering Digitalization and Construction IT for Energy Projects"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "Energy Economics and Policy".

Deadline for manuscript submissions: 31 May 2020

Special Issue Editors

Guest Editor
Prof. Dr. Eul-Bum Lee

Pohang University of Science and Technology (POSTECH) / Graduate Institute of Ferrous Technology & Department of Industrial Management and Engineering, Korea
Website | E-Mail
Phone: 82542790136
Interests: building information modeling (3D-4D-5D-BIM); construction IT; smart-engineering and digitalization; engineering project management (schedule-cost integration and PMIS); contract and risk management, engineering economics and financial sustainability, infrastructure and heavy construction management
Guest Editor
Prof. Dr. H. David Jeong

Texas A&M University, Department of Construction Science, USA
Website | E-Mail
Interests: data analytics; artificial intelligence for construction management; digital project delivery; integrated project delivery; advanced scheduling; cost engineering theories and principles

Special Issue Information

Dear colleagues,

According to the EIA 2018 outlook, the energy demand is expected to grow by about 27%, or 3743 million tons oil equivalent (Mtoe), worldwide, from 2017 to 2040.

More specifically, the energy consumption of petroleum, natural gas, and coal use combined is forecast to grow 16% from 2017 to 2040. On the other hand, the oil and gas sector has reduced its investments as a result of the fall in oil prices over the last few years, but the sector has recently shown a slow but consistent growth in investment. The Korea Export–Import Bank (KEXIM) predicted the global construction market to increase by USD 10.1 billion to USD 511.6 billion by 2019. This is largely because of a projected increase of plant orders from the Middle East as a result of rising oil prices.  

According to recent studies, a large number of engineering, procurement, and construction (EPC) contractors on megaprojects in the energy sector have suffered from massive profit losses. There are many causes that can be attributed to the losses, but one of the major causes that has been pointed out is a poor understanding about project complexity, which the project creates as a result of its large size, and subsequently poor project management planning and execution during pre-construction and construction. Another challenge that the industry faces is the fact that the construction sector has had a labor-productivity growth rate of 1% per year in the global market over the past two decades, compared with 2.8% for the total world economy and 3.6% for manufacturing.

The rapid development of digital technology in recent years will provide an opportunity to overcome these limitations of management. Those technologies include, but are not limited to, the following: (a) unmanned aerial vehicles for surveying, quality assessment, and project progress monitoring; (b) remote sensing methods such as light detection and ranging for effective 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-based project risk detection; (g) automated schedule monitoring and progress evaluation based on digitalized planned schedule; (h) texting mining and natural language processing (NLP)-based project document review, evaluation and compliance checking; and (i) digitalized design and engineering data-based project work flow re-engineering. 

This Special Issue will collect the state-of the art advancements in these areas that may have significant implications to the construction industry, especially for the energy sector and academia. Both technical papers and case studies are welcome for publication in this Special Issue.

Prof. Dr. Eul-Bum Lee
Prof. Dr. H. David Jeong
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 papers will be 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. Energies 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 1800 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

  • Energy sector projects such as oil and gas (onshore and offshore), power plants, industrial plants, and iron and steel plants
  • Innovation in EPC project management and engineering management
  • Big data platform and solutions applications
  • Use of unmanned aerial vehicles
  • Surveying innovations such as remote sensing methods, point cloud creation, and any digital and electronical conversion, and the recognition of image drawings and documents
  • 3D-BIM, 4D-BIM, and 5D-BIM application
  • Innovation in safety management
  • Artificial intelligence (AI) and machine learning (ML) application
  • Automated and non-automated integration of schedule and cost engineering (estimation and control)
  • Text-mining and contextual analysis
  • Natural language processing (NLP)
  • Advanced work packaging (AWP) and BIM-based engineering collaboration
  • Virtual reality (VR), augmented reality (AR), and mixed reality (MR) applications
  • IoT implementation, senor data, and smart-tracking with RFID and QR codes, and bar codes
  • Engineering cloud service
  • Project management information systems
  • Information technology or big data-based engineering, procurement, construction, and/or general process improvements

Published Papers (2 papers)

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Research

Open AccessArticle
A Forecast Model for the Level of Engineering Maturity Impact on Contractor’s Procurement and Construction Costs for Offshore EPC Megaprojects
Energies 2019, 12(12), 2295; https://doi.org/10.3390/en12122295 (registering DOI)
Received: 28 May 2019 / Revised: 8 June 2019 / Accepted: 12 June 2019 / Published: 16 June 2019
PDF Full-text (2638 KB) | HTML Full-text | XML Full-text
Abstract
This paper focuses on the influence of detailed engineering maturities on offshore engineering, procurement, and construction (EPC) project procurement and construction cost performance. The authors propose a detailed engineering completion rating index system (DECRIS) to estimate the engineering maturities, from contract award to [...] Read more.
This paper focuses on the influence of detailed engineering maturities on offshore engineering, procurement, and construction (EPC) project procurement and construction cost performance. The authors propose a detailed engineering completion rating index system (DECRIS) to estimate the engineering maturities, from contract award to beginning of construction or steel cutting. The DECRIS is supplemented in this study with an artificial neural network methodology (ANN) to forecast procurement and construction cost performances. The study shows that R2 and mean error values using ANN functions are 20.2% higher and 19.7% lower, respectively, than cost performance estimations using linear regressions. The DECRIS cutoff score at each gate and DECRIS forecasting performance of total cost impact were validated through the results of fifteen historical offshore EPC South Korean mega-projects, which contain over 300 procurement cost performance data points in total. Finally, based on the DECRIS and ANN findings and a trade-off optimization using a Monte-Carlo simulation with a genetic algorithm, the authors propose a cost mitigation plan for potential project risks based on optimizing the engineering resources. This research aids both owners and EPC contractors to mitigate cost overrun risks, which could be continuously monitored at the key engineering gates, and engineering resources could be adjusted per optimization results. Full article
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Open AccessArticle
Using Text Mining to Estimate Schedule Delay Risk of 13 Offshore Oil and Gas EPC Case Studies During the Bidding Process
Energies 2019, 12(10), 1956; https://doi.org/10.3390/en12101956
Received: 21 April 2019 / Revised: 9 May 2019 / Accepted: 17 May 2019 / Published: 22 May 2019
PDF Full-text (1002 KB) | HTML Full-text | XML Full-text
Abstract
Korean offshore oil and gas (O&G) mega project contractors have recently suffered massive deficits due to the challenges and risks inherent to the offshore engineering, procurement, and construction (EPC) of megaprojects. This has resulted in frequent prolonged projects, schedule delay, and consequently significant [...] Read more.
Korean offshore oil and gas (O&G) mega project contractors have recently suffered massive deficits due to the challenges and risks inherent to the offshore engineering, procurement, and construction (EPC) of megaprojects. This has resulted in frequent prolonged projects, schedule delay, and consequently significant cost overruns. Existing literature has identified one of the major causes of project delays to be the lack of adequate tools or techniques to diagnose the appropriateness and sufficiency of the contract deadline proposed by project owners prior to signing the contract in the bid. As such, this paper seeks to propose appropriate or correct project durations using the research methodology of text mining for bid documents. With the emergence of ‘big data’ research, text mining has become an acceptable research strategy, having already been utilized in various industries including medicine, legal, and securities. In this study the scope of work (SOW), as a main part of EPC contracts is analyzed using text mining processes in a sequence of pre-processing, structuring, and normalizing. Lessons learned, collected from 13 executed off shore EPC projects, are then used to reinforce the findings from said process. For this study, critical terms (CT), representing the root of past problems, are selected from the reports of lessons learned. The occurrence of the CT in the SOW are then counted and converted to a schedule delay risk index (SDRI) for the sample projects. The measured SDRI of each sample project are then correlated to the project’s actual schedule delay via regression analysis. The resultant regression model is entitled the schedule delay estimate model (SDEM) for this paper based on the case studies. Finally, the developed SDEM’s accuracy is validated through its use to predict schedule delays on recently executed projects with the findings being compared with actual schedule performance. This study found the relationship between the SDRI, frequency of CTs in the SOW, and delays to be represented by the regression formula. Through assessing its performance with respect to the 13th project, said formula was found to have an accuracy of 81%. As can be seen, this study found that more CTs in the SOW leads to a higher tendency for a schedule delay. Therefore, a higher project SDRI implies that there are more issues on projects which required more time to resolve them. While the low number of projects used to develop the model reduces its generalizability, the text mining research methodology used to quantitatively estimate project schedule delay can be generalized and applied to other industries where contractual documents and information regarding lessons learned are available. Full article
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