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Applications of Engineering Digitalization and Construction IT for Energy Projects

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

Deadline for manuscript submissions: closed (31 May 2020) | Viewed by 34753

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Special Issue Editors

Prof. Dr. Eul-Bum Lee

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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
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Prof. Dr. H. David Jeong

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Department of Construction Science, Texas A&M University, College Station, TX 77843, USA
Interests: data analytics and 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 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. 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 2600 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 (5 papers)

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Research

24 pages, 3511 KiB

Open AccessFeature PaperArticle

Solving Scheduling Problem in a Distributed Manufacturing System Using a Discrete Fruit Fly Optimization Algorithm

by Xiaohui Zhang, Xinhua Liu, Shufeng Tang, Grzegorz Królczyk and Zhixiong Li

Energies 2019, 12(17), 3260; https://doi.org/10.3390/en12173260 - 23 Aug 2019

Cited by 22 | Viewed by 2536

Abstract

This study attempts to optimize the scheduling decision to save production cost (e.g., energy consumption) in a distributed manufacturing environment that comprises multiple distributed factories and where each factory has one flow shop with blocking constraints. A new scheduling optimization model is developed based on a discrete fruit fly optimization algorithm (DFOA). In this new evolutionary optimization method, three heuristic methods were proposed to initialize the DFOA model with good quality and diversity. In the smell-based search phase of DFOA, four neighborhood structures according to factory reassignment and job sequencing adjustment were designed to help explore a larger solution space. Furthermore, two local search methods were incorporated into the framework of variable neighborhood descent (VND) to enhance exploitation. In the vision-based search phase, an effective update criterion was developed. Hence, the proposed DFOA has a large probability to find an optimal solution to the scheduling optimization problem. Experimental validation was performed to evaluate the effectiveness of the proposed initialization schemes, neighborhood strategy, and local search methods. Additionally, the proposed DFOA was compared with well-known heuristics and metaheuristics on small-scale and large-scale test instances. The analysis results demonstrate that the search and optimization ability of the proposed DFOA is superior to well-known algorithms on precision and convergence. Full article

(This article belongs to the Special Issue Applications of Engineering Digitalization and Construction IT for Energy Projects)

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19 pages, 5006 KiB

Open AccessFeature PaperArticle

A Novel Multi-Agent-Based Collaborative Virtual Manufacturing Environment Integrated with Edge Computing Technique

by Xiaohui Zhang, Shufeng Tang, Xinhua Liu, Reza Malekian and Zhixiong Li

Energies 2019, 12(14), 2815; https://doi.org/10.3390/en12142815 - 22 Jul 2019

Cited by 15 | Viewed by 3480

Abstract

This paper proposes a multi-agent-based collaborative virtual manufacturing environment (VME) to save energy consumption and improve efficiency in the manufacturing process. In order to achieve the high autonomy of the manufacturing system, a multi-agent system (MAS) is designed to build a collaborative VME. In this new VME environment, edge computing is embedded to strengthen the cyber resource utilization and system economy. Moreover, an efficient communication channel between networks is proposed. The subsequent cooperation and collaboration protocols among agents are designed to ensure flexible and process-oriented operations. Furthermore, the fuzzy resolution algorithm is employed to resolve the competition conflicts among function-similar MASs in the distributed manufacturing scenario. Lastly, a simulation and case study are performed to evaluate the performance of the proposed VME in Internet of Things (IoT)-based manufacturing. The analysis results have demonstrated the feasibility and effectiveness of the proposed VME system. Full article

(This article belongs to the Special Issue Applications of Engineering Digitalization and Construction IT for Energy Projects)

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Graphical abstract

26 pages, 6378 KiB

Open AccessArticle

A Digitization and Conversion Tool for Imaged Drawings to Intelligent Piping and Instrumentation Diagrams (P&ID)

by Sung-O Kang, Eul-Bum Lee and Hum-Kyung Baek

Energies 2019, 12(13), 2593; https://doi.org/10.3390/en12132593 - 5 Jul 2019

Cited by 44 | Viewed by 18994

Abstract

In the Fourth Industrial Revolution, artificial intelligence technology and big data science are emerging rapidly. To apply these informational technologies to the engineering industries, it is essential to digitize the data that are currently archived in image or hard-copy format. For previously created design drawings, the consistency between the design products is reduced in the digitization process, and the accuracy and reliability of estimates of the equipment and materials by the digitized drawings are remarkably low. In this paper, we propose a method and system of automatically recognizing and extracting design information from imaged piping and instrumentation diagram (P&ID) drawings and automatically generating digitized drawings based on the extracted data by using digital image processing techniques such as template matching and sliding window method. First, the symbols are recognized by template matching and extracted from the imaged P&ID drawing and registered automatically in the database. Then, lines and text are recognized and extracted from in the imaged P&ID drawing using the sliding window method and aspect ratio calculation, respectively. The extracted symbols for equipment and lines are associated with the attributes of the closest text and are stored in the database in neutral format. It is mapped with the predefined intelligent P&ID information and transformed to commercial P&ID tool formats with the associated information stored. As illustrated through the validation case studies, the intelligent digitized drawings generated by the above automatic conversion system, the consistency of the design product is maintained, and the problems experienced with the traditional and manual P&ID input method by engineering companies, such as time consumption, missing items, and misspellings, are solved through the final fine-tune validation process. Full article

(This article belongs to the Special Issue Applications of Engineering Digitalization and Construction IT for Energy Projects)

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18 pages, 2638 KiB

Open AccessArticle

A Forecast Model for the Level of Engineering Maturity Impact on Contractor’s Procurement and Construction Costs for Offshore EPC Megaprojects

by Myung-Hun Kim and Eul-Bum Lee

Energies 2019, 12(12), 2295; https://doi.org/10.3390/en12122295 - 16 Jun 2019

Cited by 8 | Viewed by 4492

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 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

(This article belongs to the Special Issue Applications of Engineering Digitalization and Construction IT for Energy Projects)

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25 pages, 1002 KiB

Open AccessArticle

Using Text Mining to Estimate Schedule Delay Risk of 13 Offshore Oil and Gas EPC Case Studies During the Bidding Process

by Byung-Yun Son and Eul-Bum Lee

Energies 2019, 12(10), 1956; https://doi.org/10.3390/en12101956 - 22 May 2019

Cited by 19 | Viewed by 4553

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 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

(This article belongs to the Special Issue Applications of Engineering Digitalization and Construction IT for Energy Projects)

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