Special Issue "Project Intelligence and Management"

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

Deadline for manuscript submissions: 30 November 2021.

Special Issue Editors

Prof. Dr. Jui-Sheng (Rayson) Chou
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Guest Editor
Distinguished Professor in Project Management, Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
Interests: civil & hydraulic engineering informatics; project quantitative analytics for sustainable engineering and the built environment; decision, risk, failure analysis & disaster management
Special Issues and Collections in MDPI journals
Prof. Dr. Soonwook Kwon
Website
Guest Editor
Sungkyunkwan University, Korea
Interests: Reverse Engineering & Rapid Prototyping in AEC (Laser Scanning, Image Capturing (Drone, Mobile Camera); Advanced Construction Technology for Super High-Rise Building; Construction Robotics; IoT based Construction and Maintenance; Cloud and Big data based BIM service
Prof. Dr. Wai Oswald Chong
Website
Guest Editor
Arizona State University, United States
Interests: knowledge mining and modeling; modeling and simulation; cloud technology; predictive analytics; engineering knowledge; information technology; life-cycle analysis; systems behaviors

Special Issue Information

Dear Colleagues,

The applications of and research in project management and sustainable operations have grown rapidly at an exponential rate over the past decades. This Special Issue will focus on the latest advances in novel technologies applying for project development and sustainable engineering disciplines, which are too complex to be solved following a conventional approach and require the application of new techniques, analytical tools, and simulation of support for practitioners to elucidate real-world problems, thus improving the quality of an urban living environment. It will provide a platform for academicians, researchers, and engineers to share their experience and smart solutions to problems in various areas of civil engineering and project management for efficient use of available assets, limited resources, and existing infrastructure in the developing or developed cities.

The scope of the Special Issue includes but is not limited to the followings:

  • Advanced construction technology for super high-rise buildings
  • Applied machine learning with engineering data
  • Big Data and cloud computing with civil engineering applications
  • ChatBot applications for engineering project management
  • Cloud and Big Data-based BIM service
  • Construction robotics
  • Deep learning applications for engineering informatics
  • Energy performance and efficiency
  • Energy system management and engineering
  • Hazards forecasting, response, and mitigation
  • Implementation and deployment of computing systems for engineering applications
  • Infrastructure informatics
  • IoT-based construction and maintenance
  • Modeling, simulation, and optimization for civil, architectural, and environmental engineering
  • Project management and data analytics
  • Real-time prediction for project management and engineering applications
  • Reverse engineering and rapid prototyping in AEC (laser scanning, image capturing (drone, mobile camera)
  • Robotics for engineering automation
  • Sustainable engineering and operations
  • Water resources engineering and management

Prof. Dr. Jui-Sheng Chou
Prof. Dr. Soonwook Kwon
Prof. Dr. Wai Oswald Chong
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 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. 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 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

  • project management
  • sustainable development
  • civil and hydraulic engineering
  • engineering informatics
  • project quantitative analytics
  • automation
  • computer technology
  • infrastructure management
  • hazard management

Published Papers (6 papers)

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Research

Open AccessArticle
The Use of Parallel Computing to Accelerate Fire Simulations for Cultural Heritage Buildings
Sustainability 2020, 12(23), 10005; https://doi.org/10.3390/su122310005 - 30 Nov 2020
Abstract
This study proposes an optimization design to improve the accuracy of fire risk models by combining the results of the UFSM (Urban Fire Spread Model, Japan) with the United States (US) Fire Simulation Software FDS6.7.3 (Fire Dynamics Simulator, FDS). Using parallel processing, the [...] Read more.
This study proposes an optimization design to improve the accuracy of fire risk models by combining the results of the UFSM (Urban Fire Spread Model, Japan) with the United States (US) Fire Simulation Software FDS6.7.3 (Fire Dynamics Simulator, FDS). Using parallel processing, the simulation time was dramatically reduced, and this may assist the risk factor analysis of buildings in a large area. Fire destroyed all seven main structures of the Shuri Castle World Heritage site on 31 October 2019, and this tool may have identified risk factors, which could have been mitigated and potentially prevented the building loss. Other historical buildings may benefit from using this tool to identify their relevant risk factors. This study completed a full-scale simulation of the 76 m × 45 m × 15 m area, which contained the nine temples, with 6.4 million grids for a simulation time of 600 s in 45 h. This tool can assist in input-data risk factor analysis and contribute to the improvement of protection technology for cultural heritage buildings. Full article
(This article belongs to the Special Issue Project Intelligence and Management)
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Open AccessArticle
Design of Predictive Models to Estimate Corrosion in Buried Steel Structures
Sustainability 2020, 12(23), 9879; https://doi.org/10.3390/su12239879 - 26 Nov 2020
Abstract
Corrosion is the main mechanism of the degradation of steel structures buried in the soil. Due to its aggressiveness, the material gradually loses thickness until the structure fails, which may cause serious environmental problems. The lack of a clearly established method in the [...] Read more.
Corrosion is the main mechanism of the degradation of steel structures buried in the soil. Due to its aggressiveness, the material gradually loses thickness until the structure fails, which may cause serious environmental problems. The lack of a clearly established method in the design leads to the need for conservative excess thicknesses to ensure their useful life. This implies inefficient use of steel and an increase in the cost of the structure. In this paper, four quantitative and multivariate models were created to predict the loss of buried steel as a function of time. We developed a basic model, as well as a physical and an electrochemical one, based on multivariate adaptive regression spline (MARS), and a simpler model for comparative purposes based on clusters with Euclidean distance. The modeling was synthesized in a computer tool where the inputs were the characteristics of the soil and the time and the outputs were the loss of thickness of each predictive model and the description of the most similar real tests. The results showed that in all models, for relative errors of 10%, over 90% of predictions were correct. In addition, a real example of the operation of the tool was defined, where it was found that the estimates of the models allow the necessary optimization of steel to fulfill its useful life. Full article
(This article belongs to the Special Issue Project Intelligence and Management)
Open AccessArticle
Multivariable Analysis of Transport Network Seismic Performance: Mexico City
Sustainability 2020, 12(22), 9726; https://doi.org/10.3390/su12229726 - 21 Nov 2020
Abstract
In densely populated urban areas, predicting the post-earthquake performance of a transport network is a particularly challenging task that requires the integration of modeled structural seismic response, damage scenarios, and resulting traffic behavior. Previous approaches assessing the vulnerability and performance of networks after [...] Read more.
In densely populated urban areas, predicting the post-earthquake performance of a transport network is a particularly challenging task that requires the integration of modeled structural seismic response, damage scenarios, and resulting traffic behavior. Previous approaches assessing the vulnerability and performance of networks after earthquakes have not succeeded in capturing and estimating the interdependencies between seismic risk parameters and key traffic behavior variables. This paper presents a methodology, based on data analysis and optimization, where the dynamic traffic modeling and probabilistic seismic hazard assessment are coupled, to link and characterize key network performance variables after extreme earthquakes and establish a multivariable seismic performance measure. The methodology is used to study the transport network in the southern part of Mexico City for a set of scenarios. The seismic environment is established through uniform hazard spectra derived for firm soil. Damage to structures is estimated considering site response and using fragility functions. Dynamic traffic modeling is developed to simulate damage-induced road closures and resulting in traffic variations. Post-earthquake network performance is evaluated through data envelopment analyses, obtaining sets of seismic performance boundaries, and seismic performance maps. The methodology offers a quantitative tool with applications in the planning of urban areas that are sustainable and seismic resilient. Full article
(This article belongs to the Special Issue Project Intelligence and Management)
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Open AccessArticle
The Impact of Project Manager Soft Competences on Project Sustainability
Sustainability 2020, 12(16), 6537; https://doi.org/10.3390/su12166537 - 13 Aug 2020
Abstract
The current study suggests a different and innovative view by testing a unique combination of variables, which are unproven in a single model for the purpose of increasing the ratio of sustainable projects. The project manager can use the model to look their [...] Read more.
The current study suggests a different and innovative view by testing a unique combination of variables, which are unproven in a single model for the purpose of increasing the ratio of sustainable projects. The project manager can use the model to look their projects and can compose necessary changes for better outcomes. The study objects to postulate the competence breach of project managers with regard to sustainability, and to deliver direction that how to fulfill the research gap. The given work is centered on the result of project supervisor soft capabilities on project sustainability mediated by innovation. To achieve this aim, deductive approach was adopted. The sample size of the study was 242 respondents, and data were collected from software houses. The collected data were then analyzed by doing the structural equation modeling in PLS-SEM in order to examine the relationships. The outcomes demonstrate positive impact of project manager soft competences on project sustainability and mediating impact of innovation among the relationship of project manager soft competences and project sustainability. Innovation is directly linked to project sustainable development, and was accepted, which aligns with the previous studies. This research reflects the role of project manager soft competences on innovation and project sustainability. The study is unique in its scope and implications as the focus is upon empirical investigation of the project manager soft competences and project sustainability in the context of Pakistan. Full article
(This article belongs to the Special Issue Project Intelligence and Management)
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Open AccessArticle
The Development of a Decision Support Model for Eco-Friendly Material Selection in Vietnam
Sustainability 2020, 12(7), 2769; https://doi.org/10.3390/su12072769 - 01 Apr 2020
Cited by 2
Abstract
In recent years, the awareness of sustainable construction has increasingly risen in countries around the world, with the main goal being to avoid depleting energy resources and raw materials and to greatly reduce carbon emissions. Therefore, the selection of eco-friendly building materials becomes [...] Read more.
In recent years, the awareness of sustainable construction has increasingly risen in countries around the world, with the main goal being to avoid depleting energy resources and raw materials and to greatly reduce carbon emissions. Therefore, the selection of eco-friendly building materials becomes a difficult task and choosing the best construction strategy is a complicated process. Most of the studies of the building material selection often focus on optimizing material-related green building scores with budget constraints based on the environmental impacts of those materials. However, these studies do not pay attention to the impact of sustainable materials on two important aspects of a project: The initial investment cost and the total labor-working days. Hence, this study developed a model that optimizes a material mix for buildings considering the building budget, total labor-working days, and material-related green building scores. A case study in Vietnam was conducted to illustrate the effectiveness of the proposed model. This proposed model provides a guidance for decision-makers in selecting approximate materials for buildings toward sustainability. Full article
(This article belongs to the Special Issue Project Intelligence and Management)
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Open AccessArticle
Predicting Microbial Species in a River Based on Physicochemical Properties by Bio-Inspired Metaheuristic Optimized Machine Learning
Sustainability 2019, 11(24), 6889; https://doi.org/10.3390/su11246889 - 04 Dec 2019
Cited by 1
Abstract
The main goal of the analysis of microbial ecology is to understand the relationship between Earth’s microbial community and their functions in the environment. This paper presents a proof-of-concept research to develop a bioclimatic modeling approach that leverages artificial intelligence techniques to identify [...] Read more.
The main goal of the analysis of microbial ecology is to understand the relationship between Earth’s microbial community and their functions in the environment. This paper presents a proof-of-concept research to develop a bioclimatic modeling approach that leverages artificial intelligence techniques to identify the microbial species in a river as a function of physicochemical parameters. Feature reduction and selection are both utilized in the data preprocessing owing to the scarce of available data points collected and missing values of physicochemical attributes from a river in Southeast China. A bio-inspired metaheuristic optimized machine learner, which supports the adjustment to the multiple-output prediction form, is used in bioclimatic modeling. The accuracy of prediction and applicability of the model can help microbiologists and ecologists in quantifying the predicted microbial species for further experimental planning with minimal expenditure, which is become one of the most serious issues when facing dramatic changes of environmental conditions caused by global warming. This work demonstrates a neoteric approach for potential use in predicting preliminary microbial structures in the environment. Full article
(This article belongs to the Special Issue Project Intelligence and Management)
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