Special Issue "Decision Support Systems for the Digital Built Environment"

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Construction Management, and Computers & Digitization".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 13721

Special Issue Editors

Dr. António Aguiar Costa
E-Mail Website
Guest Editor
BUILT CoLAB—Collaborative Laboratory for the Digital Built Environment, Lisboa, Portugal
Interests: BIM; BIM implementation; BIM-LCA; digital construction; intelligent design; intelligent buildings
Dr. Manuel Parente
E-Mail Website
Guest Editor
BUILT CoLAB—Collaborative Laboratory for the Digital Built Environment, Lisboa, Portugal
Interests: optimization, heuristics, and meta-heuristics; dynamic programming; operations research; simulation optimization; machine learning; artificial intelligence; decision support systems; distributed ledger technologies; engineering applications of the above

Special Issue Information

Dear Colleagues,

The digitalization of the construction industry is accelerating the development of several digital tools to support decision making in the built environment, spanning all phases of construction projects, from the bidding, design, and planning stages, to construction and maintenance stages. These digital tools, based on improving technologies, enable more integrated and intelligent approaches to decision making, both promoting and supporting a more efficient and sustainable built environment.

Recent technologies such as BIM, IoT, digital twins, and artificial intelligence are progressively applied in construction, promoting interconnected and knowledge-based decisions. This Special Issue aims to gather new research and development regarding innovative decision support systems for the built environment, which are strongly supported by recent digital technologies.

Dr. António Aguiar Costa
Dr. Manuel Parente
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. Buildings is an international peer-reviewed open access monthly 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 2000 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

  • BIM
  • Digital twin
  • Artificial intelligence
  • Decision support system
  • Machine learning
  • Optimization
  • Sustainability
  • Productivity

Published Papers (7 papers)

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Research

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Article
Development of an Open Government Data (OGD) Evaluation Framework for BIM
Buildings 2022, 12(4), 490; https://doi.org/10.3390/buildings12040490 - 14 Apr 2022
Viewed by 1170
Abstract
Open government data (OGD) provide an opportunity for developing various services by disclosing information monopolized by the government to the public so that the private sector can use it. The private sector is utilizing this to improve the work efficiency and productivity by [...] Read more.
Open government data (OGD) provide an opportunity for developing various services by disclosing information monopolized by the government to the public so that the private sector can use it. The private sector is utilizing this to improve the work efficiency and productivity by collecting, analyzing, and reprocessing OGD for various work steps of a BIM-based design project. However, most studies on OGD focus on the functionality and usability of data portals and the factors for evaluating the data itself such as openness, accountability, and transparency. This study aims to provide an evaluation framework for OGD for the AEC industry to assess the data utilization environment in order to improve the productivity of BIM-based projects. Several OGD principles found within related literature are discussed, and from them we extract evaluation framework levels. Then, we validate the proposed framework by applying it to a case of developing a BIM-based design support system using OGD datasets. This research concludes by suggesting that to effectively utilize OGD in the construction industry, the private sector should simply view data after collecting them, create an institutional environment for creating new values by reprocessing data, and build an active data utilization roadmap based on this environment. Full article
(This article belongs to the Special Issue Decision Support Systems for the Digital Built Environment)
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Article
Semi-Supervised Clustering for Architectural Modularisation
Buildings 2022, 12(3), 303; https://doi.org/10.3390/buildings12030303 - 04 Mar 2022
Viewed by 1403
Abstract
Modular construction allows for a faster, safer, better controlled, and more productive construction process, yielding quality results with low risk and controlled costs. However, despite the potential advantages of this methodology, its adoption has remained slow due to the reasonably high degree of [...] Read more.
Modular construction allows for a faster, safer, better controlled, and more productive construction process, yielding quality results with low risk and controlled costs. However, despite the potential advantages of this methodology, its adoption has remained slow due to the reasonably high degree of standardisation and repetition that projects require, inexorably clashing with the unique building designs created to meet the clients’ needs. The present article proposes performing a modularisation process after the building design is complete, reaping most benefits of modular construction while preserving the unique vision and design of the building. This objective is achieved by implementing a semi-supervised methodology reliant on the clustering of individual rooms and subsequent user validation of the obtained clusters to identify base modules representative of each cluster. The proposed methodology is applied in a case study of an existing apartment complex, in which the modularisation process was previously performed manually—thus serving as a baseline. The acquired results display a 99.6% reduction in the modularisation process’ duration, while maintaining a 96.4% Normalised Mutual Information Score and a 93.3% Adjusted Mutual Information Score, justifying the continuous development and assessment of the methodology in future works. Full article
(This article belongs to the Special Issue Decision Support Systems for the Digital Built Environment)
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Article
Parametric Optimization and Decision Support Model Framework for Life Cycle Cost Analysis and Life Cycle Assessment of Flexible Industrial Building Structures Integrating Production Planning
Buildings 2022, 12(2), 162; https://doi.org/10.3390/buildings12020162 - 02 Feb 2022
Cited by 5 | Viewed by 1306
Abstract
Most industrial buildings have a very short lifespan due to frequently changing production processes. The load-bearing structure severely limits the flexibility of industrial buildings and is a major contributor to their costs, carbon footprint and waste. This paper presents a parametric optimization and [...] Read more.
Most industrial buildings have a very short lifespan due to frequently changing production processes. The load-bearing structure severely limits the flexibility of industrial buildings and is a major contributor to their costs, carbon footprint and waste. This paper presents a parametric optimization and decision support (POD) model framework that enables automated structural analysis and simultaneous calculation of life cycle cost (LCC), life cycle assessment (LCA), recycling potential and flexibility assessment. A method for integrating production planning into early structural design extends the framework to consider the impact of changing production processes on the footprint of building structures already at an early design stage. With the introduction of a novel grading system, design teams can quickly compare the performance of different building variants to improve decision making. The POD model framework is tested by means of a variant study on a pilot project from a food and hygiene production facility. The results demonstrate the effectiveness of the framework for identifying potential economic and environmental savings, specifying alternative building materials, and finding low-impact industrial structures and enclosure variants. When comparing the examined building variants, significant differences in the LCC (63%), global warming potential (62%) and flexibility (55%) of the structural designs were identified. In future research, a multi-objective optimization algorithm will be implemented to automate the design search and thus improve the decision-making process. Full article
(This article belongs to the Special Issue Decision Support Systems for the Digital Built Environment)
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Article
Exploring Natural Language Processing in Construction and Integration with Building Information Modeling: A Scientometric Analysis
Buildings 2021, 11(12), 583; https://doi.org/10.3390/buildings11120583 - 25 Nov 2021
Cited by 5 | Viewed by 2732
Abstract
The European Union (EU) aims to increase the efficiency and productivity of the construction industry. The EU suggests pairing Building Information Modeling with other digitalization technologies to seize the full potential of the digital transition. Meanwhile, industrial applications of Natural Language Processing (NLP) [...] Read more.
The European Union (EU) aims to increase the efficiency and productivity of the construction industry. The EU suggests pairing Building Information Modeling with other digitalization technologies to seize the full potential of the digital transition. Meanwhile, industrial applications of Natural Language Processing (NLP) have emerged. The growth of NLP is affecting the construction industry. However, the potential of NLP and the combination of an NLP and BIM approach is still unexplored. The study tries to address this lack by applying a scientometric analysis to explore the state of the art of NLP in the AECO sector, and the combined applications of NLP and BIM. Science mapping is used to analyze 254 bibliographic records from Scopus Database analyzing the structure and dynamics of the domain by drawing a picture of the body of knowledge. NLP in AECO, and its pairing with BIM domain and applications, are investigated by representing: Conceptual, Intellectual, and Social structure. The highest number of NLP applications in AECO are in the fields of Project, Safety, and Risk Management. Attempts at combining NLP and BIM mainly concern the Automated Compliance Checking and semantic BIM enrichment goals. Artificial intelligence, learning algorithms, and ontologies emerge as the most widespread and promising technological drivers. Full article
(This article belongs to the Special Issue Decision Support Systems for the Digital Built Environment)
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Article
A Gabor Filter-Based Protocol for Automated Image-Based Building Detection
Buildings 2021, 11(7), 302; https://doi.org/10.3390/buildings11070302 - 08 Jul 2021
Cited by 13 | Viewed by 2089
Abstract
Detecting buildings from high-resolution satellite imagery is beneficial in mapping, environmental preparation, disaster management, military planning, urban planning and research purposes. Differentiating buildings from the images is possible however, it may be a time-consuming or complicated process. Therefore, the high-resolution imagery from satellites [...] Read more.
Detecting buildings from high-resolution satellite imagery is beneficial in mapping, environmental preparation, disaster management, military planning, urban planning and research purposes. Differentiating buildings from the images is possible however, it may be a time-consuming or complicated process. Therefore, the high-resolution imagery from satellites needs to be automated to detect the buildings. Additionally, buildings exhibit several different characteristics, and their appearance in these images is unplanned. Moreover, buildings in the metropolitan environment are typically crowded and complicated. Therefore, it is challenging to identify the building and hard to locate them. To resolve this situation, a novel probabilistic method has been suggested using local features and probabilistic approaches. A local feature extraction technique was implemented, which was used to calculate the probability density function. The locations in the image were represented as joint probability distributions and were used to estimate their probability distribution function (pdf). The density of building locations in the image was extracted. Kernel density distribution was also used to find the density flow for different metropolitan cities such as Sydney (Australia), Tokyo (Japan), and Mumbai (India), which is useful for distribution intensity and pattern of facility point f interest (POI). The purpose system can detect buildings/rooftops and to test our system, we choose some crops with panchromatic high-resolution satellite images from Australia and our results looks promising with high efficiency and minimal computational time for feature extraction. We were able to detect buildings with shadows and building without shadows in 0.4468 (seconds) and 0.5126 (seconds) respectively. Full article
(This article belongs to the Special Issue Decision Support Systems for the Digital Built Environment)
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Article
Risk Identification, Assessments, and Prediction for Mega Construction Projects: A Risk Prediction Paradigm Based on Cross Analytical-Machine Learning Model
Buildings 2021, 11(4), 172; https://doi.org/10.3390/buildings11040172 - 17 Apr 2021
Cited by 13 | Viewed by 2525
Abstract
Risk identification and management are the two most important parts of construction project management. Better risk management can help in determining the future consequences, but identifying possible risk factors has a direct and indirect impact on the risk management process. In this paper, [...] Read more.
Risk identification and management are the two most important parts of construction project management. Better risk management can help in determining the future consequences, but identifying possible risk factors has a direct and indirect impact on the risk management process. In this paper, a risk prediction system based on a cross analytical-machine learning model was developed for construction megaprojects. A total of 63 risk factors pertaining to the cost, time, quality, and scope of the megaproject and primary data were collected from industry experts on a five-point Likert scale. The obtained sample was further processed statistically to generate a significantly large set of features to perform K-means clustering based on high-risk factor and allied sub-risk component identification. Descriptive analysis, followed by the synthetic minority over-sampling technique (SMOTE) and the Wilcoxon rank-sum test was performed to retain the most significant features pertaining to cost, time, quality, and scope. Eventually, unlike classical K-means clustering, a genetic-algorithm-based K-means clustering algorithm (GA–K-means) was applied with dual-objective functions to segment high-risk factors and allied sub-risk components. The proposed model identified different high-risk factors and sub-risk factors, which cumulatively can impact overall performance. Thus, identifying these high-risk factors and corresponding sub-risk components can help stakeholders in achieving project success. Full article
(This article belongs to the Special Issue Decision Support Systems for the Digital Built Environment)
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Review

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Review
Integration of Smart Pavement Data with Decision Support Systems: A Systematic Review
Buildings 2021, 11(12), 579; https://doi.org/10.3390/buildings11120579 - 25 Nov 2021
Cited by 3 | Viewed by 1425
Abstract
Nowadays, pavement management systems (PMS) are mainly based on monitoring processes that have been established for a long time, and strongly depend on acquired experience. However, with the emergence of smart technologies, such as internet of things and artificial intelligence, PMS could be [...] Read more.
Nowadays, pavement management systems (PMS) are mainly based on monitoring processes that have been established for a long time, and strongly depend on acquired experience. However, with the emergence of smart technologies, such as internet of things and artificial intelligence, PMS could be improved by applying these new smart technologies to their decision support systems, not just by updating their data collection methodologies, but also their data analysis tools. The application of these smart technologies to the field of pavement monitoring and condition evaluation will undoubtedly contribute to more efficient, less costly, safer, and environmentally friendly methodologies. Thus, the main drive of the present work is to provide insight for the development of future decision support systems for smart pavement management by conducting a systematic literature review of the developed works that apply smart technologies to this field. The conclusions drawn from the analysis allowed for the identification of a series of future direction recommendations for researchers. In fact, future PMS should tend to be capable of collecting and analyzing data at different levels, both externally at the surface or inside the pavement, as well as to detect and predict all types of functional and structural flaws and defects. Full article
(This article belongs to the Special Issue Decision Support Systems for the Digital Built Environment)
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