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Applied Sciences
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Big Data in Construction Engineering and Management
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Big Data in Construction Engineering and Management

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".

Deadline for manuscript submissions: closed (10 January 2022) | Viewed by 5892

Special Issue Editors

Prof. Dr. Krzysztof Zima

E-Mail Website
Guest Editor
Faculty of Civil Engineering, Cracow University of Technology, 31-155 Kraków, Poland
Interests: construction cost estimation; building information modeling technology; design and building and integrated project delivery; activity of developer companies; construction defects; sustainable construction and using case-based reasoning and fuzzy logic in construction management
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Agnieszka Leśniak

E-Mail Website
Guest Editor
Faculty of Civil Engineering, Cracow University of Technology, 31-155 Kraków, Poland
Interests: supporting decisions in construction; delays in construction projects; risk assessment in construction; project cost estimation; tendering and bidding in construction; using artificial neural networks in construction management; building procurement
Special Issues, Collections and Topics in MDPI journals
Dr. María Dolores Andújar-Montoya

E-Mail Website
Guest Editor
Building Sciences and Urbanism Department, University of Alicante, 03690 Alicante, Spain
Interests: automation of construction management; lean construction paradigm; building information modelling technology; smart management; distributed sensors, smart sensor networks and internet-of-things in construction sites
Special Issues, Collections and Topics in MDPI journals
Dr. Ali Ghaffarian Hoseini

E-Mail Website
Guest Editor
Built Environment Engineering Department, Auckland University of Technology, Auckland 1010, New Zealand
Interests: integrated BIM; design cognition and computing; building automation systems; energy-efficient buildings; green building developments; environmental design behavior; sustainable design developments; adaptive environments; smart housing; intelligent buildings; artificial intelligence in design and construction; virtual reality (VR); augmented reality (AR); integrated design studies

Special Issue Information

Dear Colleagues,

The phenomenon of Big Data has gained tremendous importance in solving complex engineering problems in recent years, with various applications and significant impacts in the construction engineering and management domain. With monitoring systems supported by modern technologies, the use of data from the Internet and the creation of databases based on historical construction projects records, the application of Big Data supports the processing of all such available data on a larger scale. Availability of large data sets and access to cheaper sensors have acted as catalysts for an increasing interest towards adoption of Big Data practices.

Though the concept of Big data has only made inroads into the construction sector in recent years, its proven capabilities have enabled the construction sector to noticeably improve a wide range of processes. These enhanced processes have resulted in achieving satisfactory outcome in various areas: better management, more accurate budget estimates, lower project risks and guidance in making the right decisions and choices, etc. It is estimated that the adoption of Big Data solutions will be necessary for construction companies to deliver projects successfully and remain competitive in an increasingly globalized market.

The aim of this Special Issue (SI) is to review the development and key applications of new Big Data tools and methods in construction engineering and management. The aims are to further knowledge of the topic, provoke broader discussions, and raise awareness of the potential to employ various applications of Big Data in modern construction engineering and management practices. This SI would act as an international platform to showcase emergent findings and contribute to generating new knowledge in this growing field of research.

This Special Issue welcomes various submission types, such as original research contributions, case studies, comparative studies, conceptual papers, and review studies.

Topics of interest within the construction engineering and management are presented below but are not limited to:

  • BIM and integration with Big Data;
  • Building information systems using Big Data;
  • Big Data in construction management;
  • Disaster management with Big Data;
  • Big Data applications in Civil Engineering;
  • Big Data in predictive maintenance of constructed facilities;
  • Big Data case studies in the built environment;
  • New tools and software for Big Data in the construction industry;
  • Materials science and engineering based on Big Data analysis;
  • Structural and environmental engineering using Big Data;
  • Solutions for Big Data storage, visualization and analytics.

Prof. Dr. Krzysztof Zima
Prof. Dr. Agnieszka Leśniak
Dr. María Dolores Andújar-Montoya
Dr. Ali Ghaffarian Hoseini
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. Applied Sciences 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.

Published Papers (2 papers)

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14 pages, 291 KiB  
Article
Fuzzy Relations Matrixes of Damages and Technical Wear Related to Apartment Houses
by Jarosław Konior and Tomasz Stachoń
Appl. Sci. 2021, 11(5), 2223; https://doi.org/10.3390/app11052223 - 03 Mar 2021
Viewed by 1123
Abstract
The research presented in this article was conducted on a representative and purposefully selected sample of 102 residential buildings that were erected in the second half of the nineteenth and early twentieth centuries in the downtown district of Wroclaw (Poland). The degree of [...] Read more.
The research presented in this article was conducted on a representative and purposefully selected sample of 102 residential buildings that were erected in the second half of the nineteenth and early twentieth centuries in the downtown district of Wroclaw (Poland). The degree of the technical wear of an old residential building is determined by the conditions of its maintenance and operation. The diagnosis of the impact of the maintenance of residential buildings on the degree of their technical wear was carried out using quantitative methods in the categories of fuzzy sets and also by using the authors’ own models created in fuzzy conditions. It was proved that the expression of the operational state of a building, considered as the process that plays the greatest role in its accelerated destruction, is mechanical damage to the internal structure of its elements. This damage is determined in the categories of fuzzy sets and has a high frequency and a cumulative effect of occurrence, which are characteristic for buildings in satisfactory and average maintenance conditions. The use of simple operations in fuzzy set calculus enabled the impact of elementary damage that occurs with a specific frequency, as well as the measure of its correlation on the observed technical wear of building elements to be considered. As a result, it was possible to identify the elementary damage that determines the degree of the technical wear of a building element. For each of the selected building elements, the maximal and minimal fuzzy relational equations (damage and technical wear) were determined. Their solutions were given in the form of clear relational matrixes that constitute big data arrays. They define the domain and range of the maximal and minimal fuzzy relations, the height of the fuzzy relations, their differences, and the place of their occurrence between the maximal and minimal dependencies. Full article
(This article belongs to the Special Issue Big Data in Construction Engineering and Management)

Other

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23 pages, 5540 KiB  
Case Report
Prediction of the Energy Consumption of School Buildings
by Adel Alshibani
Appl. Sci. 2020, 10(17), 5885; https://doi.org/10.3390/app10175885 - 25 Aug 2020
Cited by 15 | Viewed by 3468
Abstract
The energy consumption of a constructed facility is a primary concern as a result of its impact on the total energy expenditure. It has been found that up to 70% of the power consumption in Saudi Arabia are caused by building structures and [...] Read more.
The energy consumption of a constructed facility is a primary concern as a result of its impact on the total energy expenditure. It has been found that up to 70% of the power consumption in Saudi Arabia are caused by building structures and air conditioning (AC). Energy consumption in government-constructed buildings constitutes a considerable ≈13% of the consumption of the total energy in Saudi Arabia. Therefore, the government of Saudi Arabia initiated the Saudi Energy Efficiency Program (SEEP) that goals to lower the domestic energy severity by roughly 30% by 2030. This paper introduces a study carried out in Eastern Province in Saudi Arabia to identify factors influencing the consumption of energy in school facilities (which are built of concrete in hot and humid climate zones), investigate the correlation between those factors and their impacts on the consumption of energy in school facilities, and finally, develop a prediction model for the energy consumption of school facilities. The study was based on the utilization of 352 real-world datasets of energy consumption of operating schools across Eastern Province in Saudi Arabia. The developed energy prediction model considers eleven identified factors that influence the consumption of energy of constructed schools. The identified factors were utilized as input variables to build the model. A systematic search among different neural network (NN) design architectures was conducted to identify the optimal network model. Validation of the developed model on eight real-world cases demonstrated that the accuracy of the developed model was about 87.5%. Moreover, the findings of this study indicate that the weakest correlation between the input variables was recorded as −0.015 between “type of school” and “AC capacity,” while the strongest correlation was recorded as 0.95 between the variables of “number of classrooms” and “total air-conditioned area (sqm),” followed by “total air-conditioned area (sqm)” and “number of students,” which was recorded as 0.90. It is worth noting that “AC capacity” was the most significant predictor, which increased exponentially for high values of energy consumption, followed by “total school roof area.” The study also found that the age of the schools had a very small impact on energy consumption, although the age of the schools varied from 11 to 51 years. This was probably due to a good maintenance system applied by the Ministry of Education. The implication of the developed prediction model was that the model can be used by the Ministry of Education to predict the energy consumption and its associated cost for public school buildings for the purpose of budget allocation. The model may be utilized as a stand-alone application, or it can be integrated with an existing building information module (BIM)-based system. Full article
(This article belongs to the Special Issue Big Data in Construction Engineering and Management)
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