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Data Sensing and Analysis in Design, Construction, Operation, Monitoring, and...
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Data Sensing and Analysis in Design, Construction, Operation, Monitoring, and Maintenance of Built Environments

A special issue of Data (ISSN 2306-5729).

Deadline for manuscript submissions: closed (30 April 2020) | Viewed by 53516

Special Issue Editors

Dr. Changbum R. Ahn

E-Mail Website
Guest Editor
Department of Construction Science, College of Architecture, 330B Francis Hall, 3137 TAMU, College Station, TX, USA
Special Issues, Collections and Topics in MDPI journals
Dr. Eric Jing Du

E-Mail Website
Guest Editor
Department of Civil and Coastal Engineering, The Herbert Wertheim College of Engineering, University of Florida, 1949 Stadium Road, Gainesville, FL 32611, USA
Interests: virtual reality/augmented reality (AR/VR) in construction engineering; building information modeling; information overload in construction operations
Special Issues, Collections and Topics in MDPI journals
Dr. Youngjib Ham

E-Mail Website
Guest Editor
Department of Construction Science, Texas A&M University, College Station, TX 77843, USA
Interests: smart building; human-building interaction; thermographic & physiological sensing and analytics; building information modeling (BIM); smart construction; automation in construction; digital twins in construction; smart city

Special Issue Information

Dear Colleagues,

The advancements in technologies, including multimodal visual sensors, wireless infrastructure sensors, wearable sensors, unmanned aerial systems (UAS), and virtual reality/augmented reality (AR/VR), have changed the way we tackling complex problems in civil engineering. High-resolution data collected from buildings, roads, bridges, public spaces, and other infrastructure have become increasingly available and have brought new discoveries and challenges in design, construction, maintenance, and operation of buildings and infrastructure. Making such data transparent and accessible is now critical in supporting reproducible research and generating new research opportunities for other researchers. However, the efforts to publish data in a way that enables reproducibility and reuse are limited in civil engineering and valuable data are often buried in supplementary materials.

This Special Issue thus aims to publish articles describing data collection, acquisition, (re)processing, and management in civil engineering, so that future use of the data can be assured. Potential datasets include, but are not limited to, data and methods on:

  • Built environment monitoring, control, and analysis
  • Project design, construction, planning, and management
  • Asset and facility management, operation, and maintenance
  • Data acquisition (handhelds, laser scanning, photo/videogrammetry, unmanned aerial systems, etc.) in built environment monitoring
  • Visualization (nD, VR, AR)
  • Construction automation/smart construction
  • Smart safety and health in construction
  • Internet of Things and big data analytics in construction
  • Sustainable construction and design
  • 3D geographic information systems
  • 3D printing (contour crafting)

Dr. Changbum R. Ahn
Assoc. Prof. Eric Jing Du
Dr. Youngjib Ham
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. We encourage that authors send a title and short abstract (about 100 words) of the planned paper to the Guest Editors and the Editorial Office for preliminary check.

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. Data is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) is waived for well-prepared manuscripts submitted to this issue. 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

  • Built environment monitoring, control, and analysis
  • Project design, construction, planning, and management
  • Asset and facility management, operation, and maintenance
  • Data acquisition (handhelds, laser scanning, photo/videogrammetry, unmanned aerial systems, etc.) in built environment monitoring
  • Visualization (nD, VR, AR)
  • Construction automation / Smart construction
  • Smart safety and health in construction
  • Internet of Things and big data analytics in construction
  • Sustainable construction and design
  • 3D geographic information systems
  • 3D printing (contour crafting)

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (7 papers)

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Research

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18 pages, 501 KiB  
Article
Trend Analysis on Adoption of Virtual and Augmented Reality in the Architecture, Engineering, and Construction Industry
by Mojtaba Noghabaei, Arsalan Heydarian, Vahid Balali and Kevin Han
Data 2020, 5(1), 26; https://doi.org/10.3390/data5010026 - 13 Mar 2020
Cited by 143 | Viewed by 22936
Abstract
With advances in Building Information Modeling (BIM), Virtual Reality (VR) and Augmented Reality (AR) technologies have many potential applications in the Architecture, Engineering, and Construction (AEC) industry. However, the AEC industry, relative to other industries, has been slow in adopting AR/VR technologies, partly [...] Read more.
With advances in Building Information Modeling (BIM), Virtual Reality (VR) and Augmented Reality (AR) technologies have many potential applications in the Architecture, Engineering, and Construction (AEC) industry. However, the AEC industry, relative to other industries, has been slow in adopting AR/VR technologies, partly due to lack of feasibility studies examining the actual cost of implementation versus an increase in profit. The main objectives of this paper are to understand the industry trends in adopting AR/VR technologies and identifying gaps within the industry. The identified gaps can lead to opportunities for developing new tools and finding new use cases. To achieve these goals, two rounds of a survey at two different time periods (a year apart) were conducted. Responses from 158 industry experts and researchers were analyzed to assess the current state, growth, and saving opportunities for AR/VR technologies for the AEC industry. The findings demonstrate that older generations are significantly more confident about the future of AR/VR technologies and they see more benefits in AR/VR utilization. Furthermore, the research results indicate that Residential and commercial sectors have adopted these tools the most, compared to other sectors and institutional and transportation sectors had the highest growth from 2017 to 2018. Industry experts anticipated a solid growth in the use of AR/VR technologies in 5 to 10 years, with the highest expectations towards healthcare. Ultimately, the findings show a significant increase in AR/VR utilization in the AEC industry from 2017 to 2018. Full article
(This article belongs to the Special Issue Data Sensing and Analysis in Design, Construction, Operation, Monitoring, and Maintenance of Built Environments)
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12 pages, 7193 KiB  
Article
Evaluation of Photogrammetry and Inclusion of Control Points: Significance for Infrastructure Monitoring
by Renee C. Oats, Rudiger Escobar-Wolf and Thomas Oommen
Data 2019, 4(1), 42; https://doi.org/10.3390/data4010042 - 14 Mar 2019
Cited by 7 | Viewed by 4246
Abstract
Structure from Motion (SfM)/Photogrammetry is a powerful mapping tool in extracting three-dimensional (3D) models from photographs. This method has been applied to a range of applications, including monitoring of infrastructure systems. This technique could potentially become a substitute, or at least a complement, [...] Read more.
Structure from Motion (SfM)/Photogrammetry is a powerful mapping tool in extracting three-dimensional (3D) models from photographs. This method has been applied to a range of applications, including monitoring of infrastructure systems. This technique could potentially become a substitute, or at least a complement, for costlier approaches such as laser scanning for infrastructure monitoring. This study expands on previous investigations, which utilize photogrammetry point cloud data to measure failure mode behavior of a retaining wall model, emphasizing further robust spatial testing. In this study, a comparison of two commonly used photogrammetry software packages was implemented to assess the computing performance of the method and the significance of control points in this approach. The impact of control point selection, as part of the photogrammetric modeling processes, was also evaluated. Comparisons between the two software tools reveal similar performances in capturing quantitative changes of a retaining wall structure. Results also demonstrate that increasing the number of control points above a certain number does not, necessarily, increase 3D modeling accuracies, but, in some cases, their spatial distribution can be more critical. Furthermore, errors in model reproducibility, when compared with total station measurements, were found to be spatially correlated with the arrangement of control points. Full article
(This article belongs to the Special Issue Data Sensing and Analysis in Design, Construction, Operation, Monitoring, and Maintenance of Built Environments)
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17 pages, 2719 KiB  
Article
LNSNet: Lightweight Navigable Space Segmentation for Autonomous Robots on Construction Sites
by Khashayar Asadi, Pengyu Chen, Kevin Han, Tianfu Wu and Edgar Lobaton
Data 2019, 4(1), 40; https://doi.org/10.3390/data4010040 - 13 Mar 2019
Cited by 8 | Viewed by 4934
Abstract
An autonomous robot that can monitor a construction site should be able to be can contextually detect its surrounding environment by recognizing objects and making decisions based on its observation. Pixel-wise semantic segmentation in real-time is vital to building an autonomous and mobile [...] Read more.
An autonomous robot that can monitor a construction site should be able to be can contextually detect its surrounding environment by recognizing objects and making decisions based on its observation. Pixel-wise semantic segmentation in real-time is vital to building an autonomous and mobile robot. However, the learning models’ size and high memory usage associated with real-time segmentation are the main challenges for mobile robotics systems that have limited computing resources. To overcome these challenges, this paper presents an efficient semantic segmentation method named LNSNet (lightweight navigable space segmentation network) that can run on embedded platforms to determine navigable space in real-time. The core of model architecture is a new block based on separable convolution which compresses the parameters of present residual block meanwhile maintaining the accuracy and performance. LNSNet is faster, has fewer parameters and less model size, while provides similar accuracy compared to existing models. A new pixel-level annotated dataset for real-time and mobile navigable space segmentation in construction environments has been constructed for the proposed method. The results demonstrate the effectiveness and efficiency that are necessary for the future development of the autonomous robotics systems. Full article
(This article belongs to the Special Issue Data Sensing and Analysis in Design, Construction, Operation, Monitoring, and Maintenance of Built Environments)
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Other

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18 pages, 15131 KiB  
Data Descriptor
Changes in the Building Stock of Da Nang between 2015 and 2017
by Andreas Braun, Gebhard Warth, Felix Bachofer, Tram Thi Quynh Bui, Hao Tran and Volker Hochschild
Data 2020, 5(2), 42; https://doi.org/10.3390/data5020042 - 23 Apr 2020
Cited by 3 | Viewed by 3993
Abstract
This descriptor introduces a novel dataset, which contains the number and types of buildings in the city of Da Nang in Central Vietnam. The buildings were classified into nine distinct types and initially extracted from a satellite image of the year 2015. Secondly, [...] Read more.
This descriptor introduces a novel dataset, which contains the number and types of buildings in the city of Da Nang in Central Vietnam. The buildings were classified into nine distinct types and initially extracted from a satellite image of the year 2015. Secondly, changes were identified based on a visual interpretation of an image of the year 2017, so that new buildings, demolished buildings and building upgrades can be quantitatively analyzed. The data was aggregated by administrative wards and a hexagonal grid with a diameter of 250 m to protect personal rights and to avoid the misuse of a single building’s information. The dataset shows an increase of 19,391 buildings between October 2015 and August 2017, with a variety of interesting spatial patterns. The center of the city is mostly dominated by building changes and upgrades, while most of the new buildings were constructed within a distance of five to six kilometers from the city center. Full article
(This article belongs to the Special Issue Data Sensing and Analysis in Design, Construction, Operation, Monitoring, and Maintenance of Built Environments)
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14 pages, 1165 KiB  
Data Descriptor
Residential Power Traces for Five Houses: The iHomeLab RAPT Dataset
by Patrick Huber, Melvin Ott, Martin Friedli, Andreas Rumsch and Andrew Paice
Data 2020, 5(1), 17; https://doi.org/10.3390/data5010017 - 5 Feb 2020
Cited by 13 | Viewed by 4193
Abstract
Datasets with measurements of both solar electricity production and domestic electricity consumption separated into the major loads are interesting for research focussing on (i) local optimization of solar energy consumption and (ii) non-intrusive load monitoring. To this end, we publish the iHomeLab RAPT [...] Read more.
Datasets with measurements of both solar electricity production and domestic electricity consumption separated into the major loads are interesting for research focussing on (i) local optimization of solar energy consumption and (ii) non-intrusive load monitoring. To this end, we publish the iHomeLab RAPT dataset consisting of electrical power traces from five houses in the greater Lucerne region in Switzerland spanning a period from 1.5 up to 3.5 years with a sampling frequency of five minutes. For each house, the electrical energy consumption of the aggregated household and specific appliances such as dishwasher, washing machine, tumble dryer, hot water boiler, or heating pump were metered. Additionally, the data includes electric production data from PV panels for all five houses, and battery power flow measurement data from two houses. Thermal metadata is also provided for the three houses with a heating pump. Full article
(This article belongs to the Special Issue Data Sensing and Analysis in Design, Construction, Operation, Monitoring, and Maintenance of Built Environments)
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9 pages, 625 KiB  
Data Descriptor
Building Stock and Building Typology of Kigali, Rwanda
by Felix Bachofer, Andreas Braun, Florian Adamietz, Sally Murray, Pablo d’Angelo, Edward Kyazze, Abias Philippe Mumuhire and Jonathan Bower
Data 2019, 4(3), 105; https://doi.org/10.3390/data4030105 - 21 Jul 2019
Cited by 15 | Viewed by 6405
Abstract
This study uses very high-resolution Pléiades imagery for the densely built-up central part of the City of Kigali for the year 2015 in order to derive urban morphology data on building footprints, building archetypes and building heights. Aerial images of the study area [...] Read more.
This study uses very high-resolution Pléiades imagery for the densely built-up central part of the City of Kigali for the year 2015 in order to derive urban morphology data on building footprints, building archetypes and building heights. Aerial images of the study area from 2008–2009 were used in combination with the 2015 dataset to create a change monitoring dataset on a single building basis. A semi-automated approach was chosen which combined an object-based image analysis with an expert-based revision. The result is a geospatial dataset that detects 165,625 buildings for 2008–2009 and 211,458 for 2015. The dataset includes information on the type of changes between the two dates. Analysis of this geospatial dataset can be used for a range of research applications in economics and the social sciences, as well as a range of policy applications in urban planning and municipal finance administration. Full article
(This article belongs to the Special Issue Data Sensing and Analysis in Design, Construction, Operation, Monitoring, and Maintenance of Built Environments)
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17 pages, 2311 KiB  
Data Descriptor
Climate Data to Undertake Hygrothermal and Whole Building Simulations Under Projected Climate Change Influences for 11 Canadian Cities
by Abhishek Gaur, Michael Lacasse and Marianne Armstrong
Data 2019, 4(2), 72; https://doi.org/10.3390/data4020072 - 21 May 2019
Cited by 66 | Viewed by 5791
Abstract
Buildings and homes in Canada will be exposed to unprecedented climatic conditions in the future as a consequence of global climate change. To improve the climate resiliency of existing and new buildings, it is important to evaluate their performance over current and projected [...] Read more.
Buildings and homes in Canada will be exposed to unprecedented climatic conditions in the future as a consequence of global climate change. To improve the climate resiliency of existing and new buildings, it is important to evaluate their performance over current and projected future climates. Hygrothermal and whole building simulation models, which are important tools for assessing performance, require continuous climate records at high temporal frequencies of a wide range of climate variables for input into the kinds of models that relate to solar radiation, cloud-cover, wind, humidity, rainfall, temperature, and snow-cover. In this study, climate data that can be used to assess the performance of building envelopes under current and projected future climates, concurrent with 2 °C and 3.5 °C increases in global temperatures, are generated for 11 major Canadian cities. The datasets capture the internal variability of the climate as they are comprised of 15 realizations of the future climate generated by dynamically downscaling future projections from the CanESM2 global climate model and thereafter bias-corrected with reference to observations. An assessment of the bias-corrected projections suggests, as a consequence of global warming, future increases in the temperatures and precipitation, and decreases in the snow-cover and wind-speed for all cities. Full article
(This article belongs to the Special Issue Data Sensing and Analysis in Design, Construction, Operation, Monitoring, and Maintenance of Built Environments)
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