Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.1
3.8
Journals
Buildings
Special Issues
Advanced Technologies for the Construction Industry in the Digital Era
share announcement

Advanced Technologies for the Construction Industry in the Digital Era

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 March 2023) | Viewed by 20550

Special Issue Editors

Dr. Ahsen Maqsoom

E-Mail Website
Guest Editor
Department of Civil Engineering, COMSATS University Islamabad, Wah Campus, Rawalpindi 47040, Pakistan
Interests: machine learning; GIS; sustainability; project management; disaster management; international construction
Dr. Fahim Ullah

E-Mail Website
Guest Editor
School of Surveying and Built Environment, University of Southern Queensland, Springfield Central, QLD 4300, Australia
Interests: construction management; digital construction; project management; digital built environment; smart cities
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The construction industry is complicated and faces new challenges every day due to the involvement of complex processes and associated resource management. Advanced technologies such as artificial intelligence (AI), machine learning (ML), computer vision, and geospatial and scanning technologies have shifted the way civil engineers and urban planners operate in the digital era. Technologies such as geospatial, remote sensing, and ML techniques have provided new ways for collecting and analyzing data that were not easily possible earlier. In addition to introducing much-needed disruption, such technologies provide missing innovation and uplift the otherwise traditional construction industry. Thus, these technologies have broadened the horizons for seeking solutions to many persistent construction problems to enable more successful projects that can be objectively measured. For example, the data analytics feature of disruptive technologies such as virtual reality (VR), augmented reality (AR), ML, and AI have improved project marketing, reduced costs, and enabled much smoother schedules. Similarly, advanced technologies have enabled assessing and responding to disasters and emergent challenges in the construction industry.

This Special Issue focuses on collecting high-quality articles on advanced technologies in the construction and urban domains. The target contributors include civil engineers, urban planners, construction and project managers, city management teams, architects, government officials, and others, in addition to academics and scientists. The Guest Editors cordially welcome high-quality papers focusing on, but not limited to, the following topics:

  • Building and construction innovation;
  • VR and AR for the construction industry in the digital era;
  • Building information modeling;
  • AI, ML, and natural language processing for the construction industry in the digital era;
  • Advanced techniques in innovative construction and building materials;
  • Technologies for smart city and integrated urban development;
  • Construction demolition and waste management technologies;
  • Remote sensing and GIS;
  • Renewable energy and sustainable construction;
  • Automated construction progress monitoring;
  • Other technologies for the construction industry in the digital era.

Dr. Ahsen Maqsoom
Dr. Fahim Ullah
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 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

  • construction innovation
  • advance technology
  • artificial intelligence
  • machine learning
  • virtual reality
  • geographic information system
  • sustainable construction

Published Papers (6 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

17 pages, 13899 KiB  
Article
Rationalization of Free-Form Architecture Using Generative and Parametric Designs
by Chankyu Lee, Sangyun Shin and Raja Raymond Issa
Buildings 2023, 13(5), 1250; https://doi.org/10.3390/buildings13051250 - 10 May 2023
Cited by 1 | Viewed by 2088
Abstract
Free-form architecture is a prominent trend in contemporary architecture where streamlined geometric buildings are constructed. The main problem in free-form architecture is rationalization, which involves realizing free-form surfaces at a reasonable cost while meeting design requirements. Balancing the design intents and construction costs [...] Read more.
Free-form architecture is a prominent trend in contemporary architecture where streamlined geometric buildings are constructed. The main problem in free-form architecture is rationalization, which involves realizing free-form surfaces at a reasonable cost while meeting design requirements. Balancing the design intents and construction costs simultaneously is essential for successful rationalization. This study proposes parametric and generative program flows to balance both requirements efficiently. The suggested parametric program flow, which is based on a mathematical algorithm, classifies a free-form surface into multiple areas, which are favorable to flat, single-curved, and double-curved panels. The proposed generative program flow optimizes the double-curved panels’ area through the integration of Multi-Objective Optimization and Pareto optimality. Designers can select the best design option that fits their design objectives through trade-offs using the results of the program flows. Eventually, more efficient and mindful decisions can be made in the early design process by using the results of this study for successful free-form architecture. Full article
(This article belongs to the Special Issue Advanced Technologies for the Construction Industry in the Digital Era)
►▼ Show Figures

Figure 1

21 pages, 1713 KiB  
Article
Ensuring Earthquake-Proof Development in a Swiftly Developing Region through Neural Network Modeling of Earthquakes Using Nonlinear Spatial Variables
by Mubeen ul Basharat, Junaid Ali Khan, Umer Khalil, Aqil Tariq, Bilal Aslam and Qingting Li
Buildings 2022, 12(10), 1713; https://doi.org/10.3390/buildings12101713 - 17 Oct 2022
Cited by 5 | Viewed by 1710
Abstract
Northern Pakistan, the center of major construction projects due to the commencement of the China Pakistan Economic Corridor, is among the most earthquake-prone regions globally owing to its tectonic settings. The area has experienced several devastating earthquakes in the past, and these earthquakes [...] Read more.
Northern Pakistan, the center of major construction projects due to the commencement of the China Pakistan Economic Corridor, is among the most earthquake-prone regions globally owing to its tectonic settings. The area has experienced several devastating earthquakes in the past, and these earthquakes pose a severe threat to infrastructure and life. Several researchers have previously utilized advanced tools such as Machine Learning (ML) and Deep Learning (DL) algorithms for earthquake predictions. This technological advancement helps with construction innovation, for instance, by designing earthquake-proof buildings. However, previous studies have focused mainly on temporal rather than spatial variables. The present study examines the impact of spatial variables to assess the performance of the different ML and DL algorithms for predicting the magnitude of short-term future earthquakes in North Pakistan. Two ML methods, namely Modular Neural Network (MNN) and Shallow Neural Network (SNN), and two DL methods, namely Recurrent Neural Network (RNN) and Deep Neural Network (DNN) algorithms, were used to meet the research objectives. The performance of the techniques was assessed using statistical measures, including accuracy, information gain analysis, sensitivity, specificity, and positive and negative predictive values. These metrics were used to evaluate the impact of including a new variable, Fault Density (FD), and the standard seismic variables in the predictions. The performance of the proposed models was examined for different patterns of variables and different classes of earthquakes. The accuracy of the models for the training data ranged from 73% to 89%, and the accuracy for the testing data ranged from 64% to 85%. The analysis outcomes demonstrated an improved performance when using an additional variable of FD for the earthquakes of low and high magnitudes, whereas the performance was less for moderate-magnitude earthquakes. DNN, and SNN models, performed relatively better than other models. The results provide valuable insights about the influence of the spatial variable. The outcome of the present study adds to the existing pool of knowledge about earthquake prediction, fostering a safer and more secure regional development plan involving innovative construction. Full article
(This article belongs to the Special Issue Advanced Technologies for the Construction Industry in the Digital Era)
►▼ Show Figures

Figure 1

26 pages, 6821 KiB  
Article
Resilient Capabilities to Tackle Supply Chain Risks: Managing Integration Complexities in Construction Projects
by Afia Malik, Khurram Iqbal Ahmad Khan, Siddra Qayyum, Fahim Ullah and Ahsen Maqsoom
Buildings 2022, 12(9), 1322; https://doi.org/10.3390/buildings12091322 - 29 Aug 2022
Cited by 3 | Viewed by 2620
Abstract
Due to the increased globalization and the disruptions caused by pandemics, supply chains (SCs) are becoming more complex in all industries. Such increased complexities of the SCs bring in more risks. The construction industry is no exception; its SC has been disrupted in [...] Read more.
Due to the increased globalization and the disruptions caused by pandemics, supply chains (SCs) are becoming more complex in all industries. Such increased complexities of the SCs bring in more risks. The construction industry is no exception; its SC has been disrupted in line with its industrial counterparts. Therefore, it is important to manage the complexities in integrating SC risks and resilient capabilities (RCs) to enable a resilient SC in construction. This study investigated the complexity involved in the dynamics of effects between organizations’ SC risks and RCs to overcome disruptive events. Past researchers investigated how to improve the performance of construction projects, regardless of the complexities and interdependencies associated with the risks across the entire SC. However, the system dynamics (SD) approach to describe the diversity of construction SCs under risks has received limited attention indicating a research gap pursued by this study. This work aimed to analyze and establish interconnectivity and functionality amongst the construction SC risks and RCs using systems thinking (ST) and SD modeling approach. The SD technique is used to assess the complexity and integrated effect of SC risks on construction projects to enhance their resilience. The risks and RCs were identified by critically scrutinizing the literature and were then ranked through content analysis. Questionnaire surveys and expert opinions (involving 10 experts) helped develop causal loop diagrams (CLDs) and SD models with simulations to assess complexity qualitatively and quantitatively within the system. Research reveals that construction organizations are more vulnerable to health pandemics, budget overruns, poor information coordination, insufficient management oversight, and error visibility to stakeholders. Further, the most effective RCs include assets visibility, collaborative information exchange, business intelligence gatherings, alternative suppliers, and inventory management systems. This research helps industry practitioners identify and plan for various risks and RCs within their organizations and SCs. Furthermore, it helps understand trade-offs between suitable RCs to abate essential risks and develop preparedness against disruptions to improve organizational policymaking, project efficiency, and performance. Full article
(This article belongs to the Special Issue Advanced Technologies for the Construction Industry in the Digital Era)
►▼ Show Figures

Figure 1

20 pages, 78430 KiB  
Article
Advanced-Technological UAVs-Based Enhanced Reconstruction of Edges for Building Models
by Luping Li, Jian Chen, Xing Su and Ahsan Nawaz
Buildings 2022, 12(8), 1248; https://doi.org/10.3390/buildings12081248 - 15 Aug 2022
Cited by 5 | Viewed by 1652
Abstract
Accurate building models are widely used in the construction industry in the digital era. UAV cameras combined with image-based reconstruction provide a low-cost technology for building modeling. Most existing reconstruction methods operate on point clouds, while massive points reduce computational efficiency, and the [...] Read more.
Accurate building models are widely used in the construction industry in the digital era. UAV cameras combined with image-based reconstruction provide a low-cost technology for building modeling. Most existing reconstruction methods operate on point clouds, while massive points reduce computational efficiency, and the accumulated error of point position often distorts building edges. This paper introduces an innovative 3D reconstruction method, Edge3D, that recovers building edges in the form of 3D lines. It employs geometry constraints and progressive screening technology to improve the robustness and precision of line segment matching. An innovative bundle adjustment strategy based on endpoints is designed to reduce the global reprojection error. Edges were tested on challenging real-world image sets, and matching precisions of 96% and 94% were achieved on the two image sets, respectively, with good reconstruction results. The proposed approach reconstructs building edges using a small number of lines instead of massive points, which contributes to the rapid reconstruction of building contour construction and obtaining accurate models, serving as an important foundation for the promotion of construction advancement. Full article
(This article belongs to the Special Issue Advanced Technologies for the Construction Industry in the Digital Era)
►▼ Show Figures

Figure 1

Review

Jump to: Research

25 pages, 4127 KiB  
Review
Design for Manufacture and Assembly of Digital Fabrication and Additive Manufacturing in Construction: A Review
by Wiput Tuvayanond and Lapyote Prasittisopin
Buildings 2023, 13(2), 429; https://doi.org/10.3390/buildings13020429 - 03 Feb 2023
Cited by 23 | Viewed by 5390
Abstract
Design for manufacture and assembly (DfMA) in the architectural, engineering, and construction (AEC) industry is attracting the attention of designers, practitioners, and construction project stakeholders. Digital fabrication (Dfab) and design for additive manufacturing (DfAM) practices are found in current need of further research [...] Read more.
Design for manufacture and assembly (DfMA) in the architectural, engineering, and construction (AEC) industry is attracting the attention of designers, practitioners, and construction project stakeholders. Digital fabrication (Dfab) and design for additive manufacturing (DfAM) practices are found in current need of further research and development. The DfMA’s conceptual function is to maximize the process efficiency of Dfab and AM building projects. This work reviewed 171 relevant research articles over the past few decades. The concepts and the fundamentals of DfMA in building and construction were explored. In addition, DfMA procedures for Dfab, DfAM, and AM assembly processes were discussed. Lastly, the current machine learning research on DfMA in construction was also highlighted. As Dfab and DFAM are innovated, practical DFMA techniques begin to develop to a great extent. Large research gaps in the DfMA for Dfab and DfAM can be filled in terms of integrating them with product structural performance, management, studied cases, building information modeling (BIM), and machine learning to increase operational efficiency and sustainable practices. Full article
(This article belongs to the Special Issue Advanced Technologies for the Construction Industry in the Digital Era)
►▼ Show Figures

Figure 1

22 pages, 2285 KiB  
Review
The Drivers, Barriers, and Enablers of Building Information Modeling (BIM) Innovation in Developing Countries: Insights from Systematic Literature Review and Comparative Analysis
by Bernardus Ariono, Meditya Wasesa and Wawan Dhewanto
Buildings 2022, 12(11), 1912; https://doi.org/10.3390/buildings12111912 - 07 Nov 2022
Cited by 10 | Viewed by 5561
Abstract
Building information modeling (BIM) has received significant attention in the last two decades from the architecture, engineering, and construction (AEC) industry. Despite the popular trend of BIM in developing countries, the adoption of this technology is still low. This paper aims to investigate [...] Read more.
Building information modeling (BIM) has received significant attention in the last two decades from the architecture, engineering, and construction (AEC) industry. Despite the popular trend of BIM in developing countries, the adoption of this technology is still low. This paper aims to investigate the drivers, barriers, and enablers of BIM adoption in developing countries with regard to global challenges. A systematic literature review and an in-depth comparative qualitative analysis were conducted to accomplish the objective. Relevant articles from three major databases covering 20 years (2002–2022) of journal article publications were analyzed. The comparative study identified drivers, barriers, and enablers influencing BIM innovation in six developing countries from three different continents. Additionally, a critical review and analysis explored the importance of BIM’s innovation factors in developing countries. The stakeholders of AEC will benefit from insights offered by this study to prepare BIM implementation strategies effectively. Full article
(This article belongs to the Special Issue Advanced Technologies for the Construction Industry in the Digital Era)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop