Towards a Digital Built Environment: The Role of Digital Engineering, IoT and Artificial Intelligence

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

Deadline for manuscript submissions: closed (10 January 2022) | Viewed by 12703

Special Issue Editors

Special Issue Information

Dear colleagues,

The construction industry is increasingly being affected by various dimensions of the Fourth Industrial Revolution, commonly known as Industry 4.0. In the construction context, these effects are synonymous with the creation of cyberphysical systems through the integration of several major advanced methodologies, including digital engineering (DE), the Internet of Things (IoT), and artificial intelligence (AI). At the present time, many construction companies have transformed or are transforming themselves in order to adopt and implement these methodologies, responding to changes in customers' demands and the need to deliver high-quality services and products. However, the roles that these technological innovations play in disrupting the status quo in the construction context have hitherto received scant academic attention. Studies on the integration of DE, IoT, and AI are scarce. This proposed and pioneering Special Issue is the first to provide a platform for construction researchers who seek to showcase the emergent findings from their research not only on the roles of DE, IoT, and AI, but also on their integration, in relation to disruption in the construction industry’s ways of working. The Special Issue particularly invites papers that provide accounts of real-life DE, IoT, and AI case studies. Other themes sought for the Special Issue include exploring the emerging methodologies of DE, IoT, and AI integration, and determining how DE, IoT, and AI can benefit the construction industry.

Dr. M. Reza Hosseini
Prof. Albert P. C. Chan
Dr. Amos Darko
Guest Editors

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Keywords

  • built environment
  • construction industry
  • Industry 4.0
  • digitalization
  • building information modeling
  • innovation diffusion
  • digital engineering
  • IoT
  • Artificial Intelligence

Published Papers (2 papers)

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Research

21 pages, 4743 KiB  
Article
Design and Application of a Smart Lighting System Based on Distributed Wireless Sensor Networks
by Yusi Cheng, Chen Fang, Jingfeng Yuan and Lei Zhu
Appl. Sci. 2020, 10(23), 8545; https://0-doi-org.brum.beds.ac.uk/10.3390/app10238545 - 29 Nov 2020
Cited by 31 | Viewed by 8155
Abstract
Buildings have been an important energy consuming sector, and inefficient controlling of lights can result in wastage of energy in buildings. The aim of the study is to reduce energy consumption by implementing a smart lighting system that integrates sensor technologies, a distributed [...] Read more.
Buildings have been an important energy consuming sector, and inefficient controlling of lights can result in wastage of energy in buildings. The aim of the study is to reduce energy consumption by implementing a smart lighting system that integrates sensor technologies, a distributed wireless sensor network (WSN) using ZigBee protocol, and illumination control rules. A sensing module consists of occupancy sensors, including passive infrared (PIR) sensors and microwave Doppler sensors, an ambient light sensor, and lighting control rules. The dimming level of each luminaire is controlled by rules taking into consideration occupancy and daylight harvesting. The performance of the proposed system is evaluated in two scenarios, a metro station and an office room, and the average energy savings are about 45% and 36%, respectively. The effects of different factors on energy savings are analyzed, including people flow density, weather, desired illuminance, and the number of people in a room. Experimental results demonstrate the robustness of the proposed system and its ability to save energy consumption. The study can benefit the development of intelligent and sustainable buildings. Full article
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24 pages, 4037 KiB  
Article
An Artificial Neural Network Approach to Predicting Most Applicable Post-Contract Cost Controlling Techniques in Construction Projects
by Temitope Omotayo, Awuzie Bankole and Ayokunle Olubunmi Olanipekun
Appl. Sci. 2020, 10(15), 5171; https://0-doi-org.brum.beds.ac.uk/10.3390/app10155171 - 28 Jul 2020
Cited by 17 | Viewed by 3676
Abstract
The post-contract phase of the construction process remains critical to cost management. Several techniques have been used to facilitate effective cost management in this phase. However, the deployment of these techniques has not caused a reduction in the incidence of cost overruns hence [...] Read more.
The post-contract phase of the construction process remains critical to cost management. Several techniques have been used to facilitate effective cost management in this phase. However, the deployment of these techniques has not caused a reduction in the incidence of cost overruns hence casting doubts on their utility. The seeming underwhelming performance posted by these post-contract cost control techniques (PCCTs), has been traced to improper deployment by construction project managers (CPM) and quantity surveyors (QS). Utilizing the perspectives of CPM and QS professionals, as elicited through a survey, produced 135 samples. The instrumentality of the artificial neural networks (ANN) in this study enabled the development of a structured decision-support methodology for analysing the most appropriate PCCTs to be deployed to different construction process phases. Besides showcasing the utility of the emergent ANN-based decision support methodology, the study’s theoretical findings indicate that CPM and QS professionals influence decisions pertaining to PCCTs choice in distinct phases of the construction process. Whereas QS professionals were particularly responsible for the choice of PCCTs during the initial and mid-level phases, CPM professionals assumed responsibility for PCCTs selection during the construction process close-out phase. In construction cost management practice, the crucial PCCTs identifies more with the application of historical data and all cost monitoring approaches. Full article
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