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Data Analytics and Artificial Intelligence for Sustainable Construction Engineering and Built Environment

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Green Building".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 3816

Special Issue Editor

Dr. Reza Akhavian

E-Mail Website
Guest Editor
Department of Civil, Construction, and Environmental Engineering, San Diego State University, San Diego, CA 92182, USA
Interests: construction robotics; artificial intelligence (AI); Internet of Things (IoT); data analytics; machine learning; cyber-physical systems; building information modeling (BIM); construction automation; digital transformation; digital twins
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Engineering and management of construction projects and the built environment are experiencing a data and technology revolution. Considering the aging infrastructure and rapid urbanization trends, the need to promote sustainable practices in design, construction, and operation is more compelling than ever. The social, economic, and environmental impacts of construction projects and the future built environment around the world have multiple technological and social dimensions. A massive amount of construction project and building performance data are produced daily that can be leveraged to enhance sustainability efforts. State-of-the-art in artificial intelligence (AI), enabled by automation, robotics, the Internet of Things (IoT), and building information modelling (BIM), has created an unprecedented opportunity to optimize construction processes and performance of the built environment entities.   

This Special Issue invites original papers dealing with theoretical and practical aspects of leveraging data analytics and AI to enhance sustainable practices in construction projects and the built environment. Invited papers may cover topics including but not limited to:

  • Sustainable and resilient construction enabled by data analytics and artificial indigence (AI)
  • Data-informed building energy, emissions, cost, and comfort analysis
  • Multi-scale simulation, modelling, and computing for the built environment
  • Cyber-physical systems (CPS) in construction and the built environment
  • Data analytics processes and platforms for smart cities
  • Data-driven decision and policy making for sustainable urbanization
  • AI-enabled sustainable construction automation

Dr. Reza Akhavian
Guest Editor

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. Sustainability 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.

Keywords

  • construction automation
  • data analytics
  • artificial intelligence (AI)
  • sustainable and resilient built environment

Published Papers (1 paper)

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Research

22 pages, 2896 KiB  
Article
Automated Estimation of Construction Equipment Emission Using Inertial Sensors and Machine Learning Models
by Farid Shahnavaz and Reza Akhavian
Sustainability 2022, 14(5), 2750; https://0-doi-org.brum.beds.ac.uk/10.3390/su14052750 - 26 Feb 2022
Cited by 7 | Viewed by 3000
Abstract
The construction industry is one of the main producers of greenhouse gasses (GHG). With the looming consequences of climate change, sustainability measures including quantifying the amount of air pollution during a construction project have become an important project objective in the construction industry. [...] Read more.
The construction industry is one of the main producers of greenhouse gasses (GHG). With the looming consequences of climate change, sustainability measures including quantifying the amount of air pollution during a construction project have become an important project objective in the construction industry. A major contributor to air pollution during construction projects is the use of heavy equipment. Therefore, efficient operation and management can substantially reduce a project’s carbon footprint and other environmental harms. Using unintrusive and indirect methods to predict on-road vehicle emissions has been a widely researched topic. Nevertheless, the same is not true in the case of construction equipment. This paper describes the development and deployment of a framework that uses machine learning (ML) methods to predict the level of emissions from heavy construction equipment. Data is collected via an Internet of Things (IoT) approach with accelerometer and gyroscope sensors as data collection nodes. The developed framework was validated using an excavator performing real-world construction work. A portable emission measurement system (PEMS) was used along with the inertial sensors to record the amount of CO, NOX, CO2, SO2, and CH4 pollution emitted by the equipment. Different ML algorithms were developed and compared to identify the best model to predict emission levels from inertial sensors data. The results show that Random Forest with the coefficient of determination (R2) of 0.94, 0.91, and 0.94, and normalized root-mean-square error (NRMSE) of 4.25, 6.42, and 5.17 for CO, NOX, and CO2, respectively, was the best algorithm among different models evaluated in this study. Full article
(This article belongs to the Special Issue Data Analytics and Artificial Intelligence for Sustainable Construction Engineering and Built Environment)
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