Submit to Sustainability Review for Sustainability Propose a Special Issue

Journal Browser

► Journal Browser

Sustainable Disruptive Technologies in the Built Environment: A Step towards Industry 5.0

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (15 October 2022) | Viewed by 29129

Share This Special Issue

Special Issue Editor

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 built environment has been disrupted through innovative technologies in line with Industry 4.0 requirements laying the foundations of the smart built environment. However, with the recent introduction of Industry 5.0, additional steps are required to transform the smart built environment further. The focus of Industry 5.0 is to complement the existing "Industry 4.0" approach by specifically putting research and innovation at the service of the transition to sustainable, human-centric, and resilient industries. Accordingly, the built environment must transform and shift its focus from stakeholder satisfaction to stakeholder value and reinforce industry contribution to society and the wider built environment. Owing to this, the current Special Issue aims to attract high-quality articles focused on such Industry 5.0 technologies from a sustainability perspective. The technologies in focus include digital twins, green technologies, artificial intelligence (AI), internet of things (IoT), unmanned aerial vehicles (UAVs), fog computing, sensors technologies, building information modeling (BIM), infrastructure information modeling (IIM), big data, 3D scanning, wearable technologies, virtual reality (VR), augmented reality (AR), robotics, blockchains, cloud computing, software as a service (SaaS), 3D printing, ubiquitous computing, renewable energy, autonomous vehicles, and 5G communications. The target contributors for this Special Issue include city governance teams, construction managers, civil engineers, project managers, city and urban planners, real estate and property managers, architects, IT managers, data scientists, software developers, computer systems analysts, web developers, governance management specialists, and others.

Dr. Fahim Ullah
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

sustainable Industry 5.0 technologies
sustainable green technologies in the built environment
sustainable digital twins in the built environment
sustainable and green Internet of Things in the built environment
smart, sustainable, and green buildings in the built environment
smart city technologies
smart and sustainable approaches to real estate and property management
smart and sustainable construction
smart and sustainable architecture
smart and sustainable environments
unmanned aerial vehicles for sustainability in the built environment
BIM for sustainability in the built environment
IIM for sustainability in the built environment
sustainable geographic information systems in the built environment
artificial intelligence for sustainability in the built environment
big data for sustainability in the built environment
sustainable 3D scanning and printing in the built environment
sustainable wearable technologies and gadgets in the built environment
virtual and augmented reality for sustainability in the built environment
robotics and automation for sustainability in the built environment
blockchains for sustainability in the built environment
clouds, SaaS, ubiquitous and fog computing for sustainability in the built environment
sustainable renewable energy systems in the built environment
sustainable autonomous vehicles in the built environment
sustainable 5G communications and networking in the built environment

Published Papers (9 papers)

Download All Papers

Research

35 pages, 8094 KiB

Open AccessArticle

Smart and Automated Infrastructure Management: A Deep Learning Approach for Crack Detection in Bridge Images

by Hina Inam, Naeem Ul Islam, Muhammad Usman Akram and Fahim Ullah

Sustainability 2023, 15(3), 1866; https://0-doi-org.brum.beds.ac.uk/10.3390/su15031866 - 18 Jan 2023

Cited by 14 | Viewed by 2992

Abstract

Artificial Intelligence (AI) and allied disruptive technologies have revolutionized the scientific world. However, civil engineering, in general, and infrastructure management, in particular, are lagging behind the technology adoption curves. Crack identification and assessment are important indicators to assess and evaluate the structural health of critical city infrastructures such as bridges. Historically, such critical infrastructure has been monitored through manual visual inspection. This process is costly, time-consuming, and prone to errors as it relies on the inspector’s knowledge and the gadgets’ precision. To save time and cost, automatic crack and damage detection in bridges and similar infrastructure is required to ensure its efficacy and reliability. However, an automated and reliable system does not exist, particularly in developing countries, presenting a gap targeted in this study. Accordingly, we proposed a two-phased deep learning-based framework for smart infrastructure management to assess the conditions of bridges in developing countries. In the first part of the study, we detected cracks in bridges using the dataset from Pakistan and the online-accessible SDNET2018 dataset. You only look once version 5 (YOLOv5) has been used to locate and classify cracks in the dataset images. To determine the main indicators (precision, recall, and mAP (0.5)), we applied each of the YOLOv5 s, m, and l models to the dataset using a ratio of 7:2:1 for training, validation, and testing, respectively. The mAP (Mean average precision) values of all the models were compared to evaluate their performance. The results show mAP values for the test set of the YOLOv5 s, m, and l as 97.8%, 99.3%, and 99.1%, respectively, indicating the superior performance of the YOLOv5 m model compared to the two counterparts. In the second portion of the study, segmentation of the crack is carried out using the U-Net model to acquire their exact pixels. Using the segmentation mask allocated to the attribute extractor, the pixel’s width, height, and area are measured and visualized on scatter plots and Boxplots to segregate different cracks. Furthermore, the segmentation part validated the output of the proposed YOLOv5 models. This study not only located and classified the cracks based on their severity level, but also segmented the crack pixels and measured their width, height, and area per pixel under different lighting conditions. It is one of the few studies targeting low-cost health assessment and damage detection in bridges of developing countries that otherwise struggle with regular maintenance and rehabilitation of such critical infrastructure. The proposed model can be used by local infrastructure monitoring and rehabilitation authorities for regular condition and health assessment of the bridges and similar infrastructure to move towards a smarter and automated damage assessment system. Full article

(This article belongs to the Special Issue Sustainable Disruptive Technologies in the Built Environment: A Step towards Industry 5.0)

► Show Figures

Figure 1

26 pages, 11559 KiB

Open AccessArticle

Identification of Structural Damage and Damping Performance of a Mega-Subcontrolled Structural System (MSCSS) Subjected to Seismic Action

by Muhammad Moman Shahzad, Xun’an Zhang and Xinwei Wang

Sustainability 2022, 14(19), 12390; https://0-doi-org.brum.beds.ac.uk/10.3390/su141912390 - 29 Sep 2022

Viewed by 1066

Abstract

Due to multiple degrees of freedom, evaluating high-rise buildings’ seismic safety under unpredictable seismic excitations is difficult. To address the issue that the damage mechanism of a mega-subcontrolled structural system (MSCSS) has not yet been studied, this paper employs ABAQUS software with strong nonlinear analysis capabilities to analyze the nonlinear elastic—plastic time history of an MSCSS, analyze structural damage to the MSCSS structure, reveal the internal energy dissipation mechanism of the MSCSS, and evaluate the damping performance of the MSCSS structure. This work presents a novel and optimized MSCSS structure equipped with SPSW that improves the system’s seismic performance. First, a refined finite element model of the MSCSS is established, and the impact of vigorous seismic excitations on the damage to the MSCSS structure is considered. The MSCSS structure’s vulnerable parts are then summarized using stress nephograms and residual stresses. Finally, the favorable damping performance of the structure reveals that the newly proposed structure has good shock absorption performance based on an analysis of the energy dissipation, time history, and interstory drift of the MSCSS. This paper’s research findings elaborate the structural damage trend in MSCSS structures, which can serve as a theoretical foundation for MSCSS structure damage identification. Full article

(This article belongs to the Special Issue Sustainable Disruptive Technologies in the Built Environment: A Step towards Industry 5.0)

► Show Figures

Figure 1

20 pages, 664 KiB

Open AccessArticle

Nexus between Leader–Member Exchange, Paternalistic Leadership, and Creative Behavior in the Construction Industry

by Ahsen Maqsoom, Ifra Zahoor, Hassan Ashraf, Fahim Ullah, Badr T. Alsulami, Alaa Salman and Muwaffaq Alqurashi

Sustainability 2022, 14(12), 7211; https://0-doi-org.brum.beds.ac.uk/10.3390/su14127211 - 13 Jun 2022

Cited by 10 | Viewed by 2776

Abstract

Effective leadership and creative performance are the predominant factors for the success of modern projects in the global construction industry. However, rigorous research has not explored the nexus between such factors and the leader–member exchange (LMX). To address this gap, this study explores the relationship between dimensions of paternalistic leadership and employee creativity achieved through LMX in the context of the construction industry. Based on social exchange theory (SET), six relevant hypotheses were proposed in this study. The data were collected through a structured questionnaire. An online survey form was used for data collection, through which 288 responses were collected from the construction industry employees working in Pakistan. The collected data were analyzed using Smart PLS in two stages, i.e., measurement model evaluation (reliability analysis, convergent and discriminant validity) and structural model evaluation (R², F², and path coefficient). The findings of the current study reveal a positive association of authoritarian, benevolent, and moral leadership with employee creativity. In addition, LMX significantly mediates the relationship between the two dimensions of paternalistic leadership (benevolent and moral leadership) and creativity, except for authoritarian leadership. Based on the results, this study contributes to the body of knowledge related to the appropriate leadership style in the local construction industry that can be extended to other developing countries with similar dynamics. It also helps the managers target and develops relevant skills to acquire positive outcomes from their team members. Full article

(This article belongs to the Special Issue Sustainable Disruptive Technologies in the Built Environment: A Step towards Industry 5.0)

► Show Figures

Figure 1

29 pages, 9027 KiB

Open AccessArticle

Hybrid Architecture Based System for the Establishment of Sustainable Environment in a Construction Site with 433 MHz LoRa and 2.4 GHz Zigbee

by Gangishetty Arun Kumar, Ajay Roy, Rajesh Singh, Anita Gehlot, Mamoon Rashid, Shaik Vaseem Akram, Sultan S. Alshamrani, Abdullah Alshehri and Ahmed Saeed AlGhamdi

Sustainability 2022, 14(10), 6280; https://0-doi-org.brum.beds.ac.uk/10.3390/su14106280 - 21 May 2022

Cited by 12 | Viewed by 2065

Abstract

The rapid development of technology has empowered us to achieve resilient infrastructure to establish a sustainable ecosystem. The construction site is one of the highest risk jobs for accident-related fatalities and injuries globally. From the previous studies, it is concluded that untrained or inexperienced workers were responsible for 40% of work-related accidents and the Health and Safety Executive (HSE) report concludes that inadequate working experience, knowledge, and safety awareness were the key causes of fatal accidents in the construction industry. Moreover, it is identified from previous studies that digital technology such as IoT with the assistance of wireless sensors can enhance the safety of construction sites. Based on this advantage, this study has implemented the hybrid architecture with the integration of the 2.4 GHz Zigbee, 433 MHz long-range (LoRa), and Wi-Fi communication protocol to monitor the health status of workers and construction sites and also to identify workers’ equipment wearing status in real-time scenarios. The proposed architecture is realized by implementing customized hardware, based on 2.4 GHz Zigbee, 433 MHz long-range (LoRa), and Wi-Fi. Furthermore, in the analysis of the evaluation metrics of LoRa, it is concluded that the lowest sensitivity is observed for SF 12 at BW 41.7 kHz and the highest is observed for SF 7 at BW 500 kHz; the maximum value data rate is observed at BW 500 kHz at CR 1 for SF 7, and the minimum data rate is observed at BW 41.7 at CR 4 for SF 12. In the future, the customized hardware will be implemented in different construction environments resolving possible challenges that empower to implementation of the proposed architecture in wide extensions. Full article

(This article belongs to the Special Issue Sustainable Disruptive Technologies in the Built Environment: A Step towards Industry 5.0)

► Show Figures

Figure 1

20 pages, 486 KiB

Open AccessArticle

Promoting Customer Loyalty and Satisfaction in Financial Institutions through Technology Integration: The Roles of Service Quality, Awareness, and Perceptions

by Kamran Iqbal, Hafiz Suliman Munawar, Hina Inam and Siddra Qayyum

Sustainability 2021, 13(23), 12951; https://0-doi-org.brum.beds.ac.uk/10.3390/su132312951 - 23 Nov 2021

Cited by 8 | Viewed by 3069

Abstract

This study examines the effects of quality of service, product awareness, and perceptions among customers of Islamic financial institutions (IFIs) on customer loyalty through technology integration using customer satisfaction as a mediator. A well-structured, comprehensive questionnaire was developed and data were collected from 203 respondents who were customers of six IFIs in Pakistan and had at least 2 years of experience in dealing confiorm this is correct with these IFIs. A total of 171 accurate responses were received from the respondents. Ten hypotheses were developed and statistically verified using regression and correlation analytical techniques. The results reveal that the quality of customer services and awareness of IFIs had a direct and positive relationship with customer loyalty, which in turn was mediated by customer satisfaction. Perceptions about IFIs had a direct positive relation with customer satisfaction. However, the relation of perceptions and quality of service with customer loyalty and satisfaction in financial institutions through technology integration was found to be insignificant, even in the presence of customer satisfaction as a mediator. Full article

(This article belongs to the Special Issue Sustainable Disruptive Technologies in the Built Environment: A Step towards Industry 5.0)

► Show Figures

Figure 1

21 pages, 4819 KiB

Open AccessArticle

Assessing Rainwater Harvesting Potential in Urban Areas: A Building Information Modelling (BIM) Approach

by Ahsen Maqsoom, Bilal Aslam, Sharjeel Ismail, Muhammad Jamaluddin Thaheem, Fahim Ullah, Hafiz Zahoor, Muhammad Ali Musarat and Nikolai Ivanovich Vatin

Sustainability 2021, 13(22), 12583; https://0-doi-org.brum.beds.ac.uk/10.3390/su132212583 - 15 Nov 2021

Cited by 9 | Viewed by 6345

Abstract

Water scarcity has become a major problem for many countries, resulting in declining water supply and creating a need to find alternative solutions. One potential solution is rainwater harvesting (RwH), which allows rainwater to be stored for human needs. This study develops an RwH assessment system through building information modeling (BIM). For this purpose, a hydrological study of Cfa-type climate cities is conducted with the example of Islamabad, Pakistan. The monthly rainfall data of three sites were assessed to determine the volume of the accumulated rainwater and its potential to meet human needs. The average number of people living in a house is taken as the household number. Household number or of the number of employees working at a small enterprise, roofing material, and rooftop area are used as the key parameters for pertinent assessment in the BIM. The data simulated by BIM highlight the RwH potential using five people per house as the occupancy and a 90 m² rooftop area for residential buildings or small enterprises as parameters. The results show that the selected sites can collect as much as 8,190 L/yr of rainwater (48 L/person/day) to 103,300 L/yr of rainwater (56 L/person/day). This much water is enough to fulfill the daily demands of up to five people. Therefore, it is established that the study area has an RwH potential that is able to meet the expected demands. This study presents a baseline approach for RwH to address water scarcity issues for residential buildings and factories of the future. Full article

(This article belongs to the Special Issue Sustainable Disruptive Technologies in the Built Environment: A Step towards Industry 5.0)

► Show Figures

Figure 1

12 pages, 1290 KiB

Open AccessArticle

Fuel Consumption Monitoring through COPERT Model—A Case Study for Urban Sustainability

by Muhammad Ali, Muhammad Daud Kamal, Ali Tahir and Salman Atif

Sustainability 2021, 13(21), 11614; https://0-doi-org.brum.beds.ac.uk/10.3390/su132111614 - 21 Oct 2021

Cited by 12 | Viewed by 3338

Abstract

Trackers installed in vehicles gives insights into many useful information and predict future mobility patterns and other aspects related to vehicles movement which can be used for smart and sustainable cities planning. A novel approach is used with the COPERT model to estimate fuel consumption on a huge dataset collected over a period of one year. Since the data size is enormous, Apache Spark, a big data analytical framework is used for performance gains while estimating vehicle fuel consumption with the lowest latency possible. The research presents peak and off-peak hours fuel consumption’s in three major cities, i.e., Karachi, Lahore and Islamabad. The results can assist smart city professionals to plan alternative trip routes, avoid traffic congestion in order to save fuel and time, and protect against urban pollution for effective smart city planning. The research will be a step towards Industry 5.0 by combining sustainable disruptive technologies. Full article

(This article belongs to the Special Issue Sustainable Disruptive Technologies in the Built Environment: A Step towards Industry 5.0)

► Show Figures

Figure 1

32 pages, 15730 KiB

Open AccessArticle

UAV Based Spatiotemporal Analysis of the 2019–2020 New South Wales Bushfires

by Fahim Ullah, Sara Imran Khan, Hafiz Suliman Munawar, Zakria Qadir and Siddra Qayyum

Sustainability 2021, 13(18), 10207; https://0-doi-org.brum.beds.ac.uk/10.3390/su131810207 - 13 Sep 2021

Cited by 18 | Viewed by 3278

Abstract

Bushfires have been a key concern for countries such as Australia for a long time. These must be mitigated to eradicate the associated harmful effects on the climate and to have a sustainable and healthy environment for wildlife. The current study investigates the 2019–2020 bushfires in New South Wales (NSW) Australia. The bush fires are mapped using Geographical Information Systems (GIS) and remote sensing, the hotpots are monitored, and damage is assessed. Further, an Unmanned Aerial Vehicles (UAV)-based bushfire mitigation framework is presented where the bushfires can be mapped and monitored instantly using UAV swarms. For the GIS and remote sensing, datasets of the Australian Bureau of Meteorology and VIIRS fire data products are used, whereas the paths of UAVs are optimized using the Particle Swarm Optimization (PSO) algorithm. The mapping results of 2019–2020 NSW bushfires show that 50% of the national parks of NSW were impacted by the fires, resulting in damage to 2.5 million hectares of land. The fires are highly clustered towards the north and southeastern cities of NSW and its border region with Victoria. The hotspots are in the Deua, Kosciu Sako, Wollemi, and Yengo National Parks. The current study is the first step towards addressing a key issue of bushfire disasters, in the Australian context, that can be adopted by its Rural Fire Service (RFS), before the next fire season, to instantly map, assess, and subsequently mitigate the bushfire disasters. This will help move towards a smart and sustainable environment. Full article

(This article belongs to the Special Issue Sustainable Disruptive Technologies in the Built Environment: A Step towards Industry 5.0)

► Show Figures

Figure 1

28 pages, 4287 KiB

Open AccessArticle

Using Multivariate Regression and ANN Models to Predict Properties of Concrete Cured under Hot Weather

by Ahsen Maqsoom, Bilal Aslam, Muhammad Ehtisham Gul, Fahim Ullah, Abbas Z. Kouzani, M. A. Parvez Mahmud and Adnan Nawaz

Sustainability 2021, 13(18), 10164; https://0-doi-org.brum.beds.ac.uk/10.3390/su131810164 - 10 Sep 2021

Cited by 22 | Viewed by 2653

Abstract

Concrete is an important construction material. Its characteristics depend on the environmental conditions, construction methods, and mix factors. Working with concrete is particularly tricky in a hot climate. This study predicts the properties of concrete in hot conditions using the case study of Rawalpindi, Pakistan. In this research, variable casting temperatures, design factors, and curing conditions are investigated for their effects on concrete characteristics. For this purpose, water–cement ratio (w/c), in-situ concrete temperature (T), and curing methods of the concrete are varied, and their effects on pulse velocity (PV), compressive strength (fc), depth of water penetration (WP), and split tensile strength (ft) were studied for up to 180 days. Quadratic regression and artificial neural network (ANN) models have been formulated to forecast the properties of concrete in the current study. The results show that T, curing period, and moist curing strongly influence fc, ft, and PV, while WP is adversely affected by T and moist curing. The ANN model shows better results compared to the quadratic regression model. Furthermore, a combined ANN model of fc, ft, and PV was also developed that displayed higher accuracy than the individual ANN models. These models can help construction site engineers select the appropriate concrete parameters when concreting under hot climates to produce durable and long-lasting concrete. Full article

(This article belongs to the Special Issue Sustainable Disruptive Technologies in the Built Environment: A Step towards Industry 5.0)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Sustainable Disruptive Technologies in the Built Environment: A Step towards Industry 5.0

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Published Papers (9 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI