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The Application of Soft Computing Technologies and Techniques in Construction Sustainable Developments

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 12875

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Special Issue Editors

Dr. Saeed Reza Mohandes

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Guest Editor

Department of Building and Real Estate (BRE), Faculty of Construction and Environment (FCE), The Hong Kong Polytechnic University, Hong Kong, China
Interests: artificial intelligence and machine learning in construction; construction safety; sustainable development; infrastructure management; building energy efficiency; facility management
Special Issues, Collections and Topics in MDPI journals

Dr. Poorang Piroozfar

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Guest Editor

School of Architecture, Technology and Engineering, University of Brighton, Brighton, UK
Interests: BIM/BES, augmented/virtual/mixed reality (XR); application of AI/ML/fuzzy logic in the AEC industry; smart cities; (smart) building envelopes/urban façades; modularisation/platform design; OSP/OSC/OSM; rapid prototyping/additive manufacturing; customisation and personalisation; design research; design philosophy

Dr. Sara Shirowzhan

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Guest Editor

City Futures Research Centre, School of Built Environment, University of New South Wales, Sydney, Kensington, NSW 2052, Australia
Interests: sensing technologies; AI; machine learning; advanced GIS; BIM; digital twins; city analytics methods; digital construction; smart cities; smart construction
Special Issues, Collections and Topics in MDPI journals

Dr. Serdar Durdyev

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Guest Editor

Department of Architectural and Engineering Studies, Ara Institute of Canterbury, Christchurch, New Zealand
Interests: building information modelling; construction management; sustainable construction; construction engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The architecture, engineering and construction (AEC) industry is one of the major contributors to energy consumption, resource depletion, environmental pollution, and therefore to climate change. To resolve these problems, giant leaps towards preserving our environment have been attempted through introduction, facilitation, and promotion of environmentally conscientious design, construction, and development. With this in mind, the applications of soft-computing-based techniques and technologies (SCTT) to facilitate sustainability in design, construction, and development have brought about significant improvements, such as improving buildings’ embodied and operational energy efficiency, reduction in raw material consumption and construction waste, and reduction of GHG emissions, to name but a few. Nevertheless, the application of such innovative and smart technologies still remains in its infancy, and hence, there is a dire need to build up an appropriate podium for interested researchers to share their research and exchange their innovative ideas with like-minded fellow researchers in this fecund arena in a more structured, better organised, and more consistent manner.

Considering the importance of the above themes, this Special Issue provides a platform for interested researchers to take further steps towards enhancing sustainability in the AEC sector using SCTT through innovative approaches, methodologies, solutions, and techniques such as (but not limited to) digitalisation, digital twins, building information modelling (BIM), Internet of Things (IoT), fuzzy logic, deep learning (DL), machine learning (ML), artificial intelligence (AI), optimisation algorithms, and probabilistic simulation approaches. This Special Issue also welcomes conceptual models which focus on improving the status quo based on the exploitation of SCTT. Additionally, different types of quality review papers including (but not limited to) scientometric analysis, bibliometric reviews, critical reviews, and meta-analyses will be considered for review and publication. Moreover, studies focusing on any linkage between the outbreak of COVID-19 and the concerned domains in technology (in/of/for the AEC) with longer impacts on, or potential to transform or modernise, the AEC industry in the post-COVID-19 era will be given special consideration and reviewed with priority.

Dr. Saeed Reza Mohandes
Dr. Poorang Piroozfar
Dr. Sara Shirowzhan
Dr. Serdar Durdyev
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. 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

Green construction
Biophilic design/architecture
Sustainable design/construction/developments
Waste reduction/management
Building energy modelling/simulation
Green supply chain (management)
Fuzzy logic
Artificial neural networks
Metaheuristic algorithms
Stochastic optimisation
Probabilistic modelling
Bayesian networks
Internet of Things
Digital transformation
Digitalisation
Digital twins
Deep learning
Machine learning
Genetic algorithm
Multicriteria decision making
Neuro-fuzzy
Monte Carlo simulation
Mixed integer linear programming
Lean and agile development
GIS
BIM
Remote sensing technologies
Artificial intelligence (AI)

Published Papers (4 papers)

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Research

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20 pages, 1887 KiB

Open AccessArticle

Artificial Neural Network-Forecasted Compression Strength of Alkaline-Activated Slag Concretes

by Yi Xuan Tang, Yeong Huei Lee, Mugahed Amran, Roman Fediuk, Nikolai Vatin, Ahmad Beng Hong Kueh and Yee Yong Lee

Sustainability 2022, 14(9), 5214; https://doi.org/10.3390/su14095214 - 26 Apr 2022

Cited by 31 | Viewed by 1994

Abstract

The utilization of ordinary Portland cement (OPC) in conventional concretes is synonymous with high carbon emissions. To remedy this, an environmentally friendly concrete, alkaline-activated slag concrete (AASC), where OPC is completely replaced by ground granulated blast-furnace slag (GGBFS) industrial waste, is one of the currently pursued research interests. AASC is not commonly used in the construction industry due to limitations in experience and knowledge on the mix proportions and mechanical properties. To circumvent great labour in the experimental works toward the determination of the optimal properties, this study, therefore, presents the compressive strength prediction of AASC by employing the back-propagation artificial neural network (ANN) modelling technique. To construct this model, a sufficiently equipped experimental databank was built from the literature covering varied mix proportion effects on the compressive strength of AASC. For this, four model variants with different input parameter considerations were examined and the ideal ANN architecture for each model with the best input number–hidden layer neuron number–output number format was identified to improve its prediction accuracy. From such a setting, the most accurate prediction model with the highest determination coefficient, R², of 0.9817 was determined, with an ANN architecture of 8-18-1 containing inputs such as GGBFS, a fine to total aggregate ratio, sodium silicate, sodium hydroxide, mixing water, silica modulus of activator, percentage of sodium oxide and water–binder ratio. The prediction accuracy of the optimal ANN model was then compared to existing ANN-based models, while the variable selection was compared to existing AASC models with other machine learning algorithms, due to limitations in the ANN-based model. To identify the parametric influence, the individual relative importance of each input variable was determined through a sensitivity analysis using the connection weight approach, whose results indicated that the silica modulus of the activator and sodium silicate greatly affected the AASC compressive strength. The proposed methodology demonstrates that the ANN-based model can predict the AASC compressive strength with a high accuracy and, consequently, aids in promoting the utilization of AASC in the construction industry as green concrete without performing destructive tests. This prediction model can also accelerate the use of AASC without using a cement binder in the concrete matrix, leading to produce a sustainable construction material. Full article

(This article belongs to the Special Issue The Application of Soft Computing Technologies and Techniques in Construction Sustainable Developments)

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13 pages, 8057 KiB

Open AccessArticle

Aggregate Simulation with Statistical Approach Considering Substituting

by Byeong Hun Woo, Jeong Bae Lee, Hyunseok Lee and Hong Gi Kim

Sustainability 2022, 14(3), 1644; https://doi.org/10.3390/su14031644 - 30 Jan 2022

Cited by 2 | Viewed by 2319

Abstract

This work focused on reflecting the substituting ratio of fine aggregate in an aggregate simulation. The existing simulation studies showed superior performance on generating the particles; however, the studies did not and could not reflect the substituting ratio of fine aggregate. Therefore, a statistical approach with the Monte Carlo simulation method was tried to improve the lacking part. According to the fitting of the distributions, the Cauchy distribution was best for the natural sand and the log-normal distribution was best for the substituting materials. The chosen two distributions were mixed and applied, using the Monte Carlo method with the mixed model, rather than the existing particle generation formula of the simulation. The substitution ratio was considered to be 0, 30, 50, 70, 100%. The fraction of small particles was gradually increased by the substituting ratio. As a result, the simulated particle distribution reflected well the statistical model. In addition, the simulation was almost the same as that of real particle distribution, according to the CT scanning. Full article

(This article belongs to the Special Issue The Application of Soft Computing Technologies and Techniques in Construction Sustainable Developments)

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Review

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17 pages, 3739 KiB

Open AccessReview

Evaluation of Complexity Issues in Building Information Modeling Diffusion Research

by Longhui Liao, Kaixin Zhou, Cheng Fan and Yuanyuan Ma

Sustainability 2022, 14(5), 3005; https://doi.org/10.3390/su14053005 - 04 Mar 2022

Cited by 7 | Viewed by 3059

Abstract

This study aimed to ascertain the research status of complexity issues in building information modeling (BIM) diffusion and identify future research directions in this field. A total of 366 relevant journal articles were holistically evaluated. The visualization analysis indicated that management aspects, emergent trends (such as green building, facility management, and automation), and theme clusters (such as interoperability, waste management, laser scanning, stakeholder management, and energy efficiency) are shaping BIM research towards complexity. Areas such as supply chain, cost, digital twin, and web are also essential. The manual qualitative evaluation classified the complexity issues in BIM diffusion research into three types (complexities of network-based BIM evolution, impact of BIM adoption circumstances, and BIM-based complexity reduction for informed decision making). It was concluded that BIM has been shifting towards information models and systems-based life cycle management, waste control for healthy urban environments, and complex data analysis from a big data perspective, not only in building projects but also in heritage and infrastructure, or at the city scale, for informed decision making and automatic responses. Future research should investigate the co-evolution between collaborative networks and BIM artefacts and work processes, quality improvement of BIM-based complex networks, BIM post-adoption behaviors influenced by complex environmental contexts, and BIM-based complexity reduction approaches. Full article

(This article belongs to the Special Issue The Application of Soft Computing Technologies and Techniques in Construction Sustainable Developments)

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19 pages, 3650 KiB

Open AccessReview

A Comprehensive Review of Deterrents to the Practice of Sustainable Interior Architecture and Design

by Mojtaba Ashour, Amir Mahdiyar and Syarmila Hany Haron

Sustainability 2021, 13(18), 10403; https://doi.org/10.3390/su131810403 - 17 Sep 2021

Cited by 6 | Viewed by 4384

Abstract

The interior environment as the place where people spend nearly 95% of their time in, has recently received considerable attention within the domain of the built environment. The concept of Sustainable Interior Architecture and Design (SIAD) and its significance have been recognized given its potential for energy conservation, and its impacts on occupants’ satisfaction, comfort, as well as their physical and psychological wellbeing. Although the adoption of SIAD is crucial in achieving the sustainable development goals, its practice is still hindered by numerous deterrents. A number of studies have reported on these deterrents; however, there is no comprehensive review of the literature on this topic. Thus, as a first step toward addressing the present gap, this article provides a two decade (2000–2021) systematic review of the relevant literature that investigates a total of 51 publications. Furthermore, a scientometric analysis was conducted, and the co-citation and co-occurrence of journals and keywords were analyzed to illustrate the scientific landscape. A comprehensive summary table is provided consisting of 61 deterrents to the practice of SIAD that are categorized into five main categories: (1) economic; (2) attitude, knowledge, and awareness; (3) market, information, and technology; (4) education and training; as well as (5) government and professional bodies. Finally, the findings are deliberated upon and directions for future research are discussed. Full article

(This article belongs to the Special Issue The Application of Soft Computing Technologies and Techniques in Construction Sustainable Developments)

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