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In response to increasing global temperatures and energy demands, optimizing buildings’ energy efficiency, particularly in hot climates, is an urgent challenge. While current research often relies on conventional energy estimation methods, there has been a decrease in the efforts dedicated to leveraging AI-based methodologies as technology advances. This implies a dearth of multiparameter examinations in AI-driven extreme case studies. For this reason, this study aimed to enhance the energy performance of residential buildings in the hot climates of Dubai and Riyadh by integrating Building Information Modeling (BIM) and Machine Learning (ML). Detailed BIM models of a typical residential villa in these regions were created using Revit, incorporating conventional, modern, and green building envelopes (BEs). These models served as the basis for energy simulations conducted with Green Building Studio (GBS) and Insight, focusing on crucial building features such as floor area, external and internal walls, windows, flooring, roofing, building orientation, infiltration, daylighting, and more. To predict Energy Use Intensity (EUI), four ML algorithms, namely, Gradient Boosting Machine (GBM), Random Forest (RF), Support Vector Machine (SVM), and Lasso Regression (LR), were employed. GBM consistently outperformed the others, demonstrating superior prediction accuracy with an R2 of 0.989. This indicates that the model explains 99% of the variance in EUI, highlighting its effectiveness in capturing the relationships between building features and energy consumption. Feature importance analysis (FIA) revealed that roofs (29% in Dubai scenarios (DS) and 40% in Riyadh scenarios (RS)), external walls (19% in DS and 29% in RS), and windows (15% in DS and 9% in RS) have the most impact on energy consumption. Additionally, the study explored the potential for energy optimization, such as cavity green walls and green roofs in RS and double brick walls with VIP insulation and green roofs in DS. The findings of the paper should be interpreted in light of certain limitations but they underscore the effectiveness of combining BIM and ML for sustainable building design, offering actionable insights for enhancing energy efficiency in hot climates.

Amro Omari,1,2 Momin Samad,3 Saaquib R Bakhsh,1,2 Jahan Tajran,4 George A Williams1,2 1Beaumont Eye Institute, Royal Oak, MI, USA; 2Oakland University William Beaumont School of Medicine, Rochester, MI, USA; 3Michigan State College of Human Medicine, Grand Rapids, MI, USA; 4Wayne State University School of Medicine, Detroit, MI, USACorrespondence: Amro Omari, Beaumont Eye Institute, 3535 West 13 Mile Road Suite #555, Royal Oak, MI, 48073, USA, Tel +1 248-551-2020, Email Amro.omari@beaumont.orgBackground and Objective: To assess the use of a smartphone-based fundus camera in diagnosing acute posterior segment disease in the emergency room (ER) of an academic center, where standard fundus cameras are currently unavailable.Study Design/Patients and Methods: Images were obtained on 10 eyes with various types of posterior segment pathology. These were analyzed retrospectively by 35 reviewers (21 residents and 14 attending physicians). Accuracy of diagnosis was compared to in-office fundus photography.Results: The average probability of correct diagnosis was over 78%. There was no difference between resident and attending reviewers, except for one of the vitreous hemorrhage cases where residents performed better (p=0.039).Conclusion: Smartphone-based cameras are effective in screening for ocular diseases in the ER of academic centers, where residents and fellows are often the first providers to see various types of vision and life-threatening pathology.Keywords: fundus camera, emergency room, posterior segment, smartphone, retina

IntroductionThe present study investigates the status of education for sustainable development (ESD) as an approach applied to higher education. This study proposes cognitive, behavioral, and socio-emotional learning as the three dimensions of ESD. It examines the extent to which the current curriculum and educational structures applied by Qatar University (QU) contribute to sustainable development (SD) skills in students' attitudes.MethodsData from 319 students were collected. Factor analysis was performed to regroup skill items within the consistent dimensions of 16 core skills. A multivariate logistic regression analysis was performed to examine the relationship between core skills and the five SD skills.FindingsFive logistic regression models were investigated. Model 1 found two predictors that only increase the odds of innovative thinking skills. Model 2 detected five predictors that increase the odds of critical thinking skills. Model 3 defined three predictors that increase the odds of resolving problem skills. Model 4 mentioned four predictors that increase the odds of communication/coordination skills. Model 5 showed two predictors that increase the odds of decision-making skills.Research implicationsThis study introduces new core skills that have been reclassified based on students' real-world experiences. Such findings may help target specific skills and bring significant changes in higher education outcomes.

In response to increasing global temperatures and energy demands, optimizing buildings' energy efficiency, particularly in hot climates, is an urgent challenge. While current research often relies on conventional energy estimation methods, there has been a decrease in the efforts dedicated to leveraging AI-based methodologies as technology advances. This implies a dearth of multiparameter examinations in AI-driven extreme case studies. For this reason, this study aimed to enhance the energy performance of residential buildings in the hot climates of Dubai and Riyadh by integrating Building Information Modeling (BIM) and Machine Learning (ML). Detailed BIM models of a typical residential villa in these regions were created using Revit, incorporating conventional, modern, and green building envelopes (BEs). These models served as the basis for energy simulations conducted with Green Building Studio (GBS) and Insight, focusing on crucial building features such as floor area, external and internal walls, windows, flooring, roofing, building orientation, infiltration, daylighting, and more. To predict Energy Use Intensity (EUI), four ML algorithms, namely, Gradient Boosting Machine (GBM), Random Forest (RF), Support Vector Machine (SVM), and Lasso Regression (LR), were employed. GBM consistently outperformed the others, demonstrating superior prediction accuracy with an R2 of 0.989. This indicates that the model explains 99% of the variance in EUI, highlighting its effectiveness in capturing the relationships between building features and energy consumption. Feature importance analysis (FIA) revealed that roofs (29% in Dubai scenarios (DS) and 40% in Riyadh scenarios (RS)), external walls (19% in DS and 29% in RS), and windows (15% in DS and 9% in RS) have the most impact on energy consumption. Additionally, the study explored the potential for energy optimization, such as cavity green walls and green roofs in RS and double brick walls with VIP insulation and green roofs in DS. The findings of the paper should be interpreted in light of certain limitations but they underscore the effectiveness of combining BIM and ML for sustainable building design, offering actionable insights for enhancing energy efficiency in hot climates. [ABSTRACT FROM AUTHOR]

The frequency of natural disasters is exacerbated by the escalating impacts of climate change with the need for effective relief shelters for victims and displaced individuals. Providing accessible and easy-to-assemble relief shelters is essential in addressing these needs. Due to climate-related challenges and the need for sustainable solutions, the integration of circularity principles in shelter design has become imperative. Circular economy principles promote the efficient use of resources, minimising waste generation and the cost of shelters. Moreover, a considerable number of people usually suffer from homelessness, and an increasing number of families live in slums in every part of the globe. All such people are entitled to be housed in affordable, safe, and appropriate shelters for at least several months after a disaster until they can either rebuild their former houses or find somewhere decent to settle after recovering from the hardship. With the aim of investigating the immediate housing needs of people after a disaster, this paper identifies the essential factors that must be taken into account during shelter design. The paper also presents the prototype developed on the basis of theoretical criteria and the identified factors. The paper’s main objectives were to design an easy-to-assemble emergency shelter on circular economy principles, identify critical factors for the circularity and buildability of the shelter, and present a proposed smart shelter acceptance model. The methodology behind the research involves conducting an intensive literature review and creating a novel prototype of a smart disaster relief shelter on the basis of long-run laboratory work and various prototype iterations. The paper presents the details of the novel prototype and shows materials that enhance the circularity of the shelter, according to a unique architectural design strategy of ‘reusing’ materials to enhance circularity practice in the design and construction sectors. The prototype was developed in a workshop after 6 months of reiterations using plastic water bottles, basic pipes, and other reusable materials. Then, by incorporating the essential factors, a set of criteria was designed that can be used as a guide for the architectural design of shelters. The criteria offered in this paper are useful to evaluate each factor’s importance in shelter design. In total, 51 effective factors in designing and constructing such accommodation are presented, clustered into five design strategy groups: social–cultural, physical–technical, environmental, economic, and organisational.

It is well known that finite element analysis (FEA) is a powerful tool when it comes to the design and analysis of complex structures for various load combinations, including light steel curve members. This abstract provides an overview of the FEA simulation process for designing such members (cure members), which involves constructing a 3D model, discretising the member into small elements, assigning material properties, defining boundary conditions, conducting the simulation, analysing the results, and making any necessary modifications to the design. FEA simulations can provide valuable insights into the behaviour of light steel curved members under different load combinations. This enables designers to optimise designs for strength, safety, and cost-effectiveness. This article proposes using two commercial 3D software programs, Rhino 7 and Strand7, to complete the FEA simulation of light steel curved members. The 3D model is created in Rhino 7, and the individual elements are discretised into more minor elements using Strand7 for assigning material properties, defining boundary conditions, running simulations, and analysing the results. The paper presents five case studies of steel–glass façades and applies the proposed methodology to each. Examples include Phoenix International Media Center in Beijing, Kazakhstan Pavilion and Science Museum in Astana, Moynihan Train Hall in New York City, Chadstone Shopping Centre in Melbourne, and the central light rail station in The Hague.

Addressing clients’ demands, designers have become increasingly concerned about the operation phases of buildings and, more specifically, energy consumption. This issue has become more prominent as people realize that the Earth’s resources are limited and depleted, and buildings are major energy consumers. Building Information Modelling (BIM) has gained popularity in recent years and is now widely used by architects, engineers, and construction teams to collaborate and provide a comprehensive design that follows a sustainable strategy. The objective of this research is to examine how building variables are linked to energy consumption in various building shapes, achieved by building prototypes. The accuracy of the regression models is evaluated by undergoing a validation process. Consequently, this study created building information models of selected education facility office rooms and used Autodesk Insight 360 and Green Building Studio (GBS) to perform energy simulations. A 6 Green Star education building in Australia is chosen as the case study of this paper. Thirteen variables related to building internal design were examined, and five were found to endure a substantial effect on building energy consumption. The study also looked at the window-to-wall ratio (WWR), which was analyzed by multi-linear regression; however, the results showed that the model did not fit well, and the error obtained during the validation process ranged from 1.0% to 26.0%, which is unacceptable for this research. These findings highlight some limitations in using BIM tools and linear regression methods and discuss some potential improvements that can be achieved in future studies.

The architecture service industry has typically been slow in accepting new digital technologies due to many reasons, such as the industry’s complexity, the diverse sizes of companies, client types, and stakeholders’ technical skills. The combination of these business service factors with those that affect the intention of a user to use a technology offers a novel model for predicting the success of technology adoption in this business. This study aims to identify the factors in the architecture industry that influence the process of technology adoption. The process of qualitative data collection was conducted using semi-structured interviews with the participation of 30 architecture and design managers to explore the factors that they consider important when adopting digital technology in their organizations. This was conducted to compare these factors with those identified by users as influential in the adoption of digital technology. The analysis was conducted in three stages, namely transcribing, coding, and extracting major themes. This study will further help in identifying whether managers viewed the factors identified in the quantitative study as significant in affecting their decisions to adopt the technology. The major findings of this study revealed that several factors influence the adoption of technology in the architecture industry at the managerial level. These factors include cost, brief preparation, service quality, result demonstrability, project time, environmental considerations, training considerations, and user-friendliness.

Polarimetric Synthetic Aperture Radar Interferometry (Pol-InSAR) based forest height estimation for ecosystem monitoring and management has been developing rapidly in recent years. Spaceborne Pol-InSAR systems with long temporal baselines of several days always lead to severe temporal decorrelation, which can cause a forest height overestimation error. However, most forest height estimation studies have not considered the change in dielectric property as a factor that may cause temporal decorrelation with a long temporal baseline. Therefore, it is necessary to propose a new method that considers dielectric fluctuations and random motions of scattering elements to compensate for the temporal decorrelation effect. The lack of ground truth for forest canopy also needs a solution. Unsupervised methods could be a solution because they do not require the use of true values of tree heights as the ground truth to calculate their estimation accuracies. This paper aims to present an unsupervised forest height estimation method called Dielectric Fluctuation and Random Motion over Ground (DF-RMoG) to improve accuracy by considering the dielectric fluctuations and random motions. Its performance is investigated using Advanced Land Observing Satellite (ALOS)-1 Pol-InSAR data acquired over a German forest site with temporal intervals of 46 and 92 days. The authors analyze the relationship between forest height and different parameters with DF-RMoG and conventional models. Compared with conventional models, the proposed DF-RMoG model significantly reduces the overestimation error due to temporal decorrelation in forest height estimation according to its lowest average forest height.

NZCCs aim to minimise urban carbon emissions for healthier cities in line with national and international low-carbon targets and Sustainable Development Goals (SDGs). Many countries have recently adopted Net-Zero Carbon City (NZCC) policies and strategies. While there are many studies available on NZCC cities' definitions and policymaking, currently, research is rare on understanding the role of urban data-driven technologies such as Building Information Modelling (BIM) and Geographic Information Systems (GIS), as well as AI, for achieving the goals of NZCCs in relation to sustainable development goals (SDGs), e.g., SDGs 3, 7,11, 13, and 17. This paper aims to fill this gap by establishing a systematic review and ascertaining the opportunities and barriers of data-driven approaches, analytics, digital technologies, and AI for supporting decision-making and monitoring progress toward achieving NZCC development and policy/strategy development. Two scholarly databases, i.e., Web of Science and Scopus databases, were used to find papers based on our selected relevant keywords. We also conducted a desktop review to explore policies, strategies, and visualisation technologies that are already being used. Our inclusion/exclusion criteria refined our selection to 55 papers, focusing on conceptual and theoretical research. While digital technologies and data analytics are improving and can help in the move from net-zero carbon concepts and theories to practical analysis and the evaluation of cities' emission levels and in monitoring progress toward reducing carbon, our research shows that these capabilities of digital technologies are not used thoroughly yet to bridge theory and practice. These studies ignore advanced tools like city digital twins and GIS-based spatial analyses. No data, technologies, or platforms are available to track progress towards a NZCC. Artificial Intelligence, big data collection, and analytics are required to predict and monitor the time it takes for each city to achieve net-zero carbon emissions. GIS and BIM can be used to estimate embodied carbon and predict urban development emissions. We found that smart city initiatives and data-driven decision-making approaches are crucial for achieving NZCCs. [ABSTRACT FROM AUTHOR]

The construction industry is slow to adopt new technologies. The implementation of digital technologies and remote operations using robots were considered farfetched affairs and unbelievable approaches. However, the effect of COVID-19 on clients and construction companies put high pressure on construction managers to seek digital solutions and justified the need for remote operating or distant controlling technologies. This paper aims to investigate the state of play in construction technology implementation and presents a roadmap for developing and implementing required technologies for the construction industry. The COVID-19 disruption required new methods of working safely and remotely and coincided with the advent of advanced automation and autonomous technologies. This paper aims to identify gaps and 11 disruptive technologies that may lead to upheaval and transformation of the construction sector, perhaps in this decade. A road map for technology implementation can be helpful in developing business strategies at the organizational level as a theoretical measure, and it can facilitate the technology implementation process at the industry level as a practical measure. The roadmap can be used as a framework for policymakers to set industry or company strategies for the next 10 years (2030).

The performance of the construction industry can be improved by understanding the factors that affect the productivity of its equipment. A hypothetical framework was used to analyze six vital construction equipment parameters to understand how they affect productivity in construction projects. Data collected through a survey of 110 respondents in the construction industry were analyzed using an exploratory factor analysis (EFA) and structural equation modeling (SEM). The final model obtained using SEM consisted of 31 attributes from six construction equipment productivity factors, namely management (MG), materials (MT), human (HM), technical (TN), environmental (EM), and other factors (OT). Construction equipment productivity was found to be significantly influenced by MT and OT in construction projects. This was mainly because of their corresponding subfactors, such as operating life and equipment age, and the occurrence of accidents during construction. Consequently, based on survey feedback from various construction professionals, present gaps in construction equipment productivity were analyzed, and recommendations were made to overcome the main limiting factors under MT and OT. This study identified and quantified the interrelationships between various construction equipment productivity constraints. Therefore, the results can help experts and specialists better comprehend how to overcome delays due to idle time and improve construction equipment productivity.

Blockchain technology (BCT) can enable distributed collaboration, enhance data sharing, and automate back-end processes for digital twin (DT) decentralized applications (dApps) in the construction industry (CI) 4.0. The aim of this paper was to propose a software architecture and to develop a framework of smart contracts for blockchain-based digital twin (BCDT) dApps throughout the lifecycle of projects in CI 4.0. This paper leveraged the existing literature and action research interviews to identify and validate the critical industry problems, functional requirements (FRs), and non-functional requirements (NFRs) to be addressed by BCDT dApps in CI 4.0. Basic use cases were developed to design a framework of smart contracts for BCDT dApps throughout the lifecycle of projects. The analysis of an online survey was used to identify the key requirements and enablers to propose a software architecture for BCDT applications and to validate the requirements for developing the framework of a smart contract for BCDTs. The findings were: (1) The identification of key problems in CI 4.0 for each BIM/BCDT dimension (3D, 4D, 5D, 6D, 7D, 8D, and contractual (cD)) and the related FRs and NFRs for BCDT applications. Additionally, key use cases were designed to address the problems identified. (2) The proposed BCDT architecture permitted us to narrow gaps in the literature on blockchain-based decentralized digital twins. Moreover, the proposed BCDT architecture and smart-contract framework addressed the main requirements in the literature on BCDTs. (3) The study leveraged the non-fungible token (NFT) standard to develop a framework for smart contracts that addressed the key use cases and the related industry problems and functional requirements that were identified. The study also considered the contractual dimension (cD) as an overarching dimension in relation to the other BCDT dimensions. (4) We also compared the costs of several public blockchains for executing the proposed smart-contract framework throughout the lifecycle of a medium-sized building project. The cost analysis permitted the development of criteria to evaluate the suitability of blockchain networks for BCDT applications in CI 4.0 depending on the principal blockchain networks’ properties (security, decentralization, scalability, and interoperability). Finally, this study resulted in a novel framework that included software architecture, smart-contract use cases, and selection criteria among blockchain networks for BCDT dApps in CI 4.0.

Cost overrun has remained a key risk of construction projects that can be prevented by utilizing new technologies. This paper aims to identify the gap in the literature, which can potentially be addressed by using digital tools and technologies, by reviewing the current and state of the art practices. The paper presents the results of a systematic and critical content reviews on cost overruns, to address the question of what factors are affecting the cost overrun. This paper also reviews how building information modeling (BIM) in conjunction with other tools, such as the common tools in the Asia and Asia Pacific regions, are used for cost estimation and monitoring. The paper presents the results of the content review, including their contributions and limitations, which are also used to set key directions for future investigations. A total of 176 papers was identified to develop the construction cost management (CCM) dataset. The method was a mix of systematic reviews, including co-authorship network analyses, co-occurrence analytical map development covering 5671 keywords, and content analysis including theme identification and a critical review of selected papers. The paper critically reviewed 63 selected papers from CCM, which are divided into four clusters based on their scopes: BIM adoption for cost estimation and quantity surveying; BIM implementation for a bill of quantity, risk paths, and cost overruns; cost control and management; and, finally, BIM, virtual design, and value management. A trend analysis using a set of 16 themes (e.g., 3D model, BIM, Decision, Energy, and Life Cycle) for all the papers over the past ten years was developed. The content of each cluster of papers was reviewed based on the frequency of the selected themes in each cluster. The content of each cluster of papers was also reviewed critically and gaps were identified, so a set of directions for future investigations are presented.

The construction industry is facing constant pressure to improve its poor safety record and low productivity rate. A significant amount of research has been undertaken to identify the best practices to enhance productivity and safety. Nevertheless, the mainstream research in the field of construction focuses on one of these issues rather than implementing a holistic approach to resolve them. Consequently, the interactions between productivity and safety cannot be fully understood. Recent studies have demonstrated that management strategies and practices for improving labour productivity can trigger a series of unintended consequences that affect safety performance in construction projects. However, the behavioural aspects of these unintended consequences have yet to be investigated. This research addresses the gap by measuring the impacts of seven management strategies for improving labour productivity on the safety behaviour of construction labourers. A total of 191 construction labourers participated in a survey designed based on the Management Strategy Assessment Index (MSAI). The results show that the implemented management strategies for improving labour productivity have a greater impact on shaping safety compliance (SC) behaviours than safety participation (SP) behaviours of labourers. This study took a further step by breaking down the management strategies to their constitutive practices and measuring their impacts on SC and SP, and labour productivity. This paper provides further insight into the complex relationship between the productivity and safety behaviour of construction labourers. The findings can help project managers to improve labour productivity without harming their safety unintentionally.

This paper aims to investigate the dynamic behavior of composite steel–concrete floor systems under both free and forced vibrations. A combination of numerical and analytical methods was comprehensively employed to calibrate the suggested solutions to extend the application of accurate numerical methods in future design purposes. Different commercial Finite Element Packages including ABAQUS/CAE and Strand7 were precisely utilized. The obtained results from the Finite Element Simulations were broadly compared with the available international design guidelines including British Standard BS 6472 and international standard ISO 10137. The first 10 active vibration modes in different composite steel–concrete beam floor systems were numerically investigated. Different concrete slabs by respecting the designated various types of secondary and primary steel beam components were comprehensively examined. It was found that the lengths of primary and secondary beams can considerably influence the computed fundamental frequencies and the response factors of the simulated composite floor system. Based on carrying out an extensive parametrical study, further practical recommendations were suggested to provide a reliable benchmark for structural designers.

Smart buildings (SBs) are developed in many different ways and are self-proclaimed smart. There are a great number of publications introducing smart systems using a wider range of tools and sensors. However, the level of smartness, functions of the smart system, and the usefulness of the system are not the same, which may give a wrong impression to clients or potential buyers of a building. Developing a scoring system that enables determining the overall smartness of a building is necessary. Despite the necessity, there is a dearth of studies in this area. Hence, the purpose of this study is to develop a scoring system to evaluate the level of smartness of Sri Lankan commercial buildings. Thus, initially, smart criteria were identified, defined, and categorized through a literature survey. Subsequently, 35 experts in the commercial building sector were interviewed. Finally, the relative importance of the smart criteria was derived through the AHP technique, and accordingly, a scoring system was developed. The study identified six main criteria to evaluate the smartness of buildings in the scoring system. The automation criterion with the highest relative weight was concluded to be the dominant criterion (45.59%) in the scoring system. Communication and data sharing were placed at second with a relative weight of 18.76% and indicates the importance given by the study findings in establishing the backbones of SBs. Occupants’ comfort, energy management, occupants’ health and safety, and sustainability criterion were ranked third, fourth, fifth, and sixth within the scoring system. This study is one of the first to investigate in detail the contribution of both soft and hard services of a facility in determining the overall smartness of a building. Property developers in the commercial building sector can benefit from this study by recognizing the necessary criteria to be embedded in their SB development projects in order to attract more tenants and customers.