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This paper presents the development of personal thermal comfort models for older adults and assesses the models’ performance compared to aggregate approaches. This is necessary as individual thermal preferences can vary widely between older adults, and the use of aggregate thermal comfort models can result in thermal dissatisfaction for a significant number of older occupants. Personalised thermal comfort models hold the promise of a more targeted and accurate approach.

Twenty-eight personal comfort models have been developed, using deep learning and environmental and personal parameters. The data were collected through a nine-month monitoring study of people aged 65 and over in South Australia, who lived independently. Modelling comprised dataset balancing and normalisation, followed by model tuning to test and select the best hyperparameters’ sets. Finally, models were evaluated with an unseen dataset. Accuracy, Cohen’s Kappa Coefficient and Area Under the Receiver Operating Characteristic Curve (AUC) were used to measure models’ performance.

On average, the individualised models present an accuracy of 74%, a Cohen’s Kappa Coefficient of 0.61 and an AUC of 0.83, representing a significant improvement in predictive performance when compared to similar studies and the “Converted” Predicted Mean Vote (PMVc) model.

While current literature on personal comfort models have focussed solely on younger adults and offices, this study explored a methodology for older people and their dwellings. Additionally, it introduced health perception as a predictor of thermal preference – a variable often overseen by architectural sciences and building engineering. The study also provided insights on the use of deep learning for future studies.

The changes brought by Industry 4.0 go beyond transformations in the industrial environment. The increasingly frequent digitization and robotization of activities is not only restricted to the industrial environment, but also to people's daily routine. People spend a large part of their time inside buildings, and maintaining adequate Indoor Environmental Quality (IEQ) is an essential factor for a healthy and productive environment. In this sense, the purpose of this study is to verify how the Internet of Things (IoT) is being used to improve the indoor environment, through sensors that instantly measure the conditions of the environment.

The aim of this paper is to verify, through a literature review, how IoT is being used for building control (for energy saving purposes) and to monitor IEQ conditions inside buildings, in order to provide a better environment for occupants, in terms of health and comfort. By combining keywords in databases, PRISMA method was used to select the articles for analysis, and 91 articles were analyzed.

The main findings in this research are: (1) the main purpose for applying IoT inside buildings is to reduce energy consumption; (2) there is an interest in developing low-cost sensoring devices with a learning approach; (3) Machine Learning methods are mainly used for energy saving purposes and to learn about occupants' behavior inside buildings, focusing on thermal comfort; (4) sensors in the IoT era are a requirement to help improve people's comfort and well-being.

Studies directly correlating IoT and IEQ are limited. This paper emphasises the link between them, through the presentation of recent methods to control the built environment.

Occupant feedback is crucial for healthy, comfortable and productive offices. Existing facility management (FM) systems are limited in effective use of occupant feedback, as they fail to collect the vital contextual information (e.g. related building element, space) associated with the feedback. The purpose of this study is to formalise the contextual information requirements for structured collection of occupant feedback for rapid diagnosis and resolution of problems and integrating occupant feedback with building information modelling (BIM) for making use of its visualisation and analysis capabilities, and eventually for effective use of occupant feedback in FM operations.

A mixed-methods approach was conducted in four steps: (1) identifying occupant feedback types (e.g. echo in meeting room) in office buildings, (2) examining the current practice in collecting and processing occupant feedback via use cases, (3) determining the contextual information requirements via expert interviews and (4) validation of the information requirements via a BIM-integrated prototype.

The findings present the contextual information requirements for 107 occupant feedback types grouped under thermal comfort, indoor air quality, acoustic comfort, visual comfort, building design and facility services.

Feedback-specific contextual information items enable structured data collection and help to avoid missing data and minimise the time lost in manual data entry and recursive interaction with the occupants during FM operations.

The contextual information requirements determined are expected to enhance occupant satisfaction and FM performance in office buildings by better use of the occupant feedback and integration into BIM-enabled FM and can be extended to other building types in future studies by using the proposed methodology.

Based on the systematic literature review, this paper aims to propose a framework of Construction 4.0 (C4.0) scenarios, identifying Industry 4.0 (I4.0) enabling technologies and their applications in the construction industry. The paper reviews C4.0 trends and potential areas for development.

In this research, a systematic literature review (SLR) methodology has been applied, including bibliographic coupling analysis (BCA), co-citation network analysis of keywords, the content analysis with the visualisation of similarities (VOSviewer) software and aggregative thematic analysis (ATA). In total, 170 articles from the top 22 top construction journals in the Scopus database between 2013 and 2021 were analysed.

Six C4.0 scenarios of applications were identified. Out of nine I4.0 technology domains, Industrial Internet of Things (IIoT), Cloud Computing, Big Data and Analytics had the most references in C4.0 research, while applications of augmented/virtual reality, vertical and horizontal integration and autonomous robotics yet provide ample avenues for the future applied research. The C4.0 application scenarios include efficient energy usage, prefabricated construction, sustainability, safety and environmental management, indoor occupant comfort and efficient asset utilisation.

This research contributes to the body of knowledge by offering a framework of C4.0 scenarios revealing the status quo of research published in the top construction journals into I4.0 technology applications in the sector. The framework evaluates current C4.0 research trends and gaps in relation to nine I4.0 technology domains as compared with more advanced industry sectors and informs academic community, practitioners and strategic policymakers with interest in C4.0 trends.

This paper aims to explore the meaning of smart office environments from a user perspective by investigating user preferences and expectations.

Eleven semi-structured interviews with the users after moving into a smart office building of a Dutch Municipality and an observation as complementary data were conducted. The data were analysed based on the grounded theory and thematic analysis, combining a reflexive approach to the literature review.

Two main themes were revealed addressing user expectations and preferences for smart office environments: “enhanced interaction” with the social and physical office environment and “sense-making” of the smart concept (or smartness). Within these themes, basic and smart office aspects were identified and classified based on their association with smart office concepts or technology.

The findings reveal the meaning of the smart office concepts from a user perspective by highlighting the importance of user experience on enhanced interaction and sense-making of the smart office concept, equipped with basic and smart aspects.

To the best of the authors’ knowledge, this study is the first to qualitatively examine drivers underlying the meaning of smart office concepts from a user point of view. Organisations, environmental psychologists, designers and managers can use the findings of this study to develop guidelines for a successful smart office design.

Digital twin (DT) and building information modeling (BIM) are interconnected in some ways. However, there has been some misconception about how DT differs from BIM. As a result, industry professionals reject DT even in BIM-based construction projects due to reluctance to innovate. Furthermore, researchers have repeatedly developed tools and techniques with the same goals using DT and BIM to assist practitioners in construction projects. Therefore, this study aims to assist industry professionals and researchers in understanding the relationship between DT and BIM and synthesize existing works on DT and BIM.

A systematic review was conducted on published articles related to DT and BIM. A total record of 54 journal articles were identified and analyzed.

The analysis of the selected journal articles revealed four types of relationships between DT and BIM: BIM is a subset of DT, DT is a subset of BIM, BIM is DT, and no relationship between BIM and DT. The existing research on DT and BIM in construction projects targets improvements in five areas: planning, design, construction, operations and maintenance, and decommissioning. In addition, several areas have emerged, such as developing geo-referencing approaches for infrastructure projects, applying the proposed methodology to other construction geometries and creating 3D visualization using color schemes.

This study contributed to the existing body of knowledge by overviewing existing research related to DT and BIM in construction projects. Also, it reveals research gaps in the body of knowledge to point out directions for future research.

The Internet of Things has made the shift to the digital era possible, even though the Architecture, Engineering and Construction (AEC) sector has not embraced nor integrated it within the core functions compared to other sectors. The need to enhance sustainable construction with the adoption of Internet of Things in this sector cannot be overemphasized. However, the real-world applications of Internet of Things in smart buildings remain relatively unexplored in the AEC sector due to several issues related to deployment and energy-saving potentials. Given these challenges, this paper proposes to identify the present state of development and research in Internet of Things and smart buildings, identify Internet of Things clusters and applications in smart buildings.

Bibliometric analyses of papers from 2010 to 2023 using the Scopus database and scientometric evaluations using the VosViewer software were undertaken. The proper search keyword was identified by using the phrases “ Internet of Things” and “Smart Building”. A total of 1158 documents in all, written by 3540 different writers, representing 2285 different institutions from 97 different countries were looked at. A metasynthesis was conducted and a system of Internet of Things applications in a smart building is illustrated.

The development of IoT and Smart Buildings is done in two phases: initiation (2010–2012) and development phase (2013–2023). The IoT clusters comprised Internet of things, energy efficiency, intelligent buildings, smart buildings and automation; while the most commonly used applications were analysed and established. The study also determined the productive journals, documents, authors and countries.

Documents published in the Scopus database from 2010 to 2023 were considered for the bibliometric analysis. Journal articles, conference papers, reviews, books and book chapters written in English language represent the inclusion criteria, while articles in press, conference reviews, letters, editorials, undefined sources and all medical and health publications were excluded.

The results of this study will be used by construction stakeholders and policymakers to identify key themes and applications in IoT-enabled smart buildings and to guide future research in the policymaking process of asset management.

The study utilised bibliometric analysis, scientometrics and metasynthesis to investigate Internet of things applications in smart buildings. The study identified Internet of things clusters and applications for smart building design and construction.

The purpose of this paper is to extend the attitudinal approach to entrepreneurial intentions by using a structural analysis to explore overlooked personal values as the antecedents of entrepreneurial attitude. Based on the widely adopted value system proposed by Schwartz, this study argues that while one cluster of personal values is positively correlated with entrepreneurial attitude that leads to entrepreneurial intention, another cluster of personal values is negatively correlated with entrepreneurial attitude.

Questionnaire responses obtained from a sample of 276 MBA were analyzed using structural equation models to examine the influences of values on entrepreneurial intentions via entrepreneurial attitude.

The results of this study demonstrate that personal values of self-direction, stimulation, achievement, and universalism are all positively correlated with entrepreneurial attitude, which together constitute a comfort zone for entrepreneurship, whereas values in the opposite end of the circumplex including benevolence, tradition, conformity, security, and power are negatively correlated with entrepreneurial attitude. The values that discourage the formation of an entrepreneurial attitude also counter the positive effect of entrepreneurial attitude on intention, making the relationship between entrepreneurial attitude and intention contingent upon value conflicts.

This study regards entrepreneurship as a career development and contributes to the entrepreneurship study by differentiating the influences of a vital construct, i.e., personal values, which should not be regarded as a universalism. The value circumplex with a comfort and discomfort zone developed by this study can serve as a platform to help build the view on entrepreneurial intentions in terms of personal values.

The purpose of this paper is to analyse thermal comfort and the thermal environment in naturally ventilated classrooms. Specifically, the aims of the study were to identify the thermal environment and thermal comfort of respondents in naturally ventilated university classrooms and compare them with the ASHRAE and Indonesian National Standard (SNI); to check on whether the predicted mean vote (PMV) model is applicable or not for predicting the thermal comfort of occupants in naturally ventilated university classrooms; and to analyse the neutral temperature of occupants in the naturally ventilated university classrooms.

The study was carried out at the new campus of Faculty of Engineering, Hasanuddin University, Gowa campus. A number of field surveys, which measured thermal environments, namely, air temperature, mean radiant temperature (MRT), relative humidity, and air velocity, were carried out. The personal activity and clothing properties were also recorded. At the same time, respondents were asked to fill a questionnaire to obtain their thermal sensation votes (TSV) and thermal comfort votes (TCV), thermal preference, and thermal acceptance. A total of 118 respondents participated in the study. Before the survey was conducted, a brief explanation was provided to the participants to ensure that they understood the study objectives and also how to fill in the questionnaires.

The results indicated that the surveyed classrooms had higher thermal environments than those specified in the well-known ASHRAE standard and Indonesian National Standard (SNI). However, this condition did not make respondents feel uncomfortable because a large proportion of respondents voted within the comfort zone (+1, 0, and −1). The predictive mean vote using the PMV model was higher than the respondents’ votes either by TSV or by TCV. There was a huge difference between neutral temperature using operative temperature (T_o) and air temperature (T_a). This difference may have been because of the small value of MRT recorded in the measured classrooms.

The research shows that the use of the PMV model in predicting thermal comfort in the tropic region might be misleading. This is because PMV mostly overestimates the TSV and TCV of the respondents. People in the tropic region are more tolerant to a higher temperature. On the basis of this finding, there is a need to develop a new thermal comfort model for university classrooms that is particularly optimal for this tropical area.