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Developing a reliable cost estimate at the early stage of construction projects is challenging due to inadequate project information. Most of the information during this stage is qualitative, posing additional challenges to achieving accurate cost estimates. Additionally, there is a lack of tools that use qualitative project information and forecast the budgets required for project completion. This research, therefore, aims to develop a model for setting project budgets (excluding land) during the pre-conceptual stage of residential buildings, where project information is mainly qualitative.

Due to the qualitative nature of project information at the pre-conception stage, a natural language processing model, DistilBERT (Distilled Bidirectional Encoder Representations from Transformers), was trained to predict the cost range of residential buildings at the pre-conception stage. The training and evaluation data included 63,899 building permit activity records (2021–2022) from the Victorian State Building Authority, Australia. The input data comprised the project description of each record, which included project location and basic material types (floor, frame, roofing, and external wall).

This research designed a novel tool for predicting the project budget based on preliminary project information. The model achieved 79% accuracy in classifying residential buildings into three cost_classes ($100,000-$300,000, $300,000-$500,000, $500,000-$1,200,000) and F1-scores of 0.85, 0.73, and 0.74, respectively. Additionally, the results show that the model learnt the contextual relationship between qualitative data like project location and cost.

The current model was developed using data from Victoria state in Australia; hence, it would not return relevant outcomes for other contexts. However, future studies can adopt the methods to develop similar models for their context.

This research is the first to leverage a deep learning model, DistilBERT, for cost estimation at the pre-conception stage using basic project information like location and material types. Therefore, the model would contribute to overcoming data limitations for cost estimation at the pre-conception stage. Residential building stakeholders, like clients, designers, and estimators, can use the model to forecast the project budget at the pre-conception stage to facilitate decision-making.

Building Information Modeling (BIM) competitions are a beneficial approach to enhance BIM education, offering students practical experience in BIM application, including mastering workflows and technical tools. However, research exploring the individual perceptions influencing participation intentions and behaviors in BIM competitions is limited. Therefore, this study aims to investigate the factors affecting university students' behavioral intention and behavior in BIM competitions, providing theoretical support for BIM competitions and educational reform.

This study employs the Structural Equation Modeling (SEM) based on the Unified Theory of Acceptance and Use of Technology (UTAUT) framework to analyze the factors influencing BIM competition participation among 970 Architecture, Engineering, and Construction (AEC) university students.

The results of the study show that social influence, attitude, and self-efficacy play critical roles in shaping students' intentions to participate in BIM competitions. Furthermore, self-efficacy, facilitating conditions, and behavioral intention significantly influence students' actual engagement in such competitions. Surprisingly, effort expectancy negatively influences intentions, as less challenging tasks can lead students to perceive their participation as less impactful on their skills and learning, reducing their behavioral intention to participate.

This research provides valuable insights into the effectiveness of BIM competitions in enhancing BIM education for AEC students. Extending the UTAUT model to include self-efficacy and attitude, provides a novel perspective for understanding students' intentions and behaviors regarding BIM competitions. The study’s theoretical support proposes incorporating BIM competitions to augment BIM teaching methods and offers suggestions for advancing the efficacy of students' involvement in BIM competitions within higher education, thus contributing to educational reform in the AEC sector.

Many buildings fail to meet user expectations, causing a performance gap. Pre-occupancy evaluation (PrOE) is believed to have the potential to close the gap. It enables designers to obtain end-user feedback in the design phase and improve the design for better performance. However, PrOE implementation faces challenges due to still maturing knowledgebase. This study aims to understand the state-of-the-art knowledge of PrOE, thereby identifying future research needs to advance the domain.

A systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) framework was conducted. A thorough search in five databases and Google Scholar retrieved 90 articles, with 30 selected for systematic review after eliminating duplicates and irrelevant articles. Bibliometric analyses were performed using VOSviewer and Biblioshiny on the article metadata, and thematic analyses were conducted on their contents.

PrOE is a vehicle for engaging building end-users in the design phase to address the credibility gap caused by the discrepancies between the expected and actual performance of buildings. PrOE has gained limited applications in healthcare, residential, office and educational building design for two broad purposes: design management and marketing. Using virtual reality technologies for PrOE has demonstrated significant benefits. Yet, the PrOE domain needs to mature in multiple perspectives to serve its intended purpose effectively.

This study identifies four knowledge gaps for future research to advance the PrOE domain: (1) developing a holistic PrOE framework, integrating comprehensive performance evaluation criteria, useable at different stages of the design phase and multi-criteria decision algorithms, (2) developing a mixed reality tool, embodying the holistic PrOE framework, (3) formulating a PrOE framework for adaptive reuse of buildings and (4) managing uncertainties in user requirements during the lifecycle in PrOE decisions.

From May 2024, Victoria (Australia) will mandatorily raise the minimum house energy rating standards from 6 to 7 stars. However, the latest data shows that only 5.73% of new Victorian houses were designed beyond 7-star. While previous literature indicates the issue’s link to the compliance behaviour of building practitioners in the design phase, the underlying behavioural determinants are rarely explored. This study thus preliminarily examines building practitioners’ compliance behaviour with 7-star Australian house energy ratings and beyond.

Using a widely-applied method to initially examine an under-explored phenomenon, eight expert interviews were conducted with building practitioners, a state-level industry regulator and a leading national building energy policy researcher. The study triangulated the data with government-led research reports.

The experts indicate that most building practitioners involved in mainstream volume projects do not go for 7 stars, mainly due to perceived compliance costs and reliance on standardized designs. In contrast, those who work on custom projects are more willing to go beyond 7-star mostly due to the moral norms for a low-carbon environment. The experts further agree that four behavioural determinants (attitudes towards compliance, subjective norms, perceived behavioural control and personal norms) co-shape building practitioners’ compliance behaviour. Interventions targeting these behavioural determinants are recommended for achieving 7 stars and beyond.

This study demonstrates the behavioural determinants that influence building practitioners’ compliance decisions, and offers insight regarding how far they will go to meet 7 stars. It can facilitate the transition to 7 stars by informing policymakers of customized interventions to trigger behaviour change.

This study examines the public participation in the implementation of the Historic Urban Landscape (HUL), a United Nations Educational, Scientific and Cultural Organization (UNESCO) approach for urban development. HUL has six Critical Steps and four Tools Categories relating to community engagement, knowledge and planning, regulatory system and financial instruments. The HUL public participation component has materialized variably across the adopting cities, challenged by the local implementation capacity. In response, we have singled out two research questions revolving around the participants’ characteristics and participation methods.

This study instrumentalizes case studies as a research methodology and thematic literature as a theoretical context. The HUL projects for Ballarat, Australia and Cuenca, Ecuador have been selected as sources of published information because they exhibit comparative differences in completing community engagement. We have compared the community engagement accounts of the two cities.

The Ballarat and Cuenca HUL project accounts point out to commonalities and variances in responding to the HUL public participation mandate. The findings for the participants’ characteristics involve project setting, participants categories and participants empowerment; the findings for the participation methods involve initial preparation, domestic and international expertise and public conversation.

The results of the study help define public participation practices in HUL project implementations. The results present an opportunity for city officials, HUL project planners and field administrators for making informed decisions particularly in relation to the two public participation domains, the participants’ characteristics and the engagement methods.

Contemporary technological disruptions are espoused as though they stimulate sustainable growth in the built environment through the Green Internet of Things (G-IoT). Learning from discipline-specific experiences, this paper articulates recent advancements in the knowledge and concepts of G-IoT in relation to the construction and smart city sectors. It provides a scoping review for G-IoT as an overlooked dimension. Attention was paid to modern circularity, cleaner production and sustainability as key benefits of G-IoT adoption in line with the United Nations’ Sustainable Development Goals (UN-SDGs). In addition, this study also investigates the current application and adoption strategies of G-IoT.

This study uses the Preferred Reporting Items for Systematic and Meta-Analyses (PRISMA) review approach. Resources are drawn from Scopus and Web of Science repositories using apt search strings that reflect applications of G-IoT in the built environment in relation to construction management, urban planning, societies and infrastructure. Thematic analysis was used to analyze pertinent themes in the retrieved articles.

G-IoT is an overlooked dimension in construction and smart cities so far. Thirty-three scholarly articles were reviewed from a total of 82 articles retrieved, from which five themes were identified: G-IoT in buildings, computing, sustainability, waste management and tracking and monitoring. Among other applications, findings show that G-IoT is prominent in smart urban services, healthcare, traffic management, green computing, environmental protection, site safety and waste management. Applicable strategies to hasten adoption include raising awareness, financial incentives, dedicated work approaches, G-IoT technologies and purposeful capacity building among stakeholders. The future of G-IoT in construction and smart city research is in smart drones, building information modeling, digital twins, 3D printing, green computing, robotics and policies that incentivize adoption.

This study adds to the normative literature on envisioning potential strategies for adoption and the future of G-IoT in construction and smart cities as an overlooked dimension. No previous study to date has reviewed pertinent literature in this area, intending to investigate the current applications, adoption strategies and future direction of G-IoT in construction and smart cities. Researchers can expand on the current study by exploring the identified G-IoT applications and adoption strategies in detail, and practitioners can develop implementation policies, regulations and guidelines for holistic G-IoT adoption.

Faced with the growing need to find new viable water supply models for urban areas, this article studies and maps the strategies and identifies the key criteria of sustainable development present in pioneering water supply systems in the medieval period. The main aim is to determine which of its innovative principles could be applied in present-day cities.

From a methodological perspective, two types of cases were established, such as water supply models for human consumption and pre-industrial hydraulic systems, all of which are located in Italy. For the first group, the cases of Venice and Siena were analysed, while for the second, in the context of the cities along the Aemilian Way, the case of Bologna was selected.

Five key criteria resulted from the analysis of the cases: exploitation, self-sufficiency, maintenance, rationalisation and reuse. The said concepts were defined and contextualised within the framework of the Sustainable Development Goals.

The Middle Ages were a historic moment in technological reinvention, before the development of modern systems of sanitation. With very limited resources, these traditional systems focused on rational use and deep cultural and geographical knowledge. This is why its recognition is of great importance today, in a time full of instabilities, with a view to the work that needs to be done for the development of more sustainable communities.

This study aims to examine the effect of application architecture and application efficiency on the intention to continue using Islamic bank with data security as a moderator. The investigation was situated within the framework of a hacker attack that compromised the security of customer data at one of Indonesia’s largest Islamic bank.

A survey questionnaire method was used, and the sample population comprised users of Islamic bank in Indonesia. The respondents were then selected purposively with the criteria of individuals who were using mobile banking services. Furthermore, data collection in this study was carried out by distributing questionnaires online. To validate the questionnaire, consultation and validation were conducted by engaging four experts and conducting a pilot study. Hypothesis testing was performed using the structural equation modeling method based on partial least squares.

The results of the partial least square structural model assessment showed that application efficiency and data security positively influenced the intention to continue using Islamic bank, while application architecture had no effect. Furthermore, data security could not moderate the relationship between application architecture and efficiency toward the intention to continue using Islamic bank.

The results of this study suggested that Islamic banking practitioners must prioritize the enhancement of digital banking services, with a specific focus on improving application efficiency and ensuring robust data security. These two dimensions were critical determinants influencing the intention to continue using Islamic bank.

This study addressed the issue of data security as a moderator, particularly in the context of hacker attacks targeting a major Islamic bank in Indonesia. Furthermore, this current report expounded on the study conducted by Mir et al. (2022) by introducing novel dimensions to the e-service quality of internet banking.

The purpose of this study is to examine the buyers’ preferences influencing the purchase of privately developed affordable housing in Kolkata and to determine whether unsold houses result from misalignment with these preferences.

The literature review and user-opinion survey identified 119 independent variables that indicate buyers’ preferences. A questionnaire survey of 383 households in affordable housing units from 32 housing complexes in Kolkata recorded buyers’ preferences and satisfaction against the independent variables grouped under five levels of characteristics. The product weights of variables derived from the rank sum method and percentage satisfaction give the Utility Score. Multivariate regression and univariate linear regressions were conducted to determine the significance of each Level of characteristics and each variable, identifying the significant variables that would affect the sale of affordable houses.

The multivariate regression analysis has indicated that 68.56% of the variation in the percentage of unsold houses was explained by the five utility scores, which affirms that misalignment with buyers’ preferences significantly affects the sale of privately developed affordable houses. Furthermore, building and neighbourhood-level utility show the highest significance as predictors, while city-level and miscellaneous utility have moderate significance, but housing complex-level utility lacks statistical significance.

This study addresses a research gap in privately developed affordable housing in Kolkata, enhancing understanding of buyer preferences in this segment.

Land value in urban areas in India is influenced by regulations, bylaws and the amenities associated with them. Planning interventions play a significant role in enhancing the quality of the neighbourhood, thereby resulting in a change in its value. Land is a distinct commodity due to its fixed location, and planning interventions are also specific to certain locations. Consequently, the factors influencing land value will vary across different areas. While recent literature has explored some determinants of land value individually, conducting a comprehensive study specific to each location would be more beneficial for making informed policy decisions. Therefore, this article aims to examine and identify the critical factors that impact the value of residential land in the National Capital Territory of Delhi, India.

The study employed a combination of semi-structured and structured interview methods to construct a Relative Importance Index (RII) and ascertain the critical determinants affecting residential land value. A sample of 36 experts, comprising property valuers, urban planners and real estate professionals operating within the National Capital Territory of Delhi, India, were selected using snowball sampling techniques. Subsequently, rank correlation and ANOVA methods were employed to evaluate the obtained results.

Location and stage of urban development are the most critical determinants in determining residential land values in the National Capital Territory of Delhi, India. The study identifies a total of 13 critical determinants.

A scenario planning approach can be developed to achieve an equitable distribution of values and land use entropy. A land value assessment model can also be developed to assist professional valuers.

There has been a lack of emphasis on assessing the impact of planning interventions and territorial regulation on land values in the context of Delhi. This study will contribute to policy decision-making by developing a rank list of planning-based determinants of land value.