Purpose This paper presents an advanced deep learning methodology to overcome critical limitations in machine learning and computer vision approaches for heritage building information modelling (HBIM) point cloud classification, including the inability to handle fixed-scale features, misaligned neighbourhoods and semantic overlap inherent in heritage structures. Design/methodology/approach The experimental setup involved training on a diverse, multi-site corpus of synthetic and real-world HBIM sites, including the Maritime Museum, Park Row and Brass Foundry. Input data, characterized by coordinates, normals and colour, were classified using a consolidated 13-class taxonomy. Performance was assessed using metrics including overall accuracy and mean intersection over union (mIoU). Findings Compared to limited training configurations, expanding the training domain from two to five sites significantly enhanced cross-site generalization on the entirely unseen Queen’s House test set. This comprehensive approach yielded a quantitative improvement in mIoU (excluding geometrically ambiguous flat classes) from 26.23% to 59.19%, validating the method’s substantial potential for establishing robust and scalable scan-to-HBIM workflows. Originality/value Our novel approach leverages the state-of-the-art Point Transformer v3 (PTv3) architecture, known for capturing multi-scale geometric details and broad contextual patterns, combined with low-rank adaptation and Point Prompt Training. This integration facilitates resource-efficient domain adaptation necessary for heterogeneous and label-scarce HBIM environments, addressing the synthetic-to-real data gap and class imbalance issues.

Purpose Indoor Environmental Quality (IEQ) directly affects comfort, health, productivity, and well-being, making its assessment essential for healthy indoor environments. The present study is an effort towards assessing IEQ from the occupants' perspective, tailored for institutional settings. It integrates key parameters of IEQ, viz. Thermal, Acoustic, Lighting environments, and Indoor Air Quality (IAQ). The objective is to visualize and formulate quantifiable trends, enabling real-time monitoring and forecasting of IEQ for the overall well-being of occupants. The findings highlight the potential for refinements in existing standards, advocating for adaptive, occupant-centric thresholds. Design/methodology/approach The study focuses on institutional settings in Jaipur's composite climate. A cross-sectional field study was conducted, involving over 580 participants. Subjective satisfaction scores were collected through structured questionnaires, while objective measurements of environmental conditions were recorded using standardized instrumentation. Rigorous statistical analysis, regression modelling, and comparisons with prevalent standards were employed to define satisfaction ranges for comparisons with standards and derive mathematical models for various parameters of IEQ. Findings Thermal satisfaction peaks at 23.8 °C Standard Effective Temperature (SET), with a broader satisfactory range of 19.5 °C–28 °C, exceeding standard limits. Acoustic satisfaction is highest below 40 dBA and remains acceptable up to 61.2 dBA, surpassing established thresholds. Lighting satisfaction is achieved within a broad range of 70–545 lux, indicating robustness of standards. Short-term variations in IAQ had minimal impact, suggesting lower sensitivity of participants to oft-prevailing pollutant levels. Interconnection of thermal and air quality satisfaction was observed. Research limitations/implications The assessment of IAQ was restricted to pollutants such as PM2.5, PM10, and CO2. Incorporating a wider range of pollutants and additional parameters could offer a more comprehensive understanding of IAQ. Furthermore, the analysis of acoustic and lighting environments could be enriched by considering factors such as sound frequency distribution and glare metrics, providing a more nuanced evaluation of occupant satisfaction. Additionally, the study did not account for local thermal dissatisfaction, which could offer deeper insights into occupant thermal satisfaction and comfort. Practical implications The findings advocate for data-driven, occupant-centric assessment of IEQ that emphasizes subjective satisfaction rather than rigid standards, while promoting energy efficiency. Institutional facilities, where immediate infrastructural change is not feasible, can benefit from flexible operating thresholds without compromising satisfaction. Originality/value This study is an effort at developing an occupant-centred framework for IEQ assessment in institutional buildings, bridging the gap between technical benchmarks and human experience. While IEQ assessments typically follow established standards, fixed benchmarks may not always reflect occupant satisfaction due to variations in human perception and adaptive responses. Aligning assessments with user satisfaction has the potential to refine benchmarks, enabling flexible thresholds that balance comfort, practicality, and cost-effectiveness as explored in this paper.

Purpose Stepwise retrofitting offers a phased, flexible approach to improve building energy performance and overcome the financial and logistical barriers of single-step retrofitting. However, existing research relies on theoretical models and simulations, often overlooking real-world market conditions. This study investigates the practical applicability of stepwise retrofitting in the Australian residential sector, drawing on insights from experts. Design/methodology/approach An exploratory qualitative research design was employed. The data were collected through 11 expert interviews with industry, policy, and academia, followed by triangulation with a policy analysis. Thematic analysis and directed content analysis were conducted to analyse insights under three core elements of stepwise retrofitting: timing, packaging, and sequencing. Findings Experts recommend mandated 10-year retrofitting cycles with retrofit steps spaced every three years. This study proposes, for the first time, a hybrid retrofit package approach that combines standardised measures (e.g. insulation, glazing) and customised audits for complex systems (e.g. HVAC). A structured three-step retrofit sequence is recommended: (1) basic envelope improvements, (2) targeted upgrades, and (3) advanced technology integration. Research limitations/implications Since the study is confined to expert interviews, future studies should focus on conducting longitudinal case studies once the stepwise retrofitting is industrialised. Originality/value The study establishes a foundational guideline for timing, packaging, and sequencing (see Table 4) and contextualises the guideline within the existing Australian policy landscape (see Table 5). This study presents a novel sequencing approach for stepwise retrofitting and introduces a first-of-its-kind application framework for stepwise retrofitting, providing a skeleton for smart home researchers to build upon (see Figure 4).

Purpose This study examines fire safety design in hospitals, aiming to identify and understand the factors that influence its effectiveness through control and evacuation while addressing challenges related to patient mobility. Design/methodology/approach The study employs a qualitative narrative approach, systematically reviewing 130 relevant articles and research papers. Conducting a systematic literature review identified 10 key factors of safe egress in hospitals that affect patient evacuation. Then, through semi-structured interviews, 25 field experts confirmed those findings and added two more key factors. Findings This study found patient mobility rate and conditions as the most crucial key factor influencing safe egress apart from the other 12 factors which are occupant types, occupant behaviour, exit characteristics, interior layout, fire spread and fire cells, building fire-rated materials, required safe egress time/available safe egress time, fire detection and suppression systems, signs and evacuation elevators, building model simulation and staff training. Research limitations/implications This study’s limitations include the non-inclusion of the most recent fire incident reports due to their unavailability to the public. Additionally, reliance on secondary data sources in some areas introduces the potential for inaccuracies. The scope of the literature review was also limited by language (English only). The robustness of the methodology followed mitigates the limitations of the study. Practical implications This study assists field professionals – including architects, fire engineers, consultants, medical staff and firefighters – as well as the general public, by providing critical design information to ensure the safe evacuation of all hospital occupants, particularly patients, thus participating in achieving the United Nation’s goals of creating safe environments and fostering sustainable, resilient infrastructure while ensuring the safety of people of all ages. Moreover, the new factor of patient mobility conditions is an addition to the body of knowledge that would interest academics. Originality/value The novelty of the study identified patient mobility conditions as the key influencer for safe fire egress at hospitals while discussing the interrelations between the 12 critical factors.

Purpose Despite growing interest in digital transformation, the construction industry continues to face challenges in the consistent adoption of cloud-based construction management platforms (CCMPs). Existing literature often overlooks the combined influence of individual behavioural perceptions and organizational–environmental readiness. This study addresses this gap by integrating the technology acceptance model (TAM) and the technology-organization-environment (TOE) framework to develop a holistic model of CCMP adoption. Design/methodology/approach Partial least squares structural equation modelling (PLS-SEM) was applied to data collected from 256 construction professionals to examine the factors shaping CCMP adoption readiness. Findings The findings indicate that staff digital competence, cybersecurity confidence and platform interoperability significantly influence implementation readiness, mediated by perceived decision-making support and user learning curve. Organizational size was not found to moderate these effects. This study is original in developing and empirically validating an integrated TAM–TOE framework specifically tailored for CCMP, an area that has received limited theoretical and empirical attention. By bridging behavioural and contextual perspectives, the research provides a novel multi-level understanding of digital readiness in construction, offering actionable guidance for firms, platform developers, and policymakers to accelerate digital transformation. Originality/value This study contributes an integrated TAM–TOE framework that captures both individual and organizational–environmental perspectives, offering actionable insights to guide strategic CCMP implementation in digitally evolving construction environments.

Purpose In today’s complex and rapidly evolving project environments, leadership competencies are increasingly recognised as a critical factor in achieving project success. This paper explores the core leadership competencies project managers need and examines the enablers that support as well as the barriers that limit, their effective application. Design/methodology/approach Following PRISMA 2020, 43 peer-reviewed studies published between 2005 and 2025 were identified through systematic searches of leading academic databases. Studies were assessed for relevance, quality and alignment with project leadership and success criteria. Findings The findings show that emotional intelligence, communication skills, strategic thinking, adaptability and empowerment consistently contribute to project success across sectors. These competencies enhance collaboration, stakeholder engagement and decision-making in uncertain contexts. Enablers such as supportive organisational cultures, effective communication and empowering leadership behaviours strengthen their application, while barriers including an overemphasis on technical skills, rigid cultures and limited use of competency-based frameworks continue to undermine effectiveness. Research limitations/implications The results emphasise the importance of a holistic approach to project leadership that develops behavioural, emotional and strategic capabilities alongside technical expertise. They also underline the need for deeper investigation into how leadership competencies shape success across diverse project types and contexts. Practical implications Organisations are encouraged to institutionalize leadership development practices and adopt adaptive frameworks to enhance leadership impact in real-world project environments. Originality/value Insights from 43 practical studies are consolidated, offering a comprehensive understanding of both enablers and barriers of project leadership competencies. The study provides actionable directions for organisations and future research on how leadership competencies drive project success.

Purpose This study identifies and validates opportunities for automation of problematic construction contract administrative tasks and processes. Through the evaluation of identified automation opportunities, system features are proposed and prioritised to form a development roadmap for future intelligent contract (iContract) creation and evolution. Design/methodology/approach This study applies a qualitative approach to draw on experienced construction practitioners with direct knowledge of contract administration practices. Thematic mapping and co-occurrence analysis of interview data identify “Contract Process Automation Opportunities” (CPAOs) which are then evaluated and prioritised to inform a development roadmap. Findings The study establishes ten evaluation criteria, specific to contract processes and identifies eight novel CPAOs. Ten iContract system features, along with the technological and environment requirements to facilitate development, are then synthesised into the novel iContract development roadmap. Research limitations/implications An “iContract system requirements identification model” is developed by adapting established process automation theoretical frameworks. This guided the structured selection of suitable automatable contractual processes, based on both theoretical and practical insights. The roadmap offers a practical guide for iContract developers for an initial artefact and future researchers aiming to overcome the evolutionary challenges highlighted. Practical implications The roadmap offers a practical guide for iContract developers for an initial artefact and future researchers aiming to overcome the evolutionary challenges highlighted. Originality/value This study contributes a unique and founding iContract system development roadmap, in an embryonic field, that has been borne and validated by industry practitioners. It identifies the initial functions to successfully develop an iContract artefact and highlights the evolution of the concept towards an autonomous solution.

Purpose This paper concerns an integrated life cycle assessment (ILCA) comparing daylighting retrofit solutions for an existing sports hall, currently relying only on electric lighting. Daylight quality, environmental impact and financial aspects are generally independently considered, leading to suboptimal decisions when all aspects are considered together. By taking the sports hall as an example, the article demonstrates how an ILCA can support the decision-making process. Design/methodology/approach The actual space is investigated via field measurements. These are utilized for calibrating simulation models. Daylight and circadian lighting simulations are carried out in Climate Studio and Alfa software. The simulations inform the final design for three potential daylighting retrofit solutions based on tubular daylight devices, skylights and windows. The solutions are analyzed and weighted based on daylight and circadian lighting outcomes, Life Cycle Assessment (LCA), Life Cycle Cost (LCC), and, finally, from an ILCA perspective. Findings No daylighting renovation outperformed other solutions in terms of quality, environmental and financial aspects. All renovations proved more profitable than no action. ILCA demonstrated the importance of balancing and optimizing all criteria together. The study highlights the need to incorporate daylighting design early and use ILCA approaches for informed, profitable renovation decisions. Originality/value While studies have been conducted to evaluate either the lighting quality or the environmental impact of daylighting retrofits, little has been done considering both aspects together, including also a financial perspective. There is a strong need for a more interdisciplinary approach to guide informed and optimal retrofit decisions.

Purpose As cities continue to grow rapidly, urban open spaces are increasingly recognized for their contribution to sustainability, well-being and environmental quality. Yet, they remain underexplored compared to built environments, particularly regarding spatial metrics such as sky view factor (SVF) and visibility, which influence perceived thermal comfort, safety and openness. These metrics are fully morphology-based and ideal for early-stage planning, but current tools often lack support for localized, interpretable optimization. This study focuses on green spaces such as parks and introduces an AI-driven framework for optimizing SVF and visibility, to enhance the usability and spatial performance of these spaces. Design/methodology/approach The framework combines Machine Learning Models (MLMs), explainable AI techniques such as SHapley Adaptive Explanations (SHAP), and Counterfactual Explanations (CFXs) to enable localized, interpretable, and efficient optimization. A dataset of 1,152 simulated urban block configurations based on Tehran's morphological patterns was used to train and test five predictive models. SHAP was used to assess the contribution of each feature, while CFXs proposed minimal design interventions to optimize spatial performance. Findings The results show that park area and building height on the east and west sides are critical factors for SVF, while southern building distance and building width most strongly affect visibility. XGBoost achieved an R2 of 0.91 for SVF prediction and F1-scores up to 0.89 for visibility classification. The CFX approach produced optimized results in approximately 1 min with an average RMSE of 5%, whereas genetic algorithms required 15–30 min to converge. This represents a ∼90% reduction in computation time. Originality/value Unlike global optimization methods, which are computationally intensive and impractical for localized adjustments, this framework supports incremental design improvements with lower computational costs and greater flexibility. This study offers a novel application of CFXs for spatial metric optimization in urban open space design, bridging a gap in current literature. The integration of explainable AI not only improves transparency but also supports real-time decision-making in sustainable urban design.