ABSTRACT As traditional roles within the design-bid-build delivery system have changed over decades, literature has presented the effects on perceptions of the architectural profession from the standpoint of major stakeholders. This narrative literature review explores these changes through descriptive analysis that identifies, compares, and contrasts the priorities of facility owners, architects, and GCs as related to design-bid-build delivery system projects within the United States. In addition, the perspectives of United States academic instructors teaching architectural design are explored to show how instructors, who hold significant sway for shaping the ideologies of future architects, view the responsibilities of the architectural profession. Authors identify two contexts through which priorities of stakeholder groups are discussed: those pertaining to moral purpose for practice behavior, and those pertaining to standard practice.

ABSTRACT Interior decoration is a major source of embodied carbon emissions, highlighting the need for reliable low-carbon decision-making tools. However, the influence of preference uncertainty on ranking stability is seldom addressed in existing low-carbon Multi-Criteria Decision-Making (MCDM) studies. To overcome this limitation, this study proposes an integrated probabilistic decision-making framework that combines cradle-to-site Life Cycle Assessment (LCA), TOPSIS-based multi-criteria evaluation, and Dirichlet-driven Monte Carlo simulation to explicitly quantify weight uncertainty and ranking robustness. The study evaluated a real university Academic Exchange Center interior decoration project (GFA ≈ 4,200 m²) in Chongqing with three alternative schemes: high-end, medium-end, and basic. Results show that material production accounts for over 90% of cradle-to-site embodied carbon emissions across all schemes, indicating material substitution as the most effective emission reduction pathway. Although deterministic TOPSIS initially identified the medium-end scheme as optimal, 1600 Monte Carlo iterations revealed that the high-end scheme achieves the highest median closeness degree (0.5372) and the greatest probability of ranking first (54.94%), demonstrating it as the most robust optimal solution under preference uncertainty. The proposed framework advances low-carbon design methodology by shifting from deterministic ranking to probability- and robustness-based decision analysis, providing more compelling guidance for multi-stakeholder, preference-sensitive interior design practice.

ABSTRACT To address the imperative of decarbonization in the building industry, this study overcomes the limitations of conventional static Life Cycle Assessment, which overlooks the cumulative carbon impact of dynamic retrofits. Based on Shearing Layers theory, we propose a four-layer Stratified Framework encompassing Skeleton, Space & Partition, Equipment, and Interaction. This study establishes a matrix-based model to quantify emissions across the material production, construction, and operational stages. The Modularity Coefficient is introduced as a key parameter to characterize generalized construction carbon emissions, while the structural robustness of the model is systematically evaluated. Findings reveal that in the material production stage, the Space & Partition Layer contributes significantly due to its substantial physical volume and periodic replacement cycles. In the construction stage, associated losses caused by construction methods constitute the core variable of construction carbon emissions. During the operational stage, the Interaction Layer facilitates a cross-layer synergistic emission reduction pathway via intelligent optimization. This systematic methodology provides a theoretical foundation for life-cycle carbon accounting and provides strategic support for optimizing low-carbon retrofitting and sustainable renewal technologies for existing building stocks.

ABSTRACT Automated rule checking aims to automate design checking via computer-processible rules and has been extensively studied for years. Existing methods for rule interpretation are often limited in their ability to represent implicit complex computational logic, and the integration between complex logic representation methods and rule interpretation outcomes remains insufficient. In this paper, we propose a graph-based method for representing knowledge rules for automated design checking. The graph representing procedure consists of two main steps. Firstly, semantic mapping is employed, whereby the rules are represented as a semantic combination of three corresponding graph branches. Secondly, atomic function mapping is utilized, which adds atomic function nodes to the graph from the level of computable logic to form a complete graph representation, thereby forming a complete graph representation. We then provide a method for automatically generating programming code from the graphs, which, combined with the development of atomic functions, can be used for checking. This approach demonstrates considerable potential in terms of its interpretability and comprehensibility, and it provides novel ideas for the development of fully automated rule checking systems.

ABSTRACT The evolving landscape of architectural practice increasingly calls for a comprehensive understanding of professional responsibility. Despite its significance, the conceptual boundaries of these responsibilities remain fragmented across various regulatory and ethical frameworks. This study adopts a qualitative approach, applying inductive textual content analysis to organize and systematize the professional responsibilities of architects. Fifteen authoritative documents from eight countries and one supranational institution were examined through a three-stage coding process. The analysis identified sixteen principal categories, which were subsequently organized into two overarching domains: Responsibilities toward Individuals and Institutions and Responsibilities toward Principles and Values. The findings indicate that ethical integrity, legal compliance, and responsibilities toward clients constitute the most consistent and widely recognized pillars, forming the universal foundation of architectural accountability. Although contemporary codes have begun to integrate Sustainability and Social Justice more explicitly, dimensions such as aesthetics, culture, economics, and project quality remain relatively marginalized in official discourse. This imbalance reflects a structural preference for legal and behavioral standards that are readily enforceable rather than for qualitative or value-based criteria. The comparison reveals an evolutionary shift from narrow, technically driven frameworks in earlier documents toward a more holistic model of socio-environmental accountability in recent standards. The research further identifies a competency-value gap within architectural governance. The proposed multilayered framework offers a strategic foundation for revising professional codes, particularly in developing contexts such as Iran, in order to achieve a more balanced alignment between fundamental obligations and the presently underrepresented qualitative and cultural dimensions of the profession.

ABSTRACT Architectural management is a relatively underexplored discipline in Croatia, where it is often confused with design management and insufficiently integrated into professional practice and continuing professional development (CPD) frameworks. This study investigates the state of architectural management among Croatian architectural firms through an online survey of members of the Croatian Chamber of Architects (CCA), comprising 216 respondents, of whom 142 employers form the analytically relevant sample. The survey examined firm demographics, use of professional standards, fee-determination practices, management skill self-assessment and perceived challenges. Results reveal that 89.4% of surveyed firms are micro-enterprises with fewer than five employees, underscoring the dominance of SMEs in the sector. Most architects rely on simplified fee-determination methods – such as cost per square metre or percentages of construction costs – rather than structured scope-of-services standards, while over 70% identify low fees and unclear regulation as key challenges. Although respondents report relatively high self-assessed management skills, correlation analysis shows that demand for management education is not suppressed by confidence in existing abilities, suggesting that the underdevelopment of architectural management is structural rather than individual. This study provides the first systematic, survey-based assessment of architectural management in Croatia and positions its findings within broader European debates on professional fragmentation, fee formation, and firm resilience. While recent CCA reforms represent progress, the results highlight the need for deeper integration of management principles into professional standards and CPD frameworks to support the long-term sustainability of architectural practice.

ABSTRACT Against the backdrop of global energy conservation and environmental protection, the importance of applying ultra-low-energy building technologies and evaluating their cost–benefit has become increasingly prominent. However, existing studies in evaluating the cost–benefit of these technologies are mostly limited to a single dimension. They overlook the interactions among economic, social, and environmental benefits. This study aims to effectively screen out technical combination schemes with better cost–benefit by sorting out the internal relationships among various factors of the cost–benefit of ultra-low-energy building technologies. To this end, this study constructs a life cycle-system dynamics analysis framework to comprehensively analyze the constituent elements and interaction mechanisms of incremental costs, economic benefits, as well as environmental and social benefits. Finally, a typical public building in Xinxiang City, China, is selected as a case study. Eight common ultra-low-energy building technical scenarios are set up, and a 20-year cost–benefit simulation is carried out. The research results show that among the eight proposed schemes, when the building envelope structure adopts the combination of an exterior wall (70 mm graphite polystyrene board) + roof (80 mm extruded polystyrene board) + exterior window (65 Series Vinyl Windows) + floor (70 mm extruded polystyrene board) and with a PV area of 150 m² reaches the optimal level of 8,748,300 yuan. This study provides a multi-dimensional evaluation model and a reference basis for optimizing the design of ultra-low-energy buildings, and effectively promotes the realization of the sustainable development goals of the construction industry.

ABSTRACT The urgent need to reduce emissions from existing buildings has positioned carbon retrofitting as a critical strategy for advancing sustainable construction in Nigeria. However, empirical understanding of the factors influencing adoption among built-environment professionals remains limited. Measurement items were adapted from validated carbon retrofit and sustainability adoption instruments and contextualized to Nigeria through expert practitioner review. Using a structured questionnaire within a positivist research paradigm, data were collected from built-environment professionals and analyzed using PLS-SEM to examine five adoption drivers: regulatory pressure (RP), economic and financial feasibility (EFF), organizational capability (OC), technological readiness (TR), and stakeholder collaboration (SC). Results indicate that RP (β = 0.429), EFF (β = 0.392), and OC (β = 0.374) are the strongest predictors, confirming an institutional-financial-organizational adoption logic in which regulatory mandates, retrofit cost considerations and firm execution capacity shape decisions toward carbon-sensitive upgrades. Technological readiness shows a positive influence, while stakeholder collaboration is only marginally significant, reflecting coordination challenges within fragmented retrofit ecosystems. Moderate practitioner knowledge reveals gaps in carbon assessment literacy, lifecycle retrofit cost modeling, low-carbon material specification, and integration of emerging retrofit technologies, which may constrain adoption. The findings highlight the importance of targeted training, certification-linked upskilling and investment in organizational capacity. The validated framework provides actionable guidance for policy incentive design, retrofit financing prioritization, and capability development, while advancing carbon retrofit adoption theory in LMIC construction contexts and demonstrating the value of PLS-SEM for modeling complex sustainability adoption dynamics. Highlights Empirically identifies five core drivers shaping carbon retrofit adoption in Nigeria, including regulatory pressure, economic feasibility, organizational capability, technological readiness and stakeholder collaboration. Applies PLS-SEM to model complex interrelationships among adoption drivers, providing robust predictive insights for retrofit decision-making. Reveals that regulatory pressure, economic feasibility and organizational capability are the strongest predictors of carbon retrofit adoption among built-environment professionals. Presents a validated, practitioner-informed framework to guide policymakers, construction firms and project managers in implementing carbon-reduction strategies. Highlights ongoing capacity gaps among practitioners and emphasizes the need for targeted training, mentorship and knowledge-sharing initiatives to enhance adoption.

ABSTRACT Building structural designs, utilizing materials such as steel, concrete, and cross-laminated timber, contribute significantly to embodied carbon emissions in construction projects. However, traditional carbon accounting methods employed to quantify and record these emissions are often characterized by a lack of traceability, transparency, and immutability. This limitation undermines the reliability of emissions data, making it challenging for stakeholders to establish credible emissions records and implement regulatory strategies, such as carbon credits, taxes, subsidies, and green certifications, for building’s structural designs and materials. This paper addresses these challenges by proposing a transformational emissions accounting system that integrates Building Information Modeling (BIM) for automatic extraction of emissions-relevant data, alongside blockchain-enabled smart contracts to ensure traceability and immutability of emissions records. The proposed system enables data-driven decision-making for low-carbon structural designs and materials, while also facilitating the application of emissions regulations to support their implementation based on trustworthy emissions accounting.

ABSTRACT The implementation of artificial intelligence (AI) and machine learning (ML) in the architecture, engineering, and construction (AEC) domain has gained significant attention within academic research and construction technology companies. The scientific field often relies on information collected from the industry for theoretical development and sometimes test cases. However, to the best of the authors’ knowledge, there has been no systematic effort to compare academic and industry trends and developments. In this work, we examine the development and application of ML in the AEC domain from both academic and industry perspectives. To investigate both perspectives, we implement a mixed-methods approach including an academic literature review, a web-based mapping of construction technology companies worldwide, and an online questionnaire targeting practitioners and technology companies. Overall, our mixed methods analysis reveals a strong alignment between academic research and industry practice in targeting early design and post construction phases, with both communities prioritizing the application of ML for energy efficiency, facility management, and site safety, particularly within the context of Building Information Modeling (BIM). However, industry devotes more effort to sustainability, cost and scheduling solutions, whereas academia lags in these areas and often relies on limited or synthetic datasets. Finally, both sectors identify data availability and quality, particularly the scarcity of large, labeled, domain-specific repositories, as the primary barrier to wider ML adoption in the AEC industry.