Proceedings of Smart and Sustainable Built Environment Conference Series
Geospatial Assessment of Deglaciation and Climate Change Impacts in Chandra Valley, Western Himalaya (1989–2017)
Glacier retreat in the Himalaya is a visible indicator of climate change with implications for water security, hazards, and ecosystems. We present a geospatial assessment of glacier change in the Chandra Valley, Western Himalaya (India) from 1989–2017 using multi-temporal Landsat 5 TM / Landsat 8 OLI imagery supported by ALOS PALSAR DEM. A hybrid workflow (supervised classification with visual interpretation and terrain constraints) delineated five elements—accumulation area (AA), ice-exposed ablation (IEA), debris-covered ablation (DCA), deglaciated valley (DV), and glacial lakes (GL)—which were compared across epochs and intervals. Total glacier area declined from 726.36 km² (1989) to 614.45 km² (2017) (−111.91 km²; ≈15%). Over the same period, AA decreased by 74.75 km², IEA by 23.58 km², and DCA by 13.59 km²; DV expanded by 29%, and GL increased by 51%, indicating enhanced downwasting, forefield exposure, and lake growth. These results evidence accelerating cryospheric change with cascading impacts on downstream water resources, hydropower reliability, and community resilience. By quantifying long-term, feature-resolved changes and interval contrasts, this study supports climate-adaptation planning and underscores the need for sustained monitoring and integrated policies for water management and disaster preparedness in debris-rich Himalayan catchments.
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The Culture of Practice: How the Organisational Culture of Architectural Firms Influences Sustainable Urban Development in Lagos, Nigeria
The global discourse on sustainable urban development often presumes a uniform context for professional architectural practice. However, this paper argues that the local organisational culture of architectural firms acts as a critical, yet overlooked, barrier to the widespread adoption of sustainable design. Through a qualitative, in-depth case study of eleven architectural firms in Lagos, Nigeria, this research identifies the key cultural dynamics that hinder sustainability efforts. The findings reveal two significant systemic barriers: the primacy of a ‘Client-Driven Context,’ where client demands for lower costs and faster delivery consistently override sustainability goals; and a ‘Validation Vacuum,’ an absence of local awards or professional recognition for sustainable design, which reinforces the focus on client satisfaction over innovation. This paper argues that these dynamics create a significant ‘culture-policy gap,’ where well-intentioned sustainability policies are likely to fail because they do not account for the on-the-ground realities of professional practice. By providing a nuanced understanding of these cultural forces, this research offers crucial insights for policymakers and professional bodies, concluding that achieving a sustainable built environment requires targeted, culturally sensitive interventions that address the profession itself.
Global comparative study of cities through CHOAMS, a nonlinear urban design framework that can support better urban investment schemes while avoiding urban fragmentation.
At SASBE 2024, we presented a nonlinear framework to study urban environments and help practitioners reliably generate synergistic value through their urban design projects. Our work was based on a model trained for Wellington, NZ, using a dataset of ca. 200,000 data points. Here we expand our study to compare 10 cities in New Zealand, Europe, and the U.S.A. with models trained on a dataset of several million entries. This comparative research demonstrates general rules that hold across continents, as well as idiosyncrasies in the functioning of urban environments. For example, urban diversity and access to a diverse supply of urban activities typically create added value. At the same time, different cities have different modes of life, which can be quite unique, reflecting different synergies and antagonism between activities in each city. Studying mobility, we find that different mobility options are mostly beneficial, facilitating different types of access to a diverse supply of activities, which mostly creates value, although the exact parameters differ from city to city, in this case as well. These insights help architects and planners conceptualize cities and, with modeling support, make decisions for future urban designs.
Optimizing Supply Chains for Cultural Heritage Renovation: An Operational Perspective
This study presents an integrated framework for managing cultural heritage (CH) renovation projects by aligning supply chain operations with project management methodologies. Recognizing the unique challenges posed by heritage restoration, such as limited material suppliers, specialized labor, and regulatory constraints, the paper emphasizes the need for synchronized supply chain planning, procurement, and execution. Drawing on existing literature, the study outlines a supply chain operational model rooted in institutional oversight and resourcefulness, encompassing structural, energy-efficiency, and ornamental interventions. A case study of Riga’s Central Market pavilions illustrates the framework’s application, focusing on three restoration works: fiber Bragg grating sensor installation, concrete overlay strengthening, and corrosion monitoring system deployment. Using Critical Path Method (CPM) simulations, the study identifies critical tasks and bottlenecks, highlighting how delays in material delivery, equipment availability, and skilled labor can impact project timelines. The analysis reveals that activities such as rebar replacement, overlay casting, and sensor embedment are highly sensitive to supply chain disruptions. The study also maps resource flows (materials, equipment, tools, and labor) underscoring the importance of coordination among stakeholders. Findings suggest that integrating supply chain dynamics into project scheduling enhances responsiveness and reduces hidden delays. The study contributes a novel operational framework that integrates supply chain dynamics with project scheduling, offers empirical evidence of supply chain impacts on restoration timelines, and identifies resource-sensitive activities that influence project duration. It also provides practical guidance for aligning procurement, logistics, and workforce planning with restoration sequencing.
cultural heritage; supply chain; operations; critical path method; project manage-ment; renovation; institutional oversight; resourcefulness;
Towards intermodal resilience: a critical review of sensor-enabled synergies between MASS and CAVs
In the context of the ecological and digital transition of port cities, the convergence between autonomous surface ships (MASS) and connected and autonomous vehicles (CAV) represents a promising, yet largely unexplored opportunity for the creation of resilient, low-impact intermodal transport. This study reviews the literature on sensor technologies and data exchange systems currently used in smart ports—such as the OnTrack and Just-in-Time Shipping projects in Rotterdam—and analyzes their potential interoperability with autonomous mobility strategies. The OnTrack project at the port of Rotterdam, for example, was developed to optimize rail traffic management between the port and the hinterland. Thanks to this platform, operators can consult real-time information on train arrival and departure times, location, container loading/unloading status, and terminal operations progress. At the same time, the Just-in-Time Shipping project aims to reduce ship emissions and waiting times through dynamic arrival time optimization (ATA), based on geofencing and real-time digital communications with the port. Through the analysis of selected case studies, it is possible to explore how the port-city interface can be rethought thanks to distributed networks of sensors that monitor both the marine and urban environments. The review highlights how shared environmental sensors and predictive logistics made possible by BIM, IoT, and digital twin systems, could facilitate dynamic coordination between autonomous land and sea agents, improving adaptive planning and promoting sustainable mobility within complex port ecosystems.
EMBODIED MANAGEMENT DRIVERS (EMDs) AS CATALYSTS FOR SMART SUSTAINABILITY IN THE BUILT ENVIORNMENT
Purpose: The UK Construction sector faces urgent challenges in reducing greenhouse gas emissions to meet the national Net Zero Targets by 2050. Currently, construction activities contribute approximately 39% of UK total carbon emissions, with embodied carbon forming the majority share. This study aims to identify and evaluate critical Embodied Carbon Management Drivers (EMDs) that can enhance sustainability in smart building construction, with a focus on the early design stage and planning. Design/methodology/approach:A Comprehensive systematic literature review was conducted, analysing selected peer-reviewed and industry sources to identify potential EMDs. Extracted data was categorised into environmental, economic and social factors. The analysis focused on identifying gaps in practical situations of EMDs during early design and planning stages of construction projects. Findings: Thirteen key EMDs were identified: environmental (6), economic (3) and social (4). These include low-carbon design strategies, adoption of eco-friendly materials, targeting higher EPC ratings, minimising maintenance needs, integrating social responsibility among stakeholders and applying recognised best practices. The findings emphasis that early co-operation of EMDs can significantly reduce embodied carbon and accelerate the sector’s shift toward a low-carbon culture. Originality/value: The study is based on literature-derived data, which may not capture all real-world variables in project execution. Future research could validate these findings through empirical case studies and pilot projects.
Embodied Carbon Emissions, Greenhouse Gas Emissions, Net Zero Targets, Low Carbon
A Smart Multifunctional Modular Facade Panel for Building Renovation: A Comparative Assessment across European Climates of Energy Efficiency and Thermal Comfort
This study presents a comprehensive comparative assessment of a multifunctional modular facade panel for building renovation, integrating HVAC (Heating, Ventilation, and Air Conditioning) and photovoltaic (PV) systems. Three panel versions are analyzed: a conventional metal-framed version, a slim version with reduced thickness, and a timber framed version featuring bio-based materials. In addition to conventional thermostatic HVAC control, a thermal comfort control approach based on the Predicted Mean Vote (PMV) index is evaluated. Various wall panel configurations are explored by altering key components, including the insulation layer, the PV area (BIPV), and HVAC control strategies. These are virtually tested through scenario analysis on a case study building with different pre-renovation envelope thermal conditions. Four representative European climate clusters—Mediterranean, Oceanic, Continental and Nordic—are selected to investigate optimal renovation strategies in terms of energy performance and thermal comfort. Subsequently, building-level simulations are conducted using TRNSYS software to evaluate the performance of modular panels on a typical multifamily residence envelope with varying external wall U-values under different climatic conditions. A parametric study of 480 cases highlights key trade-offs between insulation, PV integration, and HVAC control methods. Notably, PMV-based control may occasionally lead to improved energy efficiency and occupant comfort compared to conventional thermostatic control. Overall, this study underscores the potential of multifunctional modular facade panels to enhance building performance in diverse European climates.
Integrated HVAC Systems, PMV-controlled HVAC, Modular Facade Panel, Building Renovation, Energy Efficiency, Thermal Comfort
AI in ANZ Construction Job Posts: A Detailed Five-Year Analysis (2021-2025)
The construction industry faces an urgent imperative to adopt Artificial Intelligence (AI) and Machine Learning (ML) capabilities to remain competitive and address persistent productivity challenges, yet empirical evidence of this workforce transformation has remained elusive. This research provides the first systematic analysis of AI skills integration in the Australia and New Zealand (ANZ) construction sector through comprehensive examination of job advertisement data from 2021-2025. Analysis of 304 AI/ML-specific construction job postings reveals explosive growth, with demand accelerating at a remarkable 67.3% compound annual growth rate—increasing nearly five-fold from 25 postings in 2021 to 117 in 2024, including a dramatic 54% surge in the final year alone. The findings expose critical workforce transformation patterns: Python programming dominates technical requirements (comprising over 25% of all AI-related skills), while Construction Project Management represents 40% of domain expertise demands, indicating integration of AI capabilities within traditional construction frameworks rather than replacement. Geographic analysis reveals striking concentration, with Sydney and Melbourne capturing over half of all opportunities, yet Regional Queensland unexpectedly outperforms major capital cities, challenging conventional urban-centric adoption assumptions. Job function analysis demonstrates that AI integration spans from Administration Entry-Level positions (42 postings) to specialized Engineering roles across multiple seniority levels, signaling comprehensive workforce restructuring rather than niche specialization. These unprecedented insights provide construction education providers, industry leaders, and policymakers with essential empirical foundations for urgent workforce development strategies, revealing that AI adoption in construction has moved beyond theoretical potential to become a rapidly expanding employment reality requiring immediate strategic response.
Artificial intelligence, Construction industry, Workforce transformation, Digital skills, Machine learning integration, Education
Towards Socially Sound Sustainable Building Projects with a Novel Life Cycle Assessment Method
In architectural practice, social values are created through design choices made by architects. Integrating social intents, along with their corresponding design choices, into a design project has significant implications for the environmental impact of the building. This fac-tor is often inadequately addressed. This paper presents an initial study towards adopting a novel environmental life cycle assessment (eLCA) approach for assessing the environmental impact of social intents in building projects. The study focuses on exploring eLCA’s goal and scope setting through a methodological triangulation approach where the goal and scope are explored through various sources as follows: (a) a literature re-view of existing approaches within the building and construction industry, (b) narrative interviews and analysis with practicing architects, and finally (c) through a practical investigation carried out on social design intents from actual building projects. The preliminary findings indicate that incorporating social intentions into building projects yields significantly varying effects on the emissions of the building, contingent upon the specific design choices that facilitate these social intents. Furthermore, the findings underscore the necessity for additional research to reconcile the functional unit with the established units from other eLCA methodologies. Such an alignment is crucial for enabling a comprehensive evaluation of the impact magnitude of social intents within the entire building context. This work contributes to bridging the existing gap in the assessment of quantifiable environmental impacts versus qualitative intention-based social impacts, paving the way for more informed decision-making and socially sound, sustainable buildings.
Advancing Sustainable Healthcare Facilities. Assessing the performance of Electrochromic Windows through Energy and Comfort KPIs. A case study
The current study, examines the case of a small-scale renovation of a hospital clinic, involving the replacement of the existing windows with advanced Smart Insulated Glass Units (IGUs), specifically Electrochromic (EC) glazing systems. A comparative assessment of the past and post-renovation condition is carried out in order to highlight the benefits of this intervention. This analysis is based on selected essential Key Performance Indicators (KPIs) addressing the needs of multiple stakeholder groups, with particular emphasis on patients and medical staff. In parallel, the study aligns with overall sustainability objectives by assessing the contribution of EC windows to energy savings, to optimize the renovation project. A comparison of the performance over the entire year was performed, using the EnergyPlus software, and the results showed a reduction of the energy heating, cooling and lighting demands for the post installation state by ca. 10%. The simulation analysis of the indoor comfort KPIs, showed also an improvement in the thermal comfort conditions, due to the operation of the electrochromic windows that lead to a reduction of radiant temperature inside the patient rooms. After the renovation, healthcare workers reported improved satisfaction with thermal comfort conditions, while they noted that interior shading was no longer necessary, due to reduced glare. This change also improved patients’ psychological well-being and recovery, as access to natural light and continuous visual connection with the outside environment were maintained throughout the day.
Enhancing Concrete Bridge Inspections with Preventive Maintenance and Data Fusion Techniques for User Safety
As a critical link between the West Coast and East Coast of Peninsular Malaysia, the Banding Bridge supports thousands of vehicles daily. Persistent structural issues such as cracks and corrosion pose significant risks, endangering lives and disrupting traffic. Urgent advanced inspection and maintenance are necessary to ensure user safety. This study proposes a concrete bridge inspection system utilizing a fusion of GNSS and vibration data to enable the Malaysian road agencies to monitor bridge health. Through visual inspection analysis, the system could identify critical damage such as slab holes and corrosion. Comparing data from the system with the visual inspection data successfully demonstrated the system’s effectiveness. The results show a vibration signal of up to 1.0000 m/s recorded in a dataset taken at point KB21 at span 13. A visual inspection rating of 3 indicates that the bridge needs to be improved. These results emphasize adopting advanced technology to enhance bridge health to prevent accidents, causing tragic fatalities. This study contributes to the field by illustrating data fusion as a reliable, evidence-based method for enhancing bridge structural health. The results are expected to encourage Malaysian road agencies to improve visual inspections by adding new tools to provide actionable insights. The comparative data could improve the authority's decision-making and preventive maintenance efficiency. Future research could explore real-time monitoring and predictive analytics to advance bridge maintenance practices further while addressing weaknesses proactively.
A DES simulation aided construction planning of modular integrated construction: A Hong Kong Case Study
Many countries face ongoing pressures from limited land resources and housing shortages, which has driven planners to seek more efficient construction methods to meet the growing demand for high-density and sustainable housing. Given that, Modular Integrated Construction (MiC) has emerged as an innovative solution that transforms conventional fragmented on-site construction into integrated off-site production and on-site assembly of prefabricated modules. Previous research on MiC has explored various aspects, such as schedule optimization, transportation arrangements, and site management strategies. However, most studies have overlooked the uncertainty associated with off-site resource planning and activity durations. Meanwhile, there is still a lack of a decision support system for MiC that can comprehensively evaluate project performance. Therefore, this study develops a Discrete Event Simulation (DES) model to simulate the MiC construction process while considering uncertainty, aiming to optimize comprehensive performance, including cost, duration, and carbon emissions. After incorporating activity durations and available resources, this study continuously adjusts off-site and on-site resource planning to determine the optimal resource configurations and identify the factors that most significantly impact performance. The DES model is applied to a case study in Hong Kong after validation. The results show that MiC performance is more sensitive to off-site resources, like trucks, cranes, and wrapping crews. Furthermore, the weight of key performance indicators also significantly affects resource planning. The proposed DES model provides practical support for MiC construction, enabling managers to make better decisions under multiple objectives and complex constraints, thereby optimizing project resource planning and overall performance.
A Data-driven Framework for Automated Pavement Maintenance Strategy Generation: A Case Study of Florid
Road infrastructure in climate-sensitive regions such as the Southeastern United States faces increasing risks under climate change. Intense rainfall, hurricanes, storm surges, and freeze–thaw cycles accelerate pavement deterioration and disrupt roadway functionality. Developing adaptive and transparent maintenance strategies is therefore essential for sustaining performance and enhancing resilience. Current pavement management systems rely on static knowledge bases and empirical rules, which limits flexibility and interpretability. This study proposes a data-driven framework that combines Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) to generate context-aware and interpretable pavement maintenance strategies. A comprehensive knowledge base was constructed using Long-Term Pavement Performance (LTPP) data, Florida Department of Transportation manuals, regional climate information, and historical maintenance records. Structured indicators such as Annual Average Daily Truck Traffic, precipitation, and freeze index guided the retrieval process. Given pavement distress, climate, and traffic conditions, the RAG module retrieved relevant cases and technical standards, enabling the LLM to produce evidence-based recommendations. Validation on 30 pavement sections in Florida achieved an exact prediction accuracy of 76.7% with 23 correct classifications. Predictions were dominated by Mill and Overlay (15 cases, 50%) and Surface Treatment (9 cases, 30%), followed by Patch Repair (3 cases, 10%), Rigid Pavement Repair (2 cases, 6.7%), and Thin Overlay (1 case, 3.3%). Crack Sealing and Recycled Treatment were not predicted. The framework showed strong performance for structural and surface renewal actions, while preventive strategies remain underrepresented. Generated reports included condition summaries, historical references, recommendations, and assumptions, which improved interpretability and partially reduced the risk of hallucination.
Innovating Construction Supply Chains for Sustainability: Lessons from Aerospace
This study investigates how the aerospace industry's Concurrent Engineering (CE) practices can be used to improve construction supply chain efficiency, integration, sustainability, and intelligence. CE is a collaborative, interdisciplinary approach that allows simultaneous product and process development. It provides a framework for proactive, integrated supply chain management (SCM) that promotes innovation, resource optimisation, and reduced environmental impact, making construction supply chains sustainable and smart. Mixed-methods research included quantitative survey data from construction experts with qualitative interviews from six large South African construction projects. The survey found various CE enablers relevant to construction, including functional knowledge, shared product ownership, optimal resource utilisation, and increased communication and problem-solving. Interview data supported quantitative outcomes by emphasising early supplier participation, adaptive scheduling, collaborative decision-making, and continuous improvement. Construction can learn from aerospace by using CE supply chains with multidisciplinary teamwork, early supplier-contractor integration, and shared accountability for project outcomes as key themes. These strategies reduce waste and promote data-driven decision-making to improve project efficiency and sustainability. The paper provides a systematic framework for using CE principles in construction SCM to promote sustainability and innovation.
The Development of a Web Application for Rebar Loss Optimization Using BIM Models
Reinforced concrete structures, prevalent in construction, face significant cost and environmental challenges due to reinforcement bar (rebar) waste, which can account for 16% of project costs and 8% of total construction waste. Conventional rebar cutting practices, which rely on manual engineering judgment, often fall short of minimizing rebar waste effectively. To overcome this issue, this study proposed a framework that integrates BIM models with an optimization technique to automate the generation of optimal rebar cutting schemes via a web application platform. Developed as a web application, the platform leverages a scalable cloud infrastructure on Amazon Web Services (AWS), facilitating the efficient extraction and processing of rebar detailing data directly from BIM, performing optimization calculations, and providing intuitive visualization of cutting scheme results in a dashboard. Validation was performed through a case study on a subway station project, where the system optimized rebar cutting for slab construction. The proposed approach achieved a significant reduction in rebar waste by 72.2% and a saving of 8.9% in total rebar stock procurement compared to conventional methods. Additionally, the cloud-based application enhances collaboration and decision-making by providing an intuitive platform for all project stakeholders, ensuring seamless integration between rebar detailing design and procurement.
Global Excellence in Environmental Regulations towards Economical Sustainable Building Design
Sustainable building contributes to a healthy ecosystem. Sustainable building has also reduced carbon emissions and improved climate management. Thus, sustainable construction improves resource management, operating systems, utility bills, and operational efficiency. Sustainable building construction improves energy efficiency and the environment by adding amenities and lowering energy use. The study conducted a moderation analysis using hierarchical multiple regression to explore whether financial constraints and lack of awareness have any moderating effect on the economic sustainability as well as use of sustainable design. The research further provides the mediation influences of waste reduction, social awareness, energy consumption, and environmental responsibility towards economic sustainability and sustainable design. The findings suggest solutions to enhance construction, sustainability, and the environment. Thus, sustainable designs have benefited the building industry and introduced sustainability to construction operations. Sustainability reviews reduce construction waste and enhance organisation. This research also examined how construction businesses please customers and retain market share. Construction promotes resource management and community development to boost product production. Sustainable expansion in the UK construction industry improves customer experiences and reduces greenhouse gas emissions. Sustainability in UK construction lowers environmental harm and enhances health. The construction industry is evolving towards net zero and sustainable services for targeted clientele, which may improve customer experiences. Researchers have found different perspectives on the construction sector, which has improved sustainability. After that, sustainable design helped the construction industry enhance workplace sustainability and corporate management. Thus, building industry concepts have improved workplace sustainability and eliminated environmental issues.
Towards Safer and More Efficient Logistics: Rack Specifications for Prefabricated Façade Panels
The increased use of modular construction focuses on streamlined rack systems, but the existing ad-hoc-based plans for stacking logistics require a methodical approach. This paper provides a comprehensive guideline on rack specification to support safe, efficient and cost-effective transportation of prefabricated façade panels during modular integrated construction (MiC) to address issues such as instability and damage in transit. A mixed methods design combines a five-step process, which involves literature review and existing standards (e.g. EN 12195; ISO 1496-1), 2-round Delphi questionnaire, 13 specialists, statistical analysis, and a validation rack design of industrial supplier against the developed specifications. The most significant results include seven drivers, such as Panel Characteristics, Handling/Loading-Unloading, Transportation Conditions, Cost and Budget, Safety and Stability, Modularity and Adaptability, and Space Efficiency, each with 25 factors, and safety and stability (e.g., vibration resistance, mean 4.538, SD 0.877) are the top drivers. The novel specification guide fills industrial design gaps, such as poor fastening mechanisms. These are small sample sizes and untested second-round data, implying that further validation should be done. In practice, the guide aids the architecture and construction logistics through the delivery of panels free of damage, and the partner's design demonstrates compliance regarding trailer compatibility. However, fasteners need to be improved to guarantee a higher level of safety. It also fosters sustainable urban development because of the reduction of costs and delays and the possible benefits to society, such as better living conditions due to efficient renovations.
Topic Modelling and Sentiment Analysis of AI-driven Circular Procurement practices
The construction industry is the biggest worldwide consumer of raw materials, producing the most waste among all industries, with the UK construction sector generating 62% of national waste. Organisations can address these challenges through Circular Procurement (CP), which implements Circular Economy (CE) principles by incorporating reuse and recycling and material efficiency into procurement processes. However, CP remains underexplored in construction scholarship and practice. The research uses sentiment analysis and topic modelling to explore the integration of Artificial Intelligence (AI) in CP discourse. The research included 23 peer-reviewed articles from Scopus and Google Scholar databases. The sentiment analysis showed a positive attitude toward AI procurement, but most of the content was neutral because it focused on description rather than emotional expression. The LDA model produced five thematic areas, including machine learning and circularity. The corpus shows that circular procurement appears in only 3% of the text, which indicates its early stage of development in this field. The results show that AI methods already simplify procurement and could be extended to embed CE principles more effectively in construction. The originality of this research lies in combining sentiment analysis with topic modelling to provide a dual perspective on both tone and latent themes of the academic discourse. The research value comes from its demonstration of limited academic studies on AI-driven CP and its presentation of how existing AI procurement tools can be modified to support circularity. The research addresses current knowledge deficiencies while creating fundamental elements for sustainable procurement methods in construction projects.
Artificial Intelligence; Circular Procurement; Construction; Sentiment Analysis; Topic Modelling
Embedding Net-Zero in Construction: Client–Contractor Practices and Collaboration
This research addresses a critical gap in the built environment: the human and collaborative factors that contribute to successful net-zero construction. While the technical and economic aspects of net-zero are well-documented, the day-to-day collaborative practices and inter-organisational dynamics that translate these goals into project reality remain underexplored. Drawing on Practice Theory, this study seeks to understand how the interactions, routines, and shared understandings of clients and contractors shape net-zero project outcomes. To achieve this, a qualitative research approach was employed, involving semi-structured interviews with a diverse group of professionals from both the client and contractor sectors involved in net-zero construction projects in the UK. This methodology was designed to provide rich, nuanced insights into the practical realities of their working relationships. Findings from early conversations with participants suggest that successful collaboration is not defined by formal contracts alone, but is profoundly influenced by informal practices, trust, and the development of shared values and emergent learning across project teams. Key challenges identified include misaligned expectations, traditional procurement models, and a lack of mechanisms for effective knowledge exchange. In conclusion, this research provides a novel theoretical contribution by applying Practice Theory to identify and analyse specific collaborative mechanisms and their implications for sustainable construction. The findings are highly relevant for enhancing project management strategies and fostering a collaborative culture. Future research could extend this practice-based lens to examine how these dynamics evolve over a project's lifecycle.
Toward Safer Integration of Active Back-Support Exoskeletons in Construction through Domain-Specific Language Models
Active back-support exoskeletons have shown promise in reducing back-related musculoskeletal disorders in labor-intensive occupations. Yet, adoption in construction remains constrained by fragmented evidence on their biomechanical benefits, human factors risks, and practical usability. This study presents the development of a domain-adapted large language model fine-tuned with the Retrieval-Augmented Fine-Tuning framework to systematically synthesize and communicate evidence relevant to active back-support exoskeleton use in construction. The training dataset was developed from a review of twenty peer-reviewed studies spanning nine construction tasks and seven active back-support exoskeleton models, covering outcomes related to muscle activity, range of motion, perceived discomfort, exertion, usability, cognitive load, fall risk, and sociotechnical adoption factors. These studies identified both facilitators (e.g., productivity gains, posture correction, and device durability) and barriers (e.g., restricted mobility, thermal discomfort, and task–device incompatibility) shaping adoption in construction workflows. A total of 3,650 question–answer pairs were generated with distractors and Chain-of-Thought reasoning and used in a teacher–student distillation process with GPT-4o and GPT-4o-mini. The fine-tuned model achieved a validation accuracy of 88% and demonstrated stable generalization without overfitting, supported by low validation loss. In head-to-head evaluation against the baseline, the fine-tuned model achieved reliable scores in coherence, relevance, and harmlessness, a 10% improvement in response completeness (96% vs. 86%), and a 2% increase in factual accuracy (82% vs. 80%). The results demonstrate the feasibility of deploying fine-tuned large language models as interactive decision-support tools for exoskeleton adoption in construction, advancing the intersection of artificial intelligence, biomechanics, and occupational safety.
Ontology-Guided Extraction of Infrastructure Damage Information during Floods Using Large Language Models
Urban floods are becoming increasingly frequent due to intensified extreme rainfalls and ongoing urbanisation, posing significant threats to both human life and the built environment in cities worldwide. Timely, high-resolution data on infrastructure damage during such events is essential for effective emergency response and long-term adaptation planning. However, conventional data collection methods, which rely on manual inspection and official reporting, often fail to capture large-scale and event-specific impacts. In contrast, social media platforms have emerged as a publicly available source of real-time user-generated observations, though their informal and unstructured nature presents challenges for systematic information extraction. This study proposes a novel approach to automatically extract structured infrastructure damage information from social media posts related to flood events. A prompt engineering strategy for Large Language Models (LLMs), guided by a domain-specific ontology of infrastructure assets and damage typologies, is developed to ensure classification consistency and extraction reliability. A case study of the 2021 Zhengzhou flood in China illustrates the effectiveness of the proposed method. Preliminary results suggest that general-purpose LLMs can be transformed into high-accuracy information extractors, providing a scalable foundation for downstream analyses of infrastructure vulnerability and disaster impacts. Moreover, the proposed approach supports the development of flood-related infrastructure damage databases, which are critically needed for advancing research on infrastructure resilience.
Integrating Accessibility Assessment into Architectural Design: A Building Information Modeling (BIM) based Approach
Accessibility is a critical component of inclusive architectural design, ensuring that built environments are usable and safe for individuals of all abilities. However, standards and guidelines for accessibility are considered as add-ons instead of integral design components, and the compliance checks tend to be laborious and inefficient. Building information modeling presents a suitable platform for embedding accessibility assessment early and consistently throughout the design lifecycle. Therefore, this study introduces a simulation system based on Building Information Modelling (BIM) for evaluating and visualising accessibility in architectural environments. A digital building model with distinct parameters such as circulation width, turning radius, ramp slopes, and spatial reachability, facilitating rule-based analysis of adherence to accessibility regulations is created. Following which, simulated movement trajectories for various user demographics, such as wheelchair users or individuals with restricted mobility, are simulated to identify areas that are hard to access, overcrowded, or poorly designed. This model would then be used to generate visual outputs such as spatial heatmaps and performance graphs, offering immediate feedback and design insights during the various design stages. The proposed methodology offers flexible implementation of different national or institutional accessibility regulations and standards. Hence, the use of this BIM-integrated approach would help architects and planners identify and fix potential barriers in designing spaces that are accessible to all, thereby enabling dignity, independence, and equitable participation, irrespective of physical ability.
A Design Framework for Adaptive Interactive Space with Kinematic Spatial Components
In contemporary interior environments, the co‐existence of diverse functions and heterogeneous user groups within a single physical setting has become increasingly prevalent, driving the need for real‐time spatial reconfiguration that accommodates users’ activities, temporal rhythms, and privacy requirements. Although emerging robotic technologies have opened new avenues for adaptive interactive design, there remains a paucity of quantitative and objective metrics to guide the kinematic and cognitive underpinnings of space–user interaction. To address this gap, we propose an integrated Human–Space Interaction (HSI) framework that systematically delineates the factors underpinning precise, bidirectional engagement between occupants and dynamically reconfigurable architectures. Drawing upon a comprehensive review of prior studies that examine users’ responses to active spatial transformations, we classify relevant variables under three principal domains: Space, Human, and Kinematics. They are organized according to three interaction typologies: (1) user‐driven spatial change, (2) impact of dynamic spatial elements on users, and (3) influence of static spatial attributes on initial user behavior and state. This framework reveals a cyclical relationship among users’ physiological and behavioral states, and spatial dynamics. To validate the framework’s applicability, we developed a prototype environment and accompanying scenario in which movable partitions responded in real time to user gestures and task demands; empirical evaluation demonstrated enhanced usability and user acceptance. Our contributions include (a) a theoretically grounded framework of HSI variables, (b) design guidelines for the development of user‐centric interactive spaces, and (c) empirical evidence supporting the framework’s efficacy.
Sustainable Building Maintenance: A Systematic Review and the Emerging Role of Building Information Modelling
Building maintenance is an important activity across the building's lifecycle, requiring effective strategies to maintain its functionality, safety and comfort. Maintenance has become a strategic sustainability approach to intentionally manage assets and maximize building productivity for longer life. In public institutional buildings, however, maintenance is not integrated into core operational activities, that risks cost overruns and reduced building lifecycle. This issue is particularly important within developing countries where public institutions often operate within financial constrain. Existing literature studies focus on traditional approaches but do not integrate emerging technologies that could help transform the maintenance practice. Therefore, this study performs a systematic literature review (SLR) of recent advancements at the intersection of building maintenance, emerging technologies and sustainability. Our results indicate that with increasing complexities of the built environment and demand for data-driven solutions, digital tools such as Building Information Modelling (BIM) could serve as an enabler for sustainable maintenance. However, the scale of industry adoption and sustainability impact remain unclear especially in developing countries. Therefore, we propose a conceptual BIM4SM (BIM for sustainable maintenance) framework, to extend the building life with increasing efficiencies and productivity for key stakeholders. This highlights the potential of BIM to support informed decision-making, enhance resource efficiency, and align maintenance activities with broader sustainability goals. The BIM4SM framework would help reimagine BIM as a sustainability-driven decision-making tool, promoting digital transformation in the operations and maintenance phase of a building’s lifecycle. The framework can help facilitate the practitioners, researchers, and policymakers choose more future-resilient building management strategies.
Enabling Circularity through Dynamic Material Passports: A Framework Integrating Digital Twin Technologies and Data Governance in the Built Environment
Achieving circularity and sustainability in the built environment requires advanced mechanisms for tracking and managing material flows across the building lifecycle. Material passports (MPs) have emerged as key digital tools to enable material traceability, reuse, and lifecycle intelligence. However, conventional MPs remain static and limited in supporting dynamic decision-making for sustainable construction practices. This study introduces a novel framework for Dynamic Material Passports (DMPs), developed to enhance material transparency, enable circular resource flows, and support lifecycle-oriented sustainability strategies. The framework leverages Building Information Model (BIM)-based digital twin technologies and internet of things to enable real-time updates and continuous alignment between physical building assets and their digital counterparts. It also incorporates structured data governance mechanisms, drawing from emerging practices in interoperability, blockchain, and stakeholder access control to ensure secure, transparent, and scalable information management. The framework is developed in alignment with Canadian digital construction priorities and standards (e.g., CCMC, NMS) but is designed for global applicability. By embedding digital twin integration and data governance directly into the structure of DMPs, the framework addresses key barriers to circularity, including data fragmentation, limited reuse planning, and lack of lifecycle accountability. The proposed approach contributes to advancing sustainable construction practices and enabling more effective circular economy strategies across the built environment.
Aesthetics vs. Efficiency? Balancing Energy Performance and Architectural Appeal through Decision Support with Telegram Bot
Balancing energy performance and architectural appeal in building renovation presents significant challenges, especially with regulations like the Buildings Energy Act (GEG) emphasizing energy efficiency. This study investigates which retrofit measures have the greatest impact on aesthetics. Through stakeholder analysis, the study examines the interests of key groups involved in renovation projects, including property owners, architects and engineers. To identify potential conflicts between energy efficiency and aesthetic quality, thirteen expert interviews were conducted. Based on these findings, a Telegram Bot is proposed as a decision-support tool. In a case study, the bot demonstrates how it can provide tailored recommendations to balance energy performance and architectural appeal, thereby strengthening the life cycle approach of buildings.
Strategic Renovation and Healthcare Infrastructure: A Case Study of Facility Upgrades and Compliance at a Private Hospital in Kenya
Legacy healthcare infrastructure in low- and middle-income countries increasingly faces pressure to modernise ageing buildings, enhance patient safety, and align with international accreditation standards. This study aims to examine how phased healthcare renovation and infrastructure upgrades can be strategically planned and implemented within an active hospital environment to meet clinical, safety, and compliance goals. The study will employ a descriptive case study to evaluate the renovation of a critical hospital unit in Kenya, a low- and middle-income country. The case study methodological approach will be guided by Evidence-Based Design (EBD), user input, and Joint Commission International (JCI) tracer assessments. Data will be drawn from internal project records, audit findings and architectural plans. Theoretically, the findings of this study are intended to contribute to the limited body of knowledge on hospital renovation and restoration in low- and middle-income countries. Practically, the findings of this study aim to provide practical insights into how healthcare facilities in resource-constrained urban settings can approach infrastructure renewal not merely as construction tasks, but as integrated, strategic opportunities to embed resilience, safety, and sustainability into the built environment. Keywords: Healthcare renovation, Infrastructure modernisation, JCI compliance, Phased construction, Kenya
Harnessing AI and Digital Twins in BIM for Wildfire-Resilient WUI Communities: A Systematic Review
Communities within the wildland–urban interface (WUI) are increasingly exposed to severe and frequent wildfire threats due to climate change, land-use change, and expanding urban development. Traditional fire management approaches have proven inadequate to address the complex interactions of fire behavior, human exposure, and infrastructure vulnerability. Emerging digital technologies, particularly Building Information Modeling (BIM) and digital twins, present opportunities to transform WUI community resilience by enabling data-rich, predictive, and adaptive strategies for fire preparedness and response. This review examines the current state of research on integrating BIM, digital twins, and artificial intelligence (AI) to support multi-scale wildfire modeling, dynamic risk assessment, real-time situational awareness, and post-event recovery in the built environment. A systematic literature review methodology was applied to synthesize studies on predictive fire spread models, sensor-driven fuel condition monitoring, and integration of geospatial and structural data. The review identifies key technical enablers and barriers, including challenges of data interoperability, real-time integration, and the lack of standardized protocols for linking AI models with BIM-based systems. The findings propose a conceptual framework for advancing AI-enabled digital twin applications to support proactive WUI fire resilience. These insights have significant implications for researchers, practitioners, and policymakers seeking to leverage the built environment as a lever of change through digital transformation, offering directions for future research to enhance the safety, adaptability, and sustainability of WUI communities facing escalating wildfire risks.
Enhancing Hybrid NLP Approaches with the Strategic Shift Toward retrieval-augmented generation for Design Phase Communication Documents Processing
Hybrid Natural Language Processing (NLP) approaches have demonstrated superior performance in construction document processing, with previous research showing that combining rule-based methods with traditional Named Entity Recognition (NER) achieves 98% accuracy for information extraction tasks in roof design documentation. However, the integration of modern pre-trained transformer models into these hybrid architectures remains underexplored. This paper investigates the enhancement of established hybrid NLP frameworks by replacing conventional machine learning components with fine-tuned pre-trained transformers for improved information extraction from design phase communication documents in roof design practice. Building upon proven hybrid methodologies combining regular expression (Regex) pattern matching with machine learning components, we systematically replace traditional NER models with fine-tuned BERT-base architecture. The enhanced hybrid approach maintains rule-based preprocessing efficiency while leveraging BERT's contextual understanding capabilities. We evaluate this BERT-enhanced hybrid system on information extraction tasks using roof design communication documents and project specifications, benchmarked against the established Regex+NER baseline (98% accuracy). Experimental results demonstrate that the Regex+BERT hybrid approach achieves 99.2% accuracy, representing a 1.2 percentage point improvement over the original hybrid baseline. Detailed analysis reveals that BERT-enhanced hybrids excel in handling contextual ambiguities and formatting variations in roof design communications while retaining computational efficiency and interpretability advantages of the original hybrid architecture. The research provides practical frameworks for practitioners seeking to enhance existing hybrid NLP systems with BERT without complete architectural redesign. Results indicate that selective integration of BERT significantly improves accuracy while preserving operational advantages that make hybrid approaches attractive for roof design practice applications
Artificial Intelligence and the Future Construction Workforce: A Qualitative Study of Emerging Skill Demands
The U.S. construction industry is in a transitional phase characterized by the gradual yet accelerating integration of digital technologies across both field and administrative operations. This transformation is occurring amidst acute skilled labor shortages and an aging workforce, compelling firms to explore automation and artificial intelligence (AI) systems to enhance productivity, efficiency, and safety. While most existing research on AI in construction largely prioritizes technological innovation, the corresponding shifts in workforce skill demands remain under-explored. This qualitative study seeks to address this gap by examining the anticipated changes in workforce competencies resulting from the adoption of AI-enabled tools in the construction industry. Based on six in-depth semi-structured interviews with construction experts engaged in AI implementation, the study investigates emerging workforce competencies and projected role shifts across short and mid-term timeframe. The findings include three primary skill competency categories: (1) AI-enabled operational proficiency: referring to the ability to work effectively with technologies such as drones, robotic equipment, and predictive analytics tools; (2) analytical interpretation: encompassing the capacity to critically engage with AI-generated data across functions such as scheduling, risk assessment, and cost management; and (3) integrated digital-trade fluency: which captures the intersection of technological literacy and domain-specific construction expertise. This study contributes to the body of knowledge by understanding the construction workforce transformation and emerging competencies essential for sustained adaptability of AI in the construction industry. These preliminary findings form part of a broader ongoing research study involving over twenty industry interviews, which will inform a forthcoming peer-reviewed journal article.