Search Results (194)

Abstract: This paper explores the evolving relationship between Digital Twins (DT) and the Metaverse, two foundational yet often conflated digital paradigms in digital architecture. While DTs function as mirrored models of real-world systems—integrating IoT, BIM, and real-time analytics to support decision-making—Metaverses are typically fictional, immersive, multi-user environments shaped by social, cultural, and speculative narratives. Through several research projects, the team investigate the divergence between DTs and Metaverses through the lens of their purpose, data structure, immersion, and interactivity, while highlighting areas of convergence driven by emerging technologies in Artificial Intelligence (AI) and Extended Reality (XR).This study aims to investigate the convergence of DTs and the Metaverse in digital architecture, examining how emerging technologies—such as AI, XR, and Large Language Models (LLMs)—are blurring their traditional boundaries. By analyzing their divergent purposes, data structures, and interactivity modes, as well as hybrid applications (e.g., data-integrated virtual environments and AI-driven collaboration), this study seeks to define the opportunities and challenges of this integration for architectural design, decision-making, and immersive user experiences. Our research spans multiple projects utilizing XR and AI to develop DT and the Metaverse. The team assess the capabilities of AI in DT environments, such as reality capture and smart building management. Concurrently, the team evaluates metaverse platforms for online collaboration and architectural education, focusing on features facilitating multi-user engagement. The paper presents evaluations of various virtual environment development pipelines, comparing traditional BIM+IoT workflows with novel approaches such as Gaussian Splatting and generative AI for content creation. The team further explores the integration of Large Language Models (LLMs) in both domains, such as virtual agents or LLM-powered Non-Player-Controlled Characters (NPC), enabling autonomous interaction and enhancing user engagement within spatial environments. Finally, the paper argues that DTs and Metaverse’s once-distinct boundaries are becoming increasingly porous. Hybrid digital spaces—such as virtual buildings with data-integrated twins and immersive, social metaverses—demonstrate this convergence. As digital environments mature, architects are uniquely positioned to shape these dual-purpose ecosystems, leveraging AI, XR, and spatial computing to fuse data-driven models with immersive and user-centered experiences. Full article

In recent decades, Japan has faced rural depopulation due to urban migration, resulting in widespread property abandonment, the “Akiyas”. This paper presents RE-HAK (Refurbish to Host in Akiyas), a blockchain-based framework promoting a circular economy (CE). RE-HAK enables occupants to live rent-free in Akiyas by completing AI-managed refurbishment milestones via smart contracts. Each milestone—waste removal, structural repairs, or energy upgrades—is verified and recorded on the blockchain. Benefits include: (1) rural economic revival through restoration incentives; (2) sustainable CE adoption; (3) preserving property values by halting deterioration; (4) safeguarding cultural heritage via traditional architecture restoration; and (5) transparent management through automated contracts, minimizing disputes. Findings from three case studies demonstrate RE-HAK’s adaptability across skill levels and project scales, though limitations such as rural digital literacy gaps and reliance on government support for scalability are noted. The framework advances Japan’s revitalization goals while offering a replicable model for nations facing depopulation and property abandonment, contingent on addressing technological and policy barriers. Full article

Wind energy has emerged as a strategic pillar in the global energy transition, offering both environmental and economic benefits. This comprehensive review explores the development of wind energy with a focus on the regulatory, socio-economic, and technological challenges that shape its deployment in Europe, particularly in Poland. The study highlights disparities between countries in terms of both total and per capita installed capacity, emphasizing the importance of equitable access to renewable energy. Denmark and Germany outperform larger economies like China and India in per capita terms, indicating the significance of effective policy frameworks and public engagement. The article presents detailed case studies of successful wind farm projects across the EU alongside economic evaluations including cost structures, return on investment, and local development impacts. Additionally, the role of innovation—such as floating offshore wind farms and AI-based energy management—is discussed in the context of improving efficiency and overcoming infrastructure and environmental barriers. The analysis is supported by quantitative comparisons, graphical representations, and policy reviews, culminating in practical recommendations for future growth. Wind energy’s expansion depends on integrated strategies that combine policy reform, technological advancement, economic viability, and community participation. Full article

Natural language interfaces can transform the construction industry by enhancing accessibility and reducing administrative workload in the day-to-day operations of project teams. This paper introduces the Voice-Integrated Scheduling Assistant for 4D BIM (VISA4D) tool that integrates speech recognition and Natural Language Processing (NLP) capabilities with Building Information Modeling (BIM) to streamline construction schedule updating and maintenance processes. It accepts voice and text inputs for schedule updates, facilitating real-time integration with Autodesk Navisworks, and eliminates the need for direct access to or advanced knowledge of BIM tools. It also provides visual progress tracking abilities through colour-coded elements within the 4D BIM model for communicating task status updates within the project teams. To demonstrate its capability to enhance schedule updating and maintenance efficiency, the VISA4D tool is implemented in an office building project in Canada and user testing is performed. An overall accuracy of 89% was observed in successfully classifying 71 out of 80 tested construction-specific commands, while the user surveys indicated high usability, with 92% of participants finding VISA4D easy to use and reporting consistent command recognition accuracy. This study advances the existing work on AI-enhanced construction management tools by tackling the challenges associated with their practical implementation in field operations. Full article

This study investigates the use of generative AI tools to create culturally inclusive case studies in postgraduate project management education, addressing a critical gap in existing research. While prior literature highlights the benefits of culturally responsive teaching (CRT) practices, there is notable lack of exploration into how generative AI can be leveraged to develop culturally relevant learning materials. Using an interpretivist philosophy and action research methodology, the study engaged eight international students to evaluate the effectiveness of AI-generated case studies tailored to diverse cultural contexts. The major contribution of this study is the development of a structured framework of strategies and considerations that guides educators in designing culturally inclusive materials using generative AI tools. The inclusion of clearly defined strategies provides educators with practical guidance, while the accompanying considerations act as essential safeguards, encouraging critical reflection on potential risks such as bias, stereotyping, and ethical misuse. The findings hold significant implications for educational practice, emphasising the ethical use of AI, targeted professional development for educators, and the potential for scalable, inclusive teaching strategies that enhance student engagement, equity, and learning outcomes in multicultural classrooms. Full article

The construction industry is transforming significantly, with emerging technologies reshaping project management by enhancing efficiency, sustainability, and safety. This study examines the integration of these innovations into Chad’s construction sector, drawing on insights from 79 industry participants. Given Chad’s unique economic and infrastructural landscape, understanding the practical implementation of these technologies is crucial. This research demonstrated strong reliability and validity through exploratory factor analysis, with a KMO value above 0.75, statistical significance at p < 0.001, and a Cronbach’s Alpha exceeding 0.8. Using Promax rotation, this study identified 15 key factors, providing valuable insights into how technologies such as Building Information Modeling (BIM), Artificial Intelligence (AI), the Internet of Things (IoT), and Digital Twin technology are transforming construction processes. These tools enhance design accuracy, facilitate real-time decision-making, and minimize material waste while supporting global sustainability goals, including the United Nations’ Sustainable Development Goals (SDGs). Examining the adoption of these technologies within Chad is particularly important, as the country faces unique challenges that demand tailored solutions. While digital transformation in the construction industry has been widely studied worldwide and in Africa, Chad’s industry remains relatively unexplored in this regard. This research bridges this gap by identifying both the opportunities and the barriers to technological integration in the sector. Embracing these innovations could help modernize Chad’s construction industry, addressing persistent inefficiencies and promoting environmental sustainability. However, widespread adoption is hindered by significant challenges, including high implementation costs, limited access to advanced tools, and a shortage of skilled professionals. Overcoming these obstacles will require strategic investments in education, infrastructure, and supportive policies. By fully leveraging technological advancements, Chad has the potential to build a more competitive, resilient, and sustainable construction industry, driving national development while aligning with global sustainability initiatives. Full article

Traditional construction safety management, reliant on manual inspections and heuristic judgments, increasingly fails to address the dynamic, multi-dimensional risks of modern projects, perpetuating fragmented safety governance and reactive hazard mitigation. This study proposes an integrated building information modeling (BIM)–AI platform to unify safety supervision across the project lifecycle, synthesizing spatial-temporal data from BIM with AI-driven probabilistic models and IoT-enabled real-time monitoring for sustainable construction safety management. Employing a Design Science Research methodology, the platform’s phase-agnostic architecture bridges technical–organizational divides, while the Multilayer Neural Risk Coupling Assessment framework quantifies interdependencies among structural, environmental, and human risk factors. Prototype testing in real-world projects demonstrates improved risk detection accuracy, reduced reliance on manual processes, and enhanced cross-departmental collaboration. The system transitions safety regimes from compliance-based protocols to proactive, data-empowered governance. This approach offers scalability across diverse projects. The BIM-AI intelligent fusion platform proposed in this study builds an intelligent construction paradigm with synergistic development of safety governance and sustainability through whole lifecycle risk coupling analysis and real-time dynamic monitoring, which realizes a proactive safety supervision system while significantly reducing construction waste and accident prevention mechanisms. Full article

The accurate prediction of river discharge is essential in water resource management, particularly under variability due to climate change. Traditional hydrological models commonly struggle to capture the complex, nonlinear relationships between climate variables and river discharge, leading to uncertainties in long-term projections. To mitigate these challenges, this research integrates machine learning (ML) and deep learning (DL) techniques to predict discharge in the Subernarekha River Basin (India) under future climate scenarios. Global climate models (GCMs) from the Coupled Model Intercomparison Project 6 (CMIP6) are assessed for their ability to reproduce historical discharge trends. The selected CNRM-M6-1 model is bias-corrected and downscaled before being used to simulate future discharge patterns under SSP585 (a high-emission scenario). Various AI-driven models, such as a temporal convolutional network (TCN), a gated recurrent unit (GRU), a support vector regressor (SVR), and a novel DL network named the Temporal Enhanced Attention Network (TeaNet), are implemented by integrating the maximum and minimum daily temperatures and precipitation as key input parameters. The performance of the models is evaluated using the mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination (R²). Among the evaluated models, TeaNet demonstrates the best performance, with the lowest error rates (RMSE: 2.34–3.04; MAE: 1.13–1.52 during training) and highest R² (0.87–0.95), outperforming the TCN (R²: 0.79–0.88), GRU (R²: 0.75–0.84), SVR (R²: 0.68–0.80), and RF (R²: 0.72–0.82) by 8–15% in accuracy across four gauge stations. The efficacy of the proposed model lies in its enhanced attention mechanism, which successfully identifies temporal relationships in hydrological information. In determining the most relevant predictors of river discharge, the feature importance is analyzed using the proposed TeaNet model. The findings of this research strengthen the role of DL architectures in improving long-term discharge prediction, providing valuable knowledge for climate adaptation and strategic planning in the Subernarekha region. Full article

Despite stricter global energy codes, performance standards, and advanced renewable technologies, the building sector must accelerate its transition to zero carbon emissions. Many studies show that new buildings, especially non-residential ones, often fail to meet projected performance levels due to poor maintenance and management of HVAC systems. The application of predictive AI models offers a cost-effective solution to enhance the efficiency and sustainability of these systems, thereby contributing to more sustainable building operations. The study aims to enhance the control of a variable air volume (VAV) system using machine learning algorithms. A novel ventilation control model, AI-VAV, is developed using a hybrid extreme learning machine (ELM) algorithm combined with simulated annealing (SA) optimisation. The model is trained on long-term monitoring data from three office buildings, enhancing robustness and avoiding the data reliability issues seen in similar models. Sensitivity analysis reveals that accurate occupancy prediction is achieved with 8500 to 10,000 measurement steps, resulting in potential additional energy savings of up to 7.5% for the ventilation system compared to traditional VAV systems, while maintaining CO₂ concentrations below 1000 ppm, and up to 12.5% if CO₂ concentrations are slightly above 1000 ppm for 1.5% of the time. Full article

Background/Objectives: Atopic dermatitis (AD) is a chronic inflammatory skin disease characterized by recurrent rashes and itching, which seriously affects the quality of life of patients and brings a heavy economic burden to society. The treat-to-target (T2T) strategy was proposed to guide optimal use of systemic therapies in patients with moderate to severe AD, and patients’ adherence is emphasized along with combined evaluation from both health providers and patients. While effective treatments for AD are available, non-adherence of treatment is common in clinical practice due to the patients’ unawareness of self-evaluation and lack of concern about the specific follow-up time points in clinics, which leads to the treatment failure and repeated relapse of AD. Methods: This project consists of three parts. First, an artificial intelligence (AI) model for diagnosis and severity grading of AD based on deep learning will be trained. Second, an AI assistant decision-making system (AIADMS) in the form of an app will be developed. Third, we design a prospective, randomized controlled trial to test the hypothesis that the AIADMS with implementation of the T2T could help control the disease progression and improve the clinical outcomes. Results: A total of 232 participants diagnosed with moderate to severe AD will be included and allocated into the app group or the control group. In the app group, participants will be assisted in using the app during the process of management and follow-up at the scheduled time points, including 2 weeks, 4 weeks, 8 weeks, 12 weeks, 6 months, and 12 months after treatment. In the control group, the diagnosis, treatment, and follow-up of participants will be carried out according to the current routine on a face-to-face basis. The primary outcome is the overall efficiency rate of treating objectives including PP-NRS, EASI, SCORAD, POEM, and DLQI at 12 weeks after treatment, which is calculated as the “Total number of participants with effective treatment of 5 treating objectives/total number of participants *100%”. Spss20.0 software will be used to analyze the data according to the principle of intent to treat. Trial Registration: The protocol was registered at the National Institutes of Health Clinical Trials Registry with the trial registration number NCT06362629 on 11 April 2024. Conclusions: This study aims to improve AD management by integrating advanced technology, patient engagement, and clinician oversight through AIADMS app to achieve treat-to-target (T2T) goals for effective and safe long-term control. Full article

This work concentrates on an experimental project for the integration of Large Language Models (LLMs) inside a Historic Building Information Modeling (HBIM) workflow. In particular, this evaluation was carried out by using open source solutions as concerns parametric modeling of BIM elements. This experimental test focuses on how Python scripts, generated by AI agents, can create parametric models for HBIM purposes and archaeology: starting from the archaeological plan, the parametric modeling of the Parthenon temple was carried out via a text-to-BIM workflow based on OpenAI and open source tools. The use of AI in generating these scripts can potentially automate and streamline the modeling process, making it more efficient and less prone to human error (or almost). FreeCAD, being a Python-based software, is identified as the perfect fieldwork for this test. Its open source nature allows extensive customization and experimentation, making it an ideal platform for integrating AI-generated Python scripts. In addition to proving a flexible and operative BIM platform, this approach could achieve the same results by parametric modeling via Python scripts generated by LLMs. By harnessing the power of LLMs, FreeCAD could serve not only as a robust BIM tool but also as a testbed for pushing the boundaries of what AI can achieve in the realm of parametric modeling and HBIM. This project opens new possibilities for automating the creation of detailed, accurate BIM models, ultimately contributing to the preservation and management of heritage buildings. Full article

This systematic review investigates the integration of artificial intelligence (AI) in cost estimation within project management, focusing on its impact on accuracy and efficiency compared to traditional methods. This study synthesizes findings from 39 high-quality articles published between 2016 and 2024, evaluating various machine learning (ML), deep learning (DL), regression, and hybrid models in sectors such as construction, healthcare, manufacturing, and real estate. The results show that AI-powered approaches, particularly artificial neural networks (ANNs)—which constitute 26.33% of the studies—, enhance predictive accuracy and adaptability to complex, dynamic project environments. Key AI techniques, including support vector machines (SVMs) (7.90% of studies), decision trees, and gradient-boosting models, offer substantial improvements in cost prediction and resource optimization. ML models, including ANNs and deep learning models, represent approximately 70% of the reviewed studies, demonstrating a clear trend toward the adoption of advanced AI techniques. On average, deep learning models perform with 85–90% accuracy in cost estimation, making them highly effective for handling complex, nonlinear relationships and large datasets. Machine learning models achieve an average accuracy of 75–80%, providing strong performance, particularly in industries like road construction and healthcare. Regression models typically deliver 70–80% accuracy, being more suitable for simpler cost estimations where the relationships between variables are linear. Hybrid models combine the strengths of different algorithms, achieving 80–90% accuracy on average, and are particularly effective in complex, multi-faceted projects. Overall, deep learning and hybrid models offer the highest accuracy in cost estimation, while machine learning and regression models still provide reliable results for specific applications. Full article

This study presents a mixed-method systematic literature review (SLR) investigating the applications of Industry 4.0 (I4.0) technologies for enhancing sustainability in transportation infrastructure projects from a construction perspective. A corpus of 199 scholarly articles published between 2009 and November 2023 was meticulously selected from the Scopus database. The thematic analysis categorised the publications into four main clusters: infrastructure type, technology types, project lifecycle stages, and geographic context. The scientometric analysis revealed a burgeoning interest in the integrating of I4.0 technologies to enhance sustainability—particularly environmental sustainability. Among these, Building Information Modelling (BIM)-related tools emerged as the most extensively studied domain (33.50%), followed by the Internet of Things (IoT) and sensors (14%), and Artificial Intelligence (AI) (13.22%). The findings demonstrate that roads, highways, and bridges are the most studied infrastructure types, with BIM being predominantly utilised for energy assessment, sustainable design, and asset management. The main contributions of this review are threefold: (1) providing a comprehensive framework that categorises I4.0 applications and their sustainability impacts across transportation infrastructure types and project lifecycle stages, (2) identifying key technical challenges in integrating I4.0 technologies with sustainability assessment tools, and (3) revealing underexplored areas and providing clear directions for future research. The findings provide actionable insights for researchers and industry practitioners aiming to adopt integrated, sustainability-driven digital approaches in transport infrastructure delivery. Full article

The construction industry is undergoing a transformative shift through automation, with advancements in Generative AI (GenAI) and prompt engineering enhancing safety and efficiency, particularly in high-risk fields like underground construction, geotechnics, and mining. In underground construction, GenAI-powered prompts are revolutionizing practices by enabling a shift from reactive to predictive approaches, leading to advancements in design, project planning, and site management. This study explores the use of Google Gemini, a recent advancement in GenAI, for the prediction of rockburst intensity levels in underground construction. The Python programming language and the Google Gemini tool are combined with prompt engineering to generate prompts that incorporate essential variables related to rockburst. A comprehensive database of 93 documented rockburst cases is compiled. Subsequently, a systematic method is established that involves the categorization of intensity levels through data visualization and factor analysis in order to identify a reduced number of unobservable underlying factors. Furthermore, K-means clustering is utilized to identify data patterns. The gradient boosting classifier is then employed to predict the intensity levels of rockburst. The results demonstrate that GenAI and prompt engineering offers an effective approach for accurately predicting rockburst events, achieving an accuracy rate of 89 percent. Through predictive modeling with GenAI, construction engineering experts can proactively evaluate the likelihood of rockburst, allowing for improved risk management, optimized excavation strategies, and enhanced safety protocols. This approach enables the automation of complex analyses and provides a powerful tool for real-time decision-making and predictive insights, offering significant benefits to industries reliant on underground construction. However, despite the considerable potential of GenAI and prompt engineering in the construction sector, challenges related to output accuracy, the dynamic nature of projects, and the need for human oversight must be carefully addressed to ensure effective implementation. Full article

The construction industry is inherently marked by high uncertainty levels driven by its complex processes. These relate to the bidding environment, resource availability, and complex project requirements. Accurate bid pricing under such uncertainty remains a critical challenge for contractors seeking a competitive advantage while managing risk exposure. This exploratory study integrates artificial intelligence (AI) into game theory models in an AI-assisted framework for bid pricing in construction. The proposed model addresses uncertainties from external market factors and adversarial behaviours in competitive bidding scenarios by leveraging AI’s predictive capabilities and game theory’s strategic decision-making principles; integrating extreme gradient boosting (XGBOOST) + hyperparameter tuning and Random Forest classifiers. The key findings show an increase of 5–10% in high-inflation periods with a high model accuracy of 87% and precision of 88.4%. AI can classify conservative (70%) and aggressive (30%) bidders through analysis, demonstrating the potential of this integrated approach to improve bid accuracy (cost estimates are generally within 10% of actual bid prices), optimise risk-sharing strategies, and enhance decision making in dynamic and competitive environments. The research extends the current body of knowledge with its potential to reshape bid-pricing strategies in construction in an integrated AI–game-theoretic model under uncertainty. Full article