Search Results (345)

The construction industry is moving towards the era of industry 4.0; 5.0 with Building Information Modelling (BIM) as the tool gaining significant traction owing to its inherent advantages such as enhancing construction design, process and data management. However, the integration of BIM presents risks that are often overlooked in project implementation. This study aims to develop a novel amalgamated dimensional factor (Techno-organizational Aspect) that is set out to identify and align appropriate management strategies to these risks. Firstly, it encompasses an in-depth analysis of BIM and risk management, through an integrative review approach. The study utilizes an exploratory-based review centered around journal articles and conference papers sourced from Scopus and Google Scholar. Then processed using NVivo 12 Pro software to categorise risks through thematic analysis, resulting in a comprehensive Risk Breakdown Structure (RBS). Then qualitative content analysis was employed to identify and develop management strategies. Further data collection via online survey was crucial for closing the research gap identified. The analysis by mixed method research enabled to determine the risk severity via the quantitative approach using SPSS (version 29), while the qualitative approach linked management strategies to the risk factors. The findings accentuate the crucial linkages of key strategies such as version control system that controls BIM data repository transactions to mitigate challenges controlling transactions in multi-model collaborative environment. The study extends into underexplored amalgamated domains (techno-organisational spectrum). Therefore, a significant contribution to bridging the existing research gap in understanding the intricate relationship between BIM implementation risks and effective management strategies. Full article

In order to address the pressing challenge posed by the proliferation of fake news in the digital age, we emphasize its profound and harmful impact on societal structures, including the misguidance of public opinion, the erosion of social trust, and the exacerbation of social polarization. Current fake news detection methods are largely limited to superficial text analysis or basic text–image integration, which face significant limitations in accurately identifying deceptive information. To bridge this gap, we propose the UC-CMAF framework, which comprehensively integrates news text, images, and user comments through an adaptive co-attention fusion mechanism. The UC-CMAF workflow consists of four key subprocesses: multimodal feature extraction, cross-modal adaptive collaborative attention fusion of news text and images, cross-modal attention fusion of user comments with news text and images, and finally, input of fusion features into a fake news detector. Specifically, we introduce multi-head cross-modal attention heatmaps and comment importance visualizations to provide interpretability support for the model’s predictions, revealing key semantic areas and user perspectives that influence judgments. Through the cross-modal adaptive collaborative attention mechanism, UC-CMAF achieves deep semantic alignment between news text and images and uses social signals from user comments to build an enhanced credibility evaluation path, offering a new paradigm for interpretable fake information detection. Experimental results demonstrate that UC-CMAF consistently outperforms 15 baseline models across two benchmark datasets, achieving F1 Scores of 0.894 and 0.909. These results validate the effectiveness of its adaptive cross-modal attention mechanism and the incorporation of user comments in enhancing both detection accuracy and interpretability. Full article

This survey provides a comprehensive review of the integration of large language models (LLMs) into autonomous robotic systems, organized around four key pillars: locomotion, navigation, manipulation, and voice-based interaction. We examine how LLMs enhance robotic autonomy by translating high-level natural language commands into low-level control signals, supporting semantic planning and enabling adaptive execution. Systems like SayTap improve gait stability through LLM-generated contact patterns, while TrustNavGPT achieves a 5.7% word error rate (WER) under noisy voice-guided conditions by modeling user uncertainty. Frameworks such as MapGPT, LLM-Planner, and 3D-LOTUS++ integrate multi-modal data—including vision, speech, and proprioception—for robust planning and real-time recovery. We also highlight the use of physics-informed neural networks (PINNs) to model object deformation and support precision in contact-rich manipulation tasks. To bridge the gap between simulation and real-world deployment, we synthesize best practices from benchmark datasets (e.g., RH20T, Open X-Embodiment) and training pipelines designed for one-shot imitation learning and cross-embodiment generalization. Additionally, we analyze deployment trade-offs across cloud, edge, and hybrid architectures, emphasizing latency, scalability, and privacy. The survey concludes with a multi-dimensional taxonomy and cross-domain synthesis, offering design insights and future directions for building intelligent, human-aligned robotic systems powered by LLMs. Full article

Adaptive reuse of Mediterranean earthen houses offers a unique opportunity to fuse heritage preservation with sustainable development. This study introduces a comprehensive, sustainability-driven framework that reimagines these vernacular structures as culturally rooted and socially inclusive assets for contemporary living. Moving beyond conventional restoration, the proposed framework integrates environmental, socio-cultural, and economic sustainability across six core dimensions: ecological performance and material conservation, respectful functional transformation, structural resilience, cultural continuity and community engagement, adaptive flexibility, and long-term economic viability. Four geographically and culturally diverse case studies—Alhambra in Spain, Ghadames in Libya, the UCCTEA Chamber of Architects Main Building in North Cyprus, and Sheikh Hilal Beehive Houses in Syria—serve as testbeds to examine how earthen heritage can be reactivated in sustainable and context-sensitive ways. Through qualitative analysis, including architectural surveys, visual documentation, and secondary data, the study identifies both embedded sustainable qualities and persistent barriers, such as structural fragility, regulatory constraints, and socio-economic disconnects. By synthesizing theoretical knowledge with real-world applications, the proposed framework offers a replicable model for policymakers, architects, and conservationists aiming to bridge tradition and innovation. This research highlights adaptive reuse as a practical and impactful strategy for extending the life of heritage buildings, enhancing environmental performance, and supporting community-centered cultural regeneration across the Mediterranean region. Full article

Civil structures carry significant service loads over long times but are prone to deterioration due to various natural impacts. Traditionally, these structures are inspected in situ by qualified engineers, a method that is high-cost, risky, time-consuming, and prone to error. Recently, researchers have explored innovative practices by using virtual reality (VR) technologies as inspection platforms. Despite such efforts, a critical question remains: can VR models accurately reflect real-world structural conditions? This study presents a comprehensive framework for assessing the visual fidelity of VR models for structural inspection. To make it viable, we first introduce a novel workflow that integrates UAV-based photogrammetry, computer graphics, and web-based VR editing to establish interactive VR user interfaces. We then propose a visual fidelity assessment methodology that quantitatively evaluates the accuracy of the VR models through image alignment, histogram matching, and pixel-level deviation mapping between rendered images from the VR models and UAV-captured images under matched viewpoints. The proposed frameworks are validated using two case studies: a historic stone arch bridge and a campus steel building. Overall, this study contributes to the growing body of knowledge on VR-based structural inspections, providing a foundation for our peers for their further research in this field. Full article

Despite growing interest in integrating Lean Construction (LC) and Building Information Modeling (BIM) to advance sustainability in the Architecture, Engineering, and Construction (AEC) industry, research remains fragmented and lacks a unified implementation framework. This study bridges this gap by conducting a systematic literature review (2010–2024) of 96 journal articles to (1) analyze research trends in BIM-LC integration; (2) evaluate its benefits for sustainable built environments; and (3) identify barriers to adoption. A key contribution is the development of a novel four-dimensional BIM-LC integration framework, encompassing information integration, supply chain management, waste management, and life cycle management, which synergizes LC principles with BIM’s technical capabilities to reduce waste, enhance resource efficiency, and support carbon neutrality goals. The findings reveal that while BIM-LC integration significantly improves construction productivity and reduces environmental impacts, technical challenges in data interoperability and fragmented lifecycle management persist. Actionable solutions are further proposed, including semantic model standardization, AI-driven supply chain resilience, and circular economy integration. This framework provides both scholars and practitioners with a roadmap to advance BIM-LC adoption for sustainable construction. Full article

The automation of smart building design processes remains a significant challenge, particularly in translating complex natural language requirements into structured design parameters within Computer-Aided Design (CAD) environments. Traditional design workflows rely heavily on manual input, which can be inefficient, error-prone, and time-consuming, limiting the integration of adaptive, real-time inputs. To address this issue, this study proposes an intelligent Natural Language Processing (NLP)-based workflow for automating the conversion of design briefs into CAD-readable parameters. This study proposes a five-step integration framework that utilizes NLP to extract key design requirements from unstructured inputs such as emails and textual descriptions. The framework then identifies optimal integration points—such as APIs, direct database connections, or plugin-based solutions—to ensure seamless adaptability across various CAD systems. The implementation of this workflow has the potential to enable the automation of routine design tasks, reducing the reliance on manual data entry and enhancing efficiency. The key findings demonstrate that the proposed NLP-based approach may significantly streamline the design process, minimize human intervention while maintaining accuracy and adaptability. By integrating NLP with CAD environments, this study contributes to advancing intelligent design automation, ultimately supporting more efficient, cost-effective, and scalable smart building development. These findings highlight the potential of NLP to bridge the gap between human input and machine-readable data, providing a transformative solution for the architectural and construction industries. Full article

Construction projects still face persistent barriers to adopting whole life costing (WLC), such as fragmented data, a lack of standardization, and inadequate tools. This study addresses these limitations by proposing a core ontology for WLC, developed using an ontology design science research methodology. The ontology formalizes WLC knowledge based on ISO 15686-5 and incorporates professional insights from surveys and expert focus groups. Implemented in web ontology language (OWL), it models cost categories, temporal aspects, and discounting logic in a machine-interpretable format. The ontology’s interoperability and extensibility are validated through its integration with the building topology ontology (BOT). Results show that the ontology effectively supports cost breakdown, time-based projections, and calculation of discounted values, offering a reusable structure for different project contexts. Practical validation was conducted using SQWRL queries and Python scripts for cost computation. The solution enables structured data integration and can support decision-making throughout the building life cycle. This work lays the foundation for future semantic web applications such as knowledge graphs, bridging the current technological gap and facilitating more informed and collaborative use of WLC in construction. Full article

Ensuring the trustworthiness of artificial intelligence (AI) systems is critical as they become increasingly integrated into domains like healthcare, finance, and public administration. This paper explores frameworks and metrics for evaluating AI trustworthiness, focusing on key principles such as fairness, transparency, privacy, and security. This study is guided by two central questions: how can trust in AI systems be systematically measured across the AI lifecycle, and what are the trade-offs involved when optimizing for different trustworthiness dimensions? By examining frameworks such as the NIST AI Risk Management Framework (AI RMF), the AI Trust Framework and Maturity Model (AI-TMM), and ISO/IEC standards, this study bridges theoretical insights with practical applications. We identify major risks across the AI lifecycle stages and outline various metrics to address challenges in system reliability, bias mitigation, and model explainability. This study includes a comparative analysis of existing standards and their application across industries to illustrate their effectiveness. Real-world case studies, including applications in healthcare, financial services, and autonomous systems, demonstrate approaches to applying trust metrics. The findings reveal that achieving trustworthiness involves navigating trade-offs between competing metrics, such as fairness versus efficiency or privacy versus transparency, and emphasizes the importance of interdisciplinary collaboration for robust AI governance. Emerging trends suggest the need for adaptive frameworks for AI trustworthiness that evolve alongside advancements in AI technologies. This paper contributes to the field by proposing a comprehensive review of existing frameworks with guidelines for building resilient, ethical, and transparent AI systems, ensuring their alignment with regulatory requirements and societal expectations. Full article

Preserving heritage sites is a complex challenge that requires multidisciplinary approaches, combining scientific accuracy with cultural and historical sensitivity. In alignment with UNESCO’s conservation guidelines, this study investigated the airflow dynamics and wind-induced structural effects within ancient architecture using advanced computational fluid dynamics (CFD). The study site was the Na Phra Meru Historical Temple in Ayutthaya, Thailand, where the shear stress transport $k-\omega$ turbulence model was applied to analyze distinctive airflow patterns. A high-precision 3D computational domain was developed using Faro focus laser scanning technology, with the CFD results being validated based on onsite experimental data. The findings provided critical insights into the temple’s ventilation behavior, revealing strong correlations between turbulence characteristics, wind speed, temperature, and relative humidity. Notably, the small slit windows generated complex flow mixing, producing a large internal recirculation zone spanning approximately 70% of the central interior space. In addition to airflow distribution, the study evaluated the aerodynamic forces and rotational moments acting on the structure based on five prevailing wind directions. Based on these results, winds from the east and northeast generated the highest aerodynamic loads and rotational stresses, particularly in the lateral and vertical directions. Overall, the findings highlighted the critical role of airflow and wind-induced forces in the deterioration and long-term stability of heritage buildings. The study demonstrated the value of integrating CFD, environmental data, and structural analysis to bridge the gap between conservation science and engineering practice. Future work will explore further the interactions between wall moisture and the multi-layered pigments in mural paintings to inform preservation practices. Full article

The demand for super-tall buildings and long-span bridges has driven concrete development toward higher strength and durability. Therefore, this study investigated the impact of composition of materials (aggregates, admixtures, and steel fibers) on the mechanical performance and economic feasibility of coarse aggregate reactive powder concrete (CA-RPC). The goal is to identify optimal combinations for both performance and cost. Scanning electron microscopy (SEM) and pore structure analysis were used to assess microstructural characteristics. The results demonstrated that replacing quartz sand with yellow sand as the fine aggregate in CA-RPC effectively reduced construction costs without compromising compressive strength. The use of basalt as the coarse aggregate led to higher mechanical strength compared to limestone. Incorporating 20% fly ash reduced the 7-day compressive strength, while the 28-day strength remained unaffected. The addition of 10% silica fume showed no obvious effect on the early strength but significantly improved the 28-day strength and workability of the concrete. Moreover, the incorporation of steel fibers improved the flexural strength and structural integrity of CA-RPC, shifting the failure mode from brittle fracture to a more ductile cracking behavior. SEM observations and pore structure analyses revealed that the admixtures altered the hydration products and pore distribution, thereby affecting the mechanical performance. This study provides valuable insights into the strength development and underlying mechanisms of CA-RPC, offering a theoretical basis for its practical application in bridge deck pavement and tunnels. Full article

Heortia vitessoides Moore (Lepidoptera: Pyralidae), the dominant outbreak defoliator of Aquilaria sinensis (Myrtales: Thymelaeaceae, the agarwood-producing tree), poses a severe threat to the sustainable development of the agarwood industry. Current research has preliminarily revealed its biological traits and gene functions. However, significant gaps persist in integrating climate adaptation mechanisms, control technologies, and host interaction networks across disciplines. This review systematically synthesizes the multidimensional mechanisms underlying H. vitessoides outbreaks through the logical framework of “Fundamental Biology of Outbreaks—Environmental Drivers—Control Strategies—Molecular Regulation—Host Defense.” First, we integrate the biological characteristics of H. vitessoides with its climatic response patterns, elucidating the ecological pathways through which temperature and humidity drive population outbreaks by regulating development duration and host resource availability. Subsequently, we assess the efficacy and limitations of existing control techniques (e.g., pheromone trapping, Beauveria bassiana application), highlighting the critical bottleneck of insufficient mechanistic understanding at the molecular level. Building on this, we delve into the molecular adaptation mechanisms of H. vitessoides. Specifically, detoxification genes (e.g., HvGSTs1) and temperature stress-responsive genes (e.g., HvCAT, HvGP) synergistically enhance stress tolerance, while chemosensory genes mediate mating and host location behaviors. Concurrently, we reveal the host defense strategy of A. sinensis, involving activation of secondary metabolite defenses via the jasmonic acid signaling pathway and emission of volatile organic compounds that attract natural enemies—an “induced resistance–natural enemy collaboration” mechanism. Finally, we propose future research directions: deep integration of gene editing to validate key targets, multi-omics analysis to decipher the host–pest–natural enemy interaction network, and development of climate–gene–population dynamics models. These approaches aim to achieve precision control by bridging molecular mechanisms with environmental regulation. This review not only provides innovative pathways for managing H. vitessoides but also establishes a paradigm for cross-scale research on pests affecting high-value economic forests. Full article

This study addresses the water basin effect in the underwater sand layer of steel pipe pile cofferdams by integrating the concept from building foundation pits to cofferdam foundation pit analysis. A theoretical derivation is presented for the deformation evolution of steel pipe piles and bottom seals within the cofferdam pit. The cofferdam construction dewatering process is divided into four stages: riverbed excavation for bottom sealing, dewatering to the second support, dewatering to the third support, and dewatering to final bottom sealing. The steel pipe piles are modeled as single-span or multi-span cantilever continuous beam structures. Using the superposition principle, deformation evolution equations for these statically indeterminate structures across the four stages are derived. The bottom seal is simplified to a single-span end-fixed beam, and its deflection curve equation under uniform load and end-fixed additional load is obtained via the same principle. A case study based on the 6# pier steel pipe pile cofferdam of Xi’an Metro Line 10 Jingwei Bridge rail-road project employs FLAC3D for hydrological–mechanical coupling analysis of the entire dewatering process to validate the water basin effect. Results reveal a unique water basin effect in cofferdam foundation pits. Consistent horizontal deformation patterns of steel pipe piles occur across all working conditions, with maximum horizontal displacement (20.72 mm) observed at 14 m below the pile top during main pier construction completion. Close agreements are found among theoretical, numerical, and monitored deformation results for both steel pipe piles and bottom seals. Proper utilization of the formed water basin effect can effectively enhance cofferdam stability. These findings offer insights for similar engineering applications. Full article

This study investigates the mechanisms underlying the diffusion of risk information about genetically modified organisms (GMOs) on the Chinese social media platform Weibo. Drawing upon social contagion theory, we examine how endogenous and exogenous mechanisms shape users’ information-sharing behaviors. An analysis of 388,722 reposts from 2444 original GMO risk-related texts enabled the construction of a comprehensive sharing network, with computational text-mining techniques employed to detect users’ attitudes toward GMOs. To bridge the gap between descriptive and inferential network analysis, we employ a Shannon entropy-based approach to quantify the uncertainty and concentration of attitudinal differences and similarities among sharing and non-sharing dyads, providing an information-theoretic foundation for understanding positional and differential homophily. The entropy-based analysis reveals that information-sharing ties are characterized by lower entropy in attitude differences, indicating greater attitudinal alignment among sharing users, especially among GMO opponents. Building on these findings, the Exponential Random Graph Model (ERGM) further demonstrates that both endogenous network mechanisms (reciprocity, preferential attachment, and triadic closure) and positional homophily influence GMO risk information sharing and dissemination. A key finding is the presence of a differential homophily effect, where GMO opponents exhibit stronger homophilic tendencies than non-opponents. Despite the prevalence of homophily, this paper uncovers substantial cross-attitude interactions, challenging simplistic notions of echo chambers in GMO risk communication. By integrating entropy and ERGM analyses, this study advances a more nuanced, information-theoretic understanding of how digital platforms mediate public perceptions and debates surrounding controversial socio-scientific issues, offering valuable implications for developing effective risk communication strategies in increasingly polarized online spaces. Full article

Why should we prepare for a flood which might never happen? Uncertainty around potential future hazards significantly limits citizens’ disaster preparedness, as it influences decision-making and action-taking greatly. To bridge this knowledge–action gap, we developed a novel, no-regrets framework for sustainable flood preparedness under uncertainty, building on a systematic literature review (PRISMA method) and an integrative review of preparedness actions. The review of 364 articles revealed that while no-regrets principles are widely applied in climate policy and risk management, they are not tailored to personal preparedness. Our resulting framework defines clear no-regrets criteria for individual and household-level preparedness (robustness, flexibility, cost-effectiveness, co-benefits, and ease of implementation) and categorizes 80+ flood preparedness actions according to four levels of uncertainty, from unknown futures to imminent hazards. Notably, we found that long-term preparedness actions remain underutilized, psychological preparedness is largely absent, and existing guidance is biased toward physical risk reduction in high-income contexts. This framework offers a practical tool for practitioners, local authorities, and community groups to promote actionable, context-sensitive flood preparedness worldwide and can be adapted to other hazards in future work. Full article