Search Results (18,718)

Building Energy Modelling (BEM) for existing buildings is constrained by the lack of reliable as-built Building Information Models (BIMs) and persistent BIM-to-BEM interoperability problems. This study proposes a semantic scan-to-Industry Foundation Classes (IFC) workflow that converts mobile indoor scans into a simplified IFC model for BEM preprocessing. Apple RoomPlan captures room-scale building elements, which are exported as JSON and converted into IFC 4×3 ADD2 using a Python-based converter. To address partial scans, the workflow generates closed analytical volumes, inferred walls and ceiling slabs, and metadata distinguishing measured from reconstructed geometry. It then automatically generates IfcSpace entities and IfcRelSpaceBoundary2ndLevel relationships. The workflow was evaluated using a historic university building. For the selected case-study area, processing from mobile scanning to initial VICUS Buildings import required 21 min, excluding subsequent manual verification of boundary conditions and assignment of thermophysical properties. Under identical construction stratigraphies and usage profiles, the scan-derived model produced a total heating-season demand 5.8% higher than the Revit reference model. These results indicate that partial semantic indoor scans can support the rapid preparation of structured IFC models for preliminary BEM applications. Full article

Partnership development has become increasingly important in Design–Build government building projects due to the need for effective collaboration among project stakeholders. However, limited studies have examined how partnership maturity mediates the relationship between collaborative factors and project performance. This study investigates the effects of relationship factors, process factors, and environmental factors on partnership maturity and project performance in Design–Build government building projects. A mixed-methods approach was employed, combining a questionnaire survey of 214 professionals involved in Design–Build projects with Structural Equation Modelling–Partial Least Squares (SEM-PLS) analysis and expert validation through Focus Group Discussions. The results indicate that relationship factors, process factors, and environmental factors significantly influence partnership maturity, with process factors exerting the strongest effect (β = 0.403). Relationship factors demonstrated the strongest direct influence on project performance (β = 0.376), followed by partnership maturity (β = 0.208), process factors (β = 0.202), and environmental factors (β = 0.167). The model exhibited strong explanatory power, with R² values of 0.851 for partnership maturity and 0.800 for project performance. The mediation analysis revealed that partnership maturity significantly mediated only the relationship between process factors and project performance. The study contributes to construction management literature by introducing partnership maturity as a selective mediating mechanism in Design–Build projects and provides practical guidance for strengthening collaborative processes to improve project performance in government building projects. Full article

For surface crack detection in in-service concrete buildings, this study proposes DCFYOLO, a lightweight crack instance segmentation algorithm improved from YOLO11n-seg, which enhances feature representation and multi-scale contextual fusion to improve crack detection and segmentation performance in complex scenarios. Frequency Channel Attention (FCA) is introduced into the C3k2 units of the backbone network, where multi-scale pooling extracts multi-spectral information to enhance channel-level feature discrimination. Context Anchor Attention (CAA) is added to the PAN-FPN structure in the neck to address multi-scale feature fusion and long-range context modeling in complex scenes. A Dynamic Snake Convolution with Efficient Channel Attention (DSECA) module is constructed by combining dynamic snake convolution and efficient channel attention. Through a serial design, this module provides the multi-scale feature fusion process with both geometrically adaptive sampling and channel-discriminative optimization. Experimental results on the DeepCrack dataset show that DCFYOLO achieves 73.2% Box mAP@0.5 and 67.4% Mask mAP@0.5 with 2.47 M parameters, improving the baseline YOLO11n-seg by 2.8 and 2.7 percentage points, respectively. Ablation experiments verify the independent contribution of each improved module. The algorithm demonstrates a balance between segmentation accuracy and inference efficiency under lightweight constraints on the DeepCrack dataset, and can provide a reference for research on concrete surface crack instance segmentation. Full article

Simulation-dependent energy analysis has long dominated building retrofit research, yet this paradigm presents substantial barriers for non-expert building owners who lack technical software proficiency and detailed building documentation-a challenge compounded by the “curse of dimensionality” when multivariate analysis requires thousands of samples beyond available empirical records. Leveraging retrofit data accumulated through Korea’s Green Remodeling programs since 2017, this study proposes a Copula-Genetic Algorithm (Copula-GA) integrated framework that enables rational retrofit decision-making with minimal user inputs (construction year, floor area, structural type). From 178 documented retrofit cases, Gaussian copula-based multivariate sampling generated 10,000 synthetic records while preserving inter-variable dependency structures. Building physics constraints addressing vintage-thermal performance and capacity-efficiency relationships filtered implausible combinations, yielding 9898 valid cases with correlation matrix fidelity confirmed by a Frobenius norm deviation of 0.043. Evolutionary clustering employing a composite fitness function of Silhouette coefficient (0.68) and Davies-Bouldin Index (0.52) identified K = 16 as the optimal partition, categorizing outcomes into four reference model archetypes: Lightweight Structure (Type A, 27.0% reduction, 15.7-year payback), Masonry Structure (Type B, 29.0%, 14.8 years), RC Structure (Type C, 30.7%, 13.4 years), and Mixed Structure (Type D, 30.9%, 13.1 years). The proposed Copula-GA framework bridges the gap between advanced energy optimization methodologies and practical accessibility for non-expert building owners. By transforming limited empirical samples into reliable reference models, this research supports building-sector decarbonization. Using three minimal inputs, a building can be matched to one of the 16 standard models to obtain its expected saving rate, payback period, and recommended measures without detailed simulation. Full article

Rapid and reliable prediction of embodied carbon emissions is essential for supporting sustainable design decision-making and reducing the environmental impacts of building engineering projects. However, existing studies have mainly focused on single buildings, with limited attention to project-level prediction and variations in information availability across design stages. To address this gap, this study developed a machine learning framework for project-level embodied carbon prediction based on a dataset of 78 projects involving 426 individual buildings. Using project attributes, scale indicators, structural characteristics, material quantities, and construction-related information, nine machine learning models were developed for the schematic design stage and the construction drawing design stage. Two residual-corrected weighted ensemble models were further introduced to improve predictive performance. The results show that the Extra Trees–KNN residual-corrected weighted ensemble model achieved the best performance at the construction drawing design stage, with a test-set R² of 0.949. SHAP analysis further revealed a stage-dependent shift in dominant drivers: gross floor area and land area dominated at the schematic design stage, whereas concrete and reinforcement quantities became the leading predictors at the construction drawing design stage. The proposed framework provides interpretable and stage-specific quantitative support for low-carbon design decision-making, thereby facilitating embodied carbon reduction and the transition toward a more sustainable built environment. Full article

The adaptive reuse of cultural heritage buildings is an important approach that ensures the sustainable preservation of these structures; however, determining appropriate functions requires a systematic evaluation of user preferences. The aim of this study is to identify user preferences regarding the adaptive reuse of cultural heritage buildings and to develop an artificial intelligence-based decision-support model. The evaluation criteria identified through a literature review were validated through a two-round Delphi process involving 30 experts. The final framework consisted of six criteria: social and cultural value, historical value, authenticity value, construction technique, environmental value, and architectural and aesthetic value. Based on these criteria, a survey was conducted on the case of the Salih Sefa Yazar Mansion in Osmaniye, in which 886 participants were reached, and after data cleaning, 844 valid responses were retained to develop a model based on the Random Forest algorithm. The findings indicate that users prioritize social and cultural values and prefer functions that support public use. The model successfully predicted adaptive reuse alternatives such as museums, Art Gallery and cultural house, libraries, cafe-restaurants, and accommodation by evaluating demographic characteristics together with criterion priorities. In addition, scenario-based predictions were conducted using sample user profiles. In conclusion, the study proposes a decision-support approach that integrates expert opinion, user preferences, and artificial intelligence. Full article

Over the past two decades, triphenylamine (TPA)-based supramolecular coordination metallacycles, constructed by incorporating TPA and its derivatives as key building blocks into two-dimensional (2D) coordination-driven assemblies, have gradually received considerable attention and emerged as an important research topic within the field of discrete supramolecular coordination complexes (SCCs). Leveraging the synthetic accessibility and outstanding optoelectronic properties of TPA units, the resulting TPA-based metallacycles exhibit well-defined topological structures, excellent emission characteristics, and unique functions. In this review, we systematically and comprehensively discuss the design strategies, synthetic methodologies, and practical applications for TPA-based supramolecular metallacycles, with an emphasis on five key aspects: (i) coordination-driven self-assembly, (ii) hierarchical self-assembly, (iii) supramolecular polymers, (iv) tunable fluorescence, and (v) diverse applications. Finally, future perspectives and challenges in this rapidly evolving field are presented. Full article

This research concentrates on the electric vehicle (EV) industry in China and Japan. China is the largest and Japan the third-largest auto production country after the U.S. This study explores the current and future transition to battery electric vehicles and hybrid electric vehicles in Japan and China. Three Japanese auto companies, Toyota, Honda, and Nissan, and BYD from China are studied in this research. Toyota, Honda, and Nissan are actively pursuing the development of hybrid electric vehicles in Japan. Meanwhile, the research examines the Chinese EV company BYD, which is a major global competitor in the EV industry. This study compares the companies in terms of their strategies, strengths, weaknesses, and export destinations and delineates their competitive strategies and outlooks. In addition, the study examines the elements of the supply chain needed for building EVs, such as lithium, nickel, and cobalt. Furthermore, this research discusses some of the issues with EVs, such as the challenges related to the production and recycling of batteries and the implications as far as green and sustainable practices regarding EVs in the selected countries are concerned. The final part of this research explores how the production of EVs can affect the global reduction of carbon emissions. The findings of this study indicate that the transition to EVs depends on the structural position as far as the supply chain, the manufacturing of electric batteries, charging stations, and the size of the operations are concerned. The results indicate that BYD is in a stronger position in terms of the infrastructure necessary for the production of EVs. Meanwhile, Japanese auto companies are focused on hybrid EVs due to infrastructure related to EV batteries, supply sources, and charging stations. In addition, this research provides informative insights into the future of electric vehicles in the global market. The study offers recommendations for a comprehensive approach that integrates national policies, technological innovation, and the environmental impact of the transition to electric vehicles on a global scale. Full article

The paradigm of sustainable development, coupled with contemporary inclinations towards green construction, exerts a considerable effect on the evolution of environmentally benign building composites. The main aim of this study is to create geopolymer composites with improved physical and mechanical properties. An examination of the properties of a geopolymer composite (GS), using ground granulated blast furnace slag (GGBFS) and plant fibers, is presented in this paper. Sisal (SF) and flax (FF) fibers, along with their combination (SF + FF), were integrated into the slag at concentrations of 0%, 0.5%, 1.0%, 1.5%, and 2% by weight. Before use, plant fibers were treated with a 5% NaOH solution. The geopolymer composites (GC) underwent evaluation for their density, compressive and flexural strengths, and water absorption characteristics. Scanning electron microscopy was employed to examine the fracture characteristics of the GC. The compressive and flexural strengths of GC were improved by including 1% SF, FF, and their combination, 0.5% SF + 0.5% FF. Compressive strength increases were 11.5%, 8.6%, and 14.5%, while flexural strength increases were 17.4%, 13%, and 19.6%, respectively. Water absorption of GC with 1% SF, FF, and SF + FF decreased by 14.6%, 10.8%, and 20.4%, respectively. The apparent synergistic performance of hybrid sisal-flax reinforcement at a total fiber content of 1% was revealed. GCs at the fracture have a homogeneous rough structure with microcracks and accumulations of geopolymer reaction products. The matrix-plant fiber interface in GC is identified by a rounded region with elongated fibers, indicating the fiber’s performance under mechanical stress. The findings of this investigation suggest the feasibility of utilizing plant fibers in environmentally sound geopolymer construction composites. Full article

The growing complexity of public policies and socio-ecological transitions demands evaluation tools that integrate environmental, economic, and social dimensions across multiple governance contexts. However, the main evaluation impact methods are fragmented and sector-specific, limiting their ability to capture cross-scale interdependencies and sustainability of trade-offs. While existing reviews address methods in isolation, no updated systematic framework is built upon joint epistemological and participatory dimensions, proposing an integrated and transformative framework. Through a common analytical framework—encompassing the type of impact assessed, territorial scale, methodological approach, degree of stakeholder participation, and operational complexity—the paper presents a systematic comparative review of eleven widely used evaluation methods, covering the environmental, economic, social, and multidimensional dimensions. These methods are selected through screening of peer-reviewed literature published between 2022 and 2025 using an AI-assisted text-mining protocol to support keyword extraction, methodological classification, and manual validation. Its focus is on the analysis and critique of their characteristics, complexity, and epistemological limitations, particularly focusing on measurability and participation. The comparative analysis reveals a structural epistemological gap between qualitative bottom-up perspectives and quantitative, top-down approaches, as well as structural fragmentation across evaluative dimensions. They also draw attention to the approaches’ inadequate ability to manage non-linear dynamics, uncertainty, and transformative scenarios, which affects the legitimacy, comparability, and general efficacy of decision-making processes. Building on these, the paper advances a paradigmatic shift toward three systemic integrated principles: (i) systemic evaluation grounded in methodological and epistemological integration; (ii) transformative evaluation oriented toward collective learning and the redefinition of development trajectories; and (iii) co-produced evaluation based on the active engagement of territorial actors. Full article

The BeiDou-3 Navigation Satellite System (BDS-3) broadcasts multi-frequency signals, including B1C, B2a, B1I, and B3I, offering a new technical approach for tilt monitoring of building structural safety. However, in building structural safety tilt monitoring based on the BDS-3 single-epoch algorithm, the engineering performance differences between the B1C/B2a new signal combination and the B1I/B3I traditional signal combination—in terms of monitoring accuracy, ambiguity fixing rate, computational efficiency, and tilt rate—have yet to be fully validated through comparative analysis. To address this issue, this paper proposes a building structural safety tilt monitoring method based on the BDS-3 single-epoch algorithm and conducts a field test on a multi-story building in Beijing. First, a BDS-3-based kinematic monitoring model is established, and an integer ambiguity error search band method based on the main and auxiliary frequencies is proposed. On this basis, three schemes are designed using medium Earth orbit (MEO), inclined geosynchronous orbit (IGSO), and geostationary Earth orbit (GEO) satellites, B1C/B2a (MEO/IGSO), B1I/B3I (MEO/IGSO), and B1I/B3I (MEO/IGSO/GEO), to comparatively analyze the accuracy, ambiguity fixing rate, computational efficiency, and measured tilt results of each scheme in building structural safety tilt monitoring. Experimental results show that all three schemes based on the BDS-3 single-epoch algorithm achieve millimeter-level monitoring accuracy and an ambiguity fixing rate exceeding 99%, with average computational times of 0.351 s, 0.338 s, and 4.572 s and corresponding building tilt rates of 0.22‰, 0.20‰, and 0.18‰, respectively, yielding an average tilt rate of 0.20‰. These results satisfy the 4‰ limit specified in the Code for Deformation Measurement of Building and Structure (JGJ 8-2016), thereby confirming the feasibility and effectiveness of the proposed method and offering a novel BDS-3 single-epoch algorithm for building tilt monitoring. Full article

Sustainable Development Goal 11 makes vertical intensification a primary way to reconcile urban growth with finite land, but its land-saving promise depends on inter-building connectivity, and that is what conventional land administration cannot govern. Organized around two-dimensional cadastres and horizontally bounded property rights, existing frameworks cannot handle the overlapping volumetric claims, shared liabilities, and cross-boundary access obligations that connected vertical development generates. We treat this as a land governance problem, using the term block city for an urban configuration coordinated across vertical levels. Combining a framework grounded in socio-technical transitions and urban governance with comparative analysis of Singapore, Hong Kong, Japan, and Buenos Aires, and a regulatory analysis of Chile, we show that the degree of vertical integration tracks the structure of land tenure: a coordinating land-rights condition is necessary, and a registrable volumetric right alone is not sufficient, as Buenos Aires shows. In Chile the barrier is institutional rather than technical, and the same gaps leave SDG Indicator 11.3.1 blind to the land that vertical intensification saves. The same reforms that would enable inter-building connectivity would make its sustainability contribution measurable. The block city is best understood not as a design typology but as a contingent institutional trajectory, whose realization depends on whether land governance can manage interdependence across property boundaries. Full article

Global stocktakes reveal that studies on natural hazards triggering technological accidents (Natech) in the context of climate change are limited in Southeast Asia, which is characterized by rapid industrial growth and high exposure to extreme weather. This study assesses the status of Natech hazards, including their consideration in national climate change adaptation policies, and translation to local stakeholders for disaster risk reduction using a case study in Malaysia. A qualitative approach was deployed, combining document analysis and semi-structured interviews in the case study area. The findings reveal that all 11 countries in Southeast Asia are susceptible to Natech hazards; however, only Malaysia, Timor-Leste (East Timor), and Vietnam mention them explicitly in their respective National Communications to the United Nations Framework Convention on Climate Change (UNFCCC), while Indonesia, Singapore, and Thailand reference them indirectly. The results of the interviews (n = 35) in IKS Kuala Selangor, Malaysia, indicate that, despite 30% of stakeholders having been directly affected by floods, fewer than 10% are prepared for Natech hazards. Potential policy entry points for mainstreaming Natech hazards include linking them to regulations on Environmental Impact Assessments and building codes for new projects, plans for climate proofing existing structures, and means for capacity enhancement. Full article

Autonomous systems must demonstrate reliable safety and robustness even under rare, safety-critical conditions. Because the vast range of possible operating situations cannot be exhaustively tested, scenario-based testing has emerged as a structured approach to expose systems to representative and challenging situations. Search-based scenario selection methods (SBSSMs) algorithmically explore the scenario space to identify critical and informative cases. Yet, existing implementations are often tightly coupled to specific domains and simulation environments, hindering reproducibility, comparability, and generalizability across application areas. This paper introduces a benchmarking framework that addresses these limitations by providing a domain-independent environment for the systematic evaluation of SBSSMs. Building on empirical observations from maritime simulation studies and the literature, the framework generates synthetic test instances that mimic the structural patterns found there while abstracting away application-specific semantics. It supplies standardized performance metrics and reference implementations of key method classes, enabling direct comparison of new approaches with established ones under controlled, repeatable conditions. Illustrative demonstrations show how the benchmark reveals characteristic strengths and weaknesses of different methods across diverse structural settings. As a reproducible and extensible baseline, the framework promotes standardized performance assessment and the transfer of methodological advances from maritime navigation to domains such as automotive safety. Full article

The rapid adoption of Industry 4.0 technologies has led to a substantial growth of sensors and connectivity in manufacturing systems, resulting in the generation of high-dimensional, memory-heavy datasets. Despite this abundance of data, many manufacturers struggle with data overload, poor utilization, and fragmented data infrastructures, which hinder the deployment of advanced analytics and trustworthy AI. As the sector transitions toward Industry 5.0, with its emphasis on human-centric, resilient, and sustainable manufacturing, the challenge is no longer how to collect more data, but how to identify and exploit the minimum set of data that meaningfully supports decision-making. This paper addresses the central research question: Is there a generalized framework to systematically identify and extract the minimum “smart data” required for specific manufacturing performance indicators? Through a structured review of recent academic and industrial literature, the paper evaluates emerging concepts including smart data, targeted data, minimum effective datasets, AI-driven feature selection, edge-based data filtering, and human-in-the-loop analytics. Building on these insights, the paper proposes a generalized, systematic framework grounded in the Hoshin planning principle. The framework links strategic manufacturing objectives to operational metrics and data requirements, ensuring consistent, goal-aligned data minimization across all organizational levels. The key takeaway is that a principled shift from indiscriminate data accumulation to minimum effective data enables improved model performance, greater trust and interpretability, reduced system complexity, and enhanced human-centric decision-making. The proposed framework aims to close a critical gap between fragmented research and practical industrial application, offering a scalable foundation for next-generation smart manufacturing. Full article