Search Results (7,335)

Building damage and collapse are the main sources of serious casualties and financial losses during earthquakes, which are among the most destructive natural disasters that endanger human life and property. Therefore, quick and precise post-earthquake building damage assessment is essential for risk assessment and emergency action. Convolutional neural networks (CNNs) primarily concentrate on local features and frequently ignore global contextual information within and across buildings, despite the fact that deep learning-based techniques allow automated damage identification. Transformer-based approaches, on the other hand, are good at capturing global dependencies, but their large memory and processing costs restrict their usefulness. As a result, existing networks still struggle to achieve an effective balance between accuracy and efficiency. To address this issue, this study proposes a lightweight and efficient network for post-earthquake building damage assessment. Specifically, we develop a two-stage method based on EfficientViM with an encoder–decoder architecture. In the encoder, Mamba is introduced to extract multi-scale change features with long-range dependencies, leveraging the state space model to preserve global modeling capability while significantly reducing computational complexity. In the decoder, two lightweight modules are designed to further enhance discriminative capability and computational efficiency. The network finally outputs building localization and pixel-level building damage, respectively. Experiments were conducted on four earthquake events from the BRIGHT dataset using a three-for-training and one-for-testing cross-event rotation evaluation strategy. The results demonstrate that LEViM-Net requires only 30.94 M parameters and 27.10 G FLOPs. In addition, for the Türkiye earthquake event, the proposed method achieves an F1 score of 80.49%, an overall accuracy (OA) of 88.17%, and a mean intersection over union (mIoU) of 49.73%. The proposed model enables efficient remote-sensing-based mapping of macroscopic and image-visible building damage, providing timely support for early-stage emergency response. Full article

In recent decades, the introduction of e-procurement has profoundly transformed the methods of procuring goods, services, and works, redefining traditional procurement processes and significantly impacting global economic, operational, and regulatory dynamics. The construction sector has also been affected by this transition, which has altered the operating models of public procurement and favoured the adoption of digital tools aimed at more efficient, transparent, and automated process management. This study proposes a systematic literature review based on the analysis of 95 scientific contributions, with the aim of outlining the evolution of the e-procurement paradigm in the construction sector and identifying the main directions for research development. Despite the widespread dissemination of studies on the topic, it emerges that the actual maturity of e-procurement systems is still limited, often resulting in a logic of document dematerialization rather than full process digitalization. In this context, the review critically analyses the role of Building Information Modelling as an enabling factor for the evolution of e-procurement, exploring the potential of its integration into procurement flows. Particular attention is paid to the contribution of the Digital Building Logbook, an information tool capable of extending the value of data generated during the tender phase throughout the building’s entire life cycle, supporting advanced management and maintenance strategies. The results highlight how, despite the significant potential of integrating e-procurement and BIM, significant technological, regulatory, and cultural issues persist that limit its large-scale adoption. This underscores the need to develop shared and interoperable methodological approaches capable of transforming procurement from a document-based process to an integrated information system, oriented toward value creation throughout the entire life cycle of projects. Full article

Healthy building concepts are increasingly recognized as important for improving occupant health and well-being, yet empirical evidence on their understanding and implementation in sub-Saharan African contexts remains limited. This study provides an exploratory assessment of construction professionals’ awareness and self-reported application of healthy building concepts in Gabon. Using a structured questionnaire survey of 45 construction professionals, including architects, engineers, and contractors, the study examines sources of awareness, patterns of application across project stages, and health-related dimensions prioritized in practice. The results indicate high levels of conceptual awareness within the surveyed group, but uneven and context-dependent application. Implementation is strongly concentrated at the design stage, while continuity during construction and operation remains limited. Professionals tend to prioritize tangible and measurable dimensions such as lighting, materials, air quality, and thermal comfort, whereas psychosocial and community-related aspects receive less attention. Based on these empirical patterns, the study proposes an empirically informed and context-sensitive framework structured around six strategic pillars to support the gradual integration of healthy construction practices in Gabon. Rather than offering a prescriptive model, the framework serves as an analytical reference to inform future research, professional capacity building, and policy dialog. Given the exploratory nature of the study and its reliance on self-reported data, the findings should be interpreted as indicative rather than generalizable. Full article

Simulation technologies are widely used in the construction industry to analyze complex operations and evaluate project performance before physical construction begins. However, interpreting simulation outputs remains challenging due to the dynamic nature of construction activities and the difficulty of representing spatial and temporal changes using traditional numerical or textual outputs. To address these limitations, researchers increasingly integrate visualization technologies with construction simulation models to improve understanding, communication, and decision-making. Using the PRISMA methodology, this paper presents a systematic literature review of visualization technology applications in construction simulation during the building phase. A total of 118 relevant publications published between 2000 and 2023 are reviewed and analyzed. The findings reveal a strong relationship between visualization technologies and Building Information Modeling (BIM), Virtual Reality (VR), and game engine technologies. Autodesk Navisworks and Unity are identified as the most frequently used visualization platforms, with game engines showing increasing adoption in recent years due to their support for immersive and interactive environments. The reviewed studies are further categorized into six primary use cases: scheduling and planning, education and training, equipment management, safety management, workspace planning, and simulation validation and verification. The results also demonstrate increasing research interest in real-time visualization, AR/VR integration, and interactive simulation environments. Overall, the findings highlight the growing role of visualization technologies in improving construction project planning, communication, training, safety, and decision-making, while also identifying important future research directions related to interoperability, real-time interaction, and extensible visualization platforms. Full article

The National Register of Historic Places (NR) is a structured artifact of meaning-making that encodes disciplinary values linking architectural and cultural significance to wealth and stylistic distinction. In doing so, it systematically underrepresents vernacular, working-class, and the built environments of racially and ethnically marginalized communities. This paper uses artificial intelligence (AI) to examine how that meaning is constructed. We analyze the preservation record across three scales: a national dataset of 100,117 NR listings (1966–2025), a state-level profile of Illinois’s 1997 NR listings, and a close analysis of Lake Forest, Illinois, a community whose exceptional concentration of NR-listed estate architecture makes it an ideal site for examining how preservation significance has been defined and what it excludes. Two parallel AI methods are applied to eighteen Lake Forest nomination documents and their associated photographs. Natural Language Processing (NLP) analyzes nomination text to trace how preservation professionals connect buildings to cultural value; blind AI image analysis examines the same properties to assess how a model trained on cultural imagery constructs visual meaning independently. NLP analysis reveals a corpus dominated by architectural description, with social history, landscape, and labor systematically underrepresented. The visual analysis confirms and amplifies the nomination record’s class-based assumptions while reproducing the same omissions regarding labor, diversity, and community context. These findings inform debates about AI’s potential to audit existing listings and support nominations for underrepresented property types, while showing that without deliberate corrective design and policy reform, such tools are as likely to replicate the preservation system’s inequities as to repair them. Full article

Accurate and timely information on forest disturbance drivers is important for sustainable forest management, global carbon cycle accounting, and climate change response. However, forest disturbance classification is difficult due to two major challenges: limited labeled samples and highly imbalanced disturbance class distribution. In this article, a new framework for multi-type forest disturbance classification based on collaborative semi-supervised learning and sample generation was proposed. First, forest disturbance is detected using long-term remote sensing time series data and disturbance detection algorithms. Spatiotemporal, spectral and terrain features of different disturbance types are extracted. On this basis, to address the problem of imbalanced and small-sample conditions, a collaborative classification strategy is developed. Based on a small number of labeled samples, Support Vector Machine (SVM) and Random Forest (RF) are used to build dual base classifiers. A confident learning (CL) framework is applied to select high-confidence pseudo-labeled samples from unlabeled data. Then, a latent diffusion model (LDM) is introduced to generate high-fidelity pseudo-samples. This increases the sample size and balances the class distribution. Based on the augmented dataset, the dual classifiers are iteratively optimized using a co-training strategy, which improves model generalization under complex conditions. The results show that the proposed framework could generate high-quality pseudo-samples and effectively reduce class imbalance. The overall accuracy (OA) of the proposed framework reaches 93.2%, which is 5.7% and 4.4% higher than single classifier baselines, respectively. After introducing the LDM-based balancing mechanism, performance is further improved by 1.8% compared with the pure semi-supervised framework. This study provides an efficient and reliable solution for large-scale forest ecosystem monitoring. Full article

This study evaluates the adoption and efficacy of the 5th Dimension Building Information Modelling (5D-BIM) as a cost dimension for mega rail projects, extending the discussion beyond just technological implementation to consider broader policy and practical implications. The purpose of this article is to understand the governance context of 5D-BIM implementation for rail and transport projects and evaluate the effectiveness of the 5D-BIM framework as currently applied by conducting semi-structured interviews with key stakeholders. Drawing on semi-structured interviews with 22 stakeholders across government, industry, and technology providers, the research examines current 5D-BIM practices. While the primary focus of the research is 5D BIM implementations within the state of Victoria, Australia, which is currently experiencing a surge in rail projects, interviews were also conducted with additional stakeholders from international rail projects for context. The findings reveal fragmented adoption, varying levels of organisational maturity, and significant policy and implementation gaps, particularly in the role of government as the primary client of transport infrastructure. The results of the interviews emphasise the centrality of government and regulatory context in driving the adoption and implementation of 5D-BIM as the primary client of transportation infrastructure and identify actionable recommendations for policymakers and practitioners towards a more integrated approach to 5D-BIM in mega rail projects. While 5D-BIM demonstrates clear benefits in enhancing cost estimation, coordination, and decision-making, its effectiveness is constrained by the absence of clear standards, limited BIM literacy, and inconsistent regulatory guidance. This study provides one of the first empirical validations of the 5D-BIM governance framework, demonstrating that its success is driven less by technological capability and more by policy alignment, standardisation, and institutional leadership. Full article

Medium-resolution cryo-electron microscopy (cryo-EM) density maps preserve substantial information about protein secondary-structure organization; however, accurately recovering the topology and connectivity of α-helices and β-strands remains challenging due to noise, structural heterogeneity, and the intrinsic resolution limitations that obscure residue-level detail. Topology determination is a key intermediate step toward building atomic protein models from medium-resolution cryo-EM density maps. It requires identifying the correct correspondence and orientation between secondary-structure elements (SSEs), i.e., α-helices and β-strands, predicted from the amino-acid sequence and those detected in the three dimensional (3D) density map. Despite significant advances in cryo-EM reconstruction and molecular modelling, this correspondence problem remains a challenging task, particularly in the presence of noisy density maps and in large, topologically complex α/β proteins. To address this issue, we propose a fully automated, classification-based framework that infers protein secondary-structure topology directly from medium-resolution cryo-EM density maps. Specifically, we cast topology determination as a supervised classification problem in three-dimensional space, leveraging geometric learning on model-derived Cα coordinate representations to establish SSE correspondences, and a Dynamic Time Warping (DTW)-based procedure to resolve density-stick directionality. Validation on a benchmark of 38 proteins spanning both simulated and experimental cryo-EM maps and covering diverse fold classes (α, β, and α/β) demonstrates strong and consistent performance. Among the evaluated predictors, the Voronoi (1-NN) classifier achieves the highest average correspondence quality, with a mean F1-score of 96.82% across the full benchmark. The framework also scales to large, topologically dense targets containing up to 65 secondary-structure elements while preserving very fast correspondence inference (<3 ms), offering a substantial improvement over prior baselines in both accuracy and computational cost. Overall, the classification-driven strategy provides reliable SSE-to-density matching and, when coupled with DTW-based direction selection, yields stronger topology constraints that directly support model building and refinement from medium-resolution cryo-EM reconstructions, while remaining easy to integrate into existing structural interpretation pipelines. Full article

Despite the focus on social justice, social work education is still heavily rooted in hierarchy and harmful educational practices. This conceptual and practice-informed article aims to highlight the deconstruction of educational hierarchy within the classroom through a justice lens, with equitable intention, and storytelling as meaningful discourse in social work education. These authors intend to deconstruct power dynamics, dismantle harmful assumptions, and encourage the unlearning of systemic and oppressive methods with the integration of clinical social work experience, useful decolonized classroom practices, and narrative pedagogy. The practice of storytelling can be healing, aid in building community, and also offer a collective learning experience that is actively working in social work education. The unlearning of harmful grading practices, classroom power structures, and models that reinforce individualism are essential for propelling social work education toward a more collective, justice-oriented approach. This article draws on transformational pedagogy and clinical social work practice to explore the ways in which change can occur with intention, attunement, and humility on behalf of instructors and lends to the ongoing conversation around decolonizing social work education. The authors posit that transformative education lies in the space between social work education and clinical practice. The methodology for this article is a culmination of a narrative literature review and the authors’ collective clinical social work practice and pedagogical experience, and this article brings what already occurs in that space into the scholarly literature. Full article

Conventional direct communication in Manned–Unmanned Teaming (MUM-T) suffers from fundamental scalability and security limitations. As the number of Unmanned Aerial Vehicles (UAVs) increases, the communication burden on the manned aircraft (MA) grows significantly, while security threats originating from UAVs may directly propagate to the MA. To address these challenges, this paper proposes a hierarchical communication architecture that introduces dedicated Network Drones (NDs) as intermediate communication mediators and trust boundaries between the MA and multiple UAV swarms. In the proposed design, the MA interacts exclusively with NDs, while UAV swarms communicate through ND-mediated links, effectively bounding the number of MA-facing connections and enabling scalable communication. Building on this structured communication model, a message-level Zero-Trust framework is enforced at the MA–ND interface. Each message is evaluated using a multi-dimensional risk model that incorporates authentication consistency, behavioral consistency, content validity, and contextual information, enabling early detection and containment of compromised UAV behavior. Furthermore, the architecture incorporates backup planning mechanisms, including dynamic reassociation and hot-standby operation, to ensure robust communication under ND failure conditions. Experimental results demonstrate that the proposed approach reduces MA-facing communication overhead, stabilizes end-to-end latency, and improves detection performance in terms of false positives and false negatives, while maintaining system robustness under failure scenarios. Full article

Electroencephalogram (EEG) signals, as a direct measure of the brain’s cortical electrophysiological activity, can objectively capture emotion-induced neural changes. Phase space reconstruction is an effective method for processing nonlinear time series. It maps time series to a high-dimensional phase space, thereby better preserving subtle dynamic information in the signal. This paper proposes a method for emotion recognition in EEG signals based on phase space reconstruction. First, the macro-topological features of the trajectories are constructed via phase space reconstruction. The time delay and embedding dimension are then optimized using the minimum cross-prediction error and the G-P method, followed by dimensionality reduction to a two-dimensional plane via local linear embedding. Building on this foundation, and in response to the limitations of manually designed features, we further propose a deep learning-based method for extracting multiscale dynamic features from trajectory images. The designed GN-MVXXS framework, which utilizes a granularity-adaptive module to adaptively switch the receptive field and a noise-filtering module to suppress isolated noise points, thereby effectively uncovers microscopic evolutionary features at the image level. Finally, to leverage the complementary strengths of macro- and micro-level information, we propose a fusion method based on dynamic attention. This approach aligns the dual representational dimensions through global average pooling and nonlinear dimension expansion, and utilizes a dynamic attention mechanism to adaptively assign feature weights, enabling the model to collaboratively enhance both overall dynamic patterns and local details based on sample characteristics. The experimental results show that the model achieved an accuracy of 96.11% in the three-class classification task on the SEED, 86.33% in the four-class classification task on the HIED, and 83.67% in classification across normal-hearing and hearing-impaired individuals, significantly outperforming single-feature models and traditional fusion methods. Full article

Visual scene matching navigation (VSMN) for unmanned aerial vehicles (UAVs) boasts advantages such as high precision, high reliability, and autonomy. The biggest challenge lies in the tension between local fine-grained information and global semantics, as well as limited generalization ability in real-world environments. While existing Transformer-based cross-view geolocation methods enhance global context modeling capabilities, they still generally face issues such as high demands on training data and computational resources, insufficient fusion of local fine-grained information and global semantics, and real-time performance in real-world complex environment. To address these problems, we propose a scene matching and localization algorithm based on the Siamese-ViT. For feature extraction, we use the ViT model to extract global features and K-means clustering to aggregate local features. Combined with the global features extracted by the ViT, a robust local–global feature representation vector is generated. For feature matching, incremental principal component analysis (IPCA) is used to reduce the dimensionality of the high-dimensional feature space, and a KD-tree is constructed for fast feature retrieval to improve matching efficiency. We validated our algorithm on the University-1652 dataset and a dataset of real-world satellite-drone image pairs. The results show that our Siamese-ViT outperforms other models in both Recall and AP. We conduct flight experiments in real-world environments, capturing drone images of complex scenes, including farmland, urban buildings, and waterways. The results show that, at a flight altitude of 350 m, our algorithm achieves an average absolute value of 6.2063 m for latitude, 6.7552 m for longitude, and 10.1922 m for horizontal error. Therefore, our Siamese-ViT demonstrates ideal overall positioning accuracy. Full article

Surface deformation is characterized by long-term accumulation and significant spatial differences, commonly inducing ground fissures and structural damage to roads and buildings, and in severe cases, causing collapses and other accidents that directly threaten human life. Reliable deformation monitoring and prediction are of great significance for early warning and infrastructure safety. Synthetic aperture radar interferometry (InSAR) technology can be used to acquire high-spatiotemporal-resolution temporal observations of surface deformation over a large area, but research on surface deformation prediction using InSAR temporal images is still relatively limited. Therefore, in this study, we used Sentinel-1 temporal imagery as the data foundation. Firstly, small baseline subset (SBAS)-InSAR inversion was used to obtain the line-of-sight-oriented cumulative deformation sequence and subsidence rate results. Based on this, the causal multi-trend fusion patch-to-point (CMTF-P2P) surface deformation prediction framework was developed. This framework effectively separates the long-term trends and short-term fluctuations in the deformation sequence through causal decomposition; introduces external drivers such as rainfall and event phase characteristics to enhance the temporal expression; and utilizes patch-to-point fusion of neighborhood spatial information to improve the spatial continuity and the ability to characterize local differences. Experimental results show that the method has an RMSE of 0.43 mm and an R² of 0.92, outperforming time-series deformation prediction models such as LSTM and iTransformer. Compared with traditional models that learn overall change patterns from historical time series, this paper alleviates the confusion between trends and volatility using causal STL decomposition, making the model’s expression of long-term trends and seasonal and short-term fluctuations in volatility clearer; event phase encoding enhances the model’s ability to characterize sudden disturbances and phased responses to events; the patch-to-point structure incorporates spatiotemporal information within the neighborhood, enhancing the model’s ability to apply spatiotemporal information; multi-branch collaborative modeling enhances the model’s ability to characterize multi-scale temporal features; and incremental cumulative consistency constraints enhance the physical consistency of the prediction results. Full article

This article proposes an expanded framework of Professional Noticing (PN) for Sustainability in Higher Education by integrating intermedial semiotics and ecopoetry as pedagogical tools aligned with the United Nations Sustainable Development Goals (SDGs). Building on the PROMISE project, in which the author participated,¹ the study conceptualises ‘noticing’ as an embodied, multimodal, and ethically inflected process of attending to human and more-than-human sign systems. The article introduces Intermedial Sustainability Noticing (ISN) as an extension of PN that foregrounds ecological awareness, intermedial perception, and cross-cultural interpretation. The study adopts a qualitative case study design based on the implementation of ISN within the Eurasia Foundation Cross-Cultural Partnerships hybrid course at Complutense University of Madrid. Participants included undergraduate students from diverse European and Asian institutions, who engaged in interdisciplinary and intercultural dialogues on sustainability through comparative literature and ecopoetry.² In the course, students developed perceptual, interpretive, and ethical awareness of global challenges by emphasising ‘noticing’ and attentional depth while broadening understanding of ecological interdependence. Data were collected through reflective journals, written assignments, multimodal projects, and classroom discussions, and analysed using an interpretive, semiotically informed approach. Findings indicate that ISN fosters enhanced attentional depth, multimodal interpretive skills, and increased ecological awareness, particularly through structured engagement with ecopoetry. The work of Kathleen Jamie is presented here as exemplary of how literary texts can activate perceptual, interpretive, and responsive dimensions of noticing, enabling students to connect textual analysis with sustainability concerns. The article argues that ISN offers a transferable pedagogical model for embedding sustainability competencies within humanities curricula, contributing to Higher Education’s role in fostering ecological literacy, intercultural dialogue, and ethically grounded engagement with global challenges. Full article

The study assessed the integration of digital technologies for managing risk events in the construction industry in Lesotho. This was done with a view to advancing sustainable construction risk management practices in the country and fostering a risk-resilient future. A quantitative research design, using a questionnaire as the data-collection instrument, was employed. Data analysis was conducted using descriptive and inferential statistics, including mean scores, the Kruskal–Wallis H-test, exploratory factor analysis, and partial least squares structural equation modelling (PLS-SEM). Findings revealed that the use of emerging digital technologies is slow-paced, albeit with gradual adoption of technologies such as Artificial Intelligence, Building Information Modelling, cloud computing, and drones by international organizations operating within the country. This low usage can negatively impact the management of risk occurrences across construction project management, health and safety, design, operations, finance, and scheduling. PLS-SEM shows that this low technology integration is mainly driven by two significant groups of challenges: knowledge and regulatory challenges, and industry-related challenges. To address these challenges, a comprehensive, strategic approach that promotes capacity building, regulatory reform, industry collaboration, and government incentives is essential. The study offers practical guidance to improve the application of digital technologies, ensuring that the construction industry in Lesotho becomes more innovative and aligns with international best practices for sustainable risk management. Full article