Search Results (900)

Research on industrialised building (IB) adoption has extensively documented technical, organisational and market barriers but has paid less attention to how responsibility for addressing them is organised. The purpose of this study is to examine how responsibility, trust and actor-positioned risk are constructed in Australian practitioner accounts of IB adoption. A critical discourse analysis of interviews with 23 practitioners provides the primary empirical evidence. A secondary corpus of English-language YouTube comments provides a bounded contrast with public discourse. Responsibility-void discourse was most evident around shared functions including training, certification, inspection, industry coordination and design-to-delivery integration. Trust helped interpret why some firms sought greater control over functions they considered insufficiently assured externally, while risk differentiated the delivery, compliance, capital and purchase-related forms of exposure described across the two corpora. The analysis also identified distinct information conditions, including knowledge-infrastructure absence and bounded cases of strategic opacity and interoperability failure. The illustrative public corpus foregrounded purchase conditions, tenure and category credibility, whereas practitioner accounts focused on institutional delivery and coordination. The study develops a framework for responsibility in IB adoption that distinguishes fragmented responsibility, responsibility shifting and responsibility voids. It conceptualises ownership as accountable coordination of work distributed across actors, reframing persistent adoption barriers as problems of institutional organisation, coordination and follow-through. Full article

Earthquakes remain a critical threat to global infrastructure. Recent catastrophic events, such as the 2023 Kahramanmaraş earthquakes in Türkiye and Syria, underscore the vital necessity of rapid, accurate post-disaster building damage evaluations. Structural collapse under seismic loading leads to substantial loss of life and severe economic disruption, particularly in regions dominated by aging building stocks that predate modern seismic design codes. To address the limitations of conventional manual inspections, this study introduces a comprehensive artificial intelligence (AI) framework designed to automate and enhance post-earthquake structural assessments. Leveraging a heterogeneous dataset from the 2021 Haiti earthquake, which includes both categorical building attributes and post-disaster imagery, the proposed approach employs rigorous data preprocessing and exploratory analysis to identify key vulnerability indicators and resolve data inconsistencies. Independent predictive pipelines were developed utilizing state-of-the-art machine learning algorithms for tabular data and deep learning architectures for image analysis. Subsequently, a novel hybrid meta-classifier was implemented to fuse these distinct modalities. By integrating spatial and structural context with direct visual evidence of damage, the hybrid model is successful in estimating structural damage severity. Among all evaluated approaches, this multimodal framework significantly improved predictive reliability. The hybrid model achieved a classification accuracy of 89%, consistently outperforming isolated tabular and image-based models. These findings highlight the efficacy of multimodal data fusion in disaster analytics and suggest that AI-driven hybrid architectures can serve as robust, scalable decision support tools for structural engineers and emergency response agencies. Full article

UAVs increasingly rely on accurate SLAM for aerial mapping and inspection in GPS-denied environments. 3D Gaussian Splatting (3DGS) has opened a new direction for UAV mapping by allowing SLAM systems to build dense, photorealistic, and renderable maps. Yet in 3DGS SLAM the map is optimized from the pose graph, so a false loop closure can deform both the UAV trajectory and the Gaussian map consumed by downstream UAV autonomy. Reliable loop admission is therefore relevant to safe GPS-denied operation because it protects the state and map estimates on which autonomous functions depend. The present work evaluated this upstream estimation-integrity problem; it did not measure closed-loop guidance, control, or navigation-safety outcomes. We address the loop-admission problem that arises after a place-recognition (PR) module proposes a candidate loop and relative-pose seed. GSSeq is a rendered-reference sequential verifier that uses the current Gaussian map as active evidence before inserting a loop factor. It renders RGB-D references with the PR seed, checks LiDAR/rendered-depth consistency and image/rendered-reference consistency over active support, and propagates the seed through a short query trajectory window. A loop is admitted only when this evidence remains geometrically supported and photometrically stable. On fixed LiDAR-PR candidate sets spanning MARS-LVIG, MUN-FRL, and independent NTU-VIRAL aerial sequences together with ground-mobility benchmarks, GSSeq provides a competitive precision-oriented operating point while suppressing false loop admissions. Thresholds calibrated only on NTU-VIRAL spms_01 combine rendered RGB agreement with LiDAR-submap geometry and are then frozen for spms_02. On this held-out sequence, GSSeq rejects all seven false-positive BTC factors while retaining one of three true-positive factors. The trajectory-to-map experiment reduced ATE RMSE from $2.609\mathrm{m}$ to $1.417\mathrm{m}$ and improved selected-view PSNR from $13.80\mathrm{dB}$ to $16.46\mathrm{dB}$. These results show that rendered verification can preserve an aligned, renderable UAV trajectory-map pair before unsupported loop factors reshape the SLAM map. Full article

We present ESNformer, a hybrid architecture that couples an Echo State Network (ESN) reservoir with a Transformer encoder for classification of structured, multi-indicator assessment data: a fixed-order vector of complementary indicators per assessment instance rather than a repeated-measures time series. The reservoir acts as a fixed nonlinear feature map over the indicator vector, while self-attention, made position-aware over the fixed column order, learns how each indicator’s evidence contributes to the final decision, so the two components, together, capture local, indicator-level detail and global, cross-indicator interactions within a single, end-to-end trainable model. Interpretability is treated as a first-class design requirement rather than an afterthought: the architecture is paired with an explainability layer combining SHAP feature attribution (reported both globally and per class), the model’s own attention weights, a deletion/insertion faithfulness test that quantitatively verifies which inputs the model actually relies on, and counterfactual maps that translate a prediction into an actionable, inspectable recommendation. We evaluate the architecture on a concrete case study, classifying Braille-literacy instructional recommendations from 15 pedagogical indicators grouped into three categories (Mangold’s, ABKL, and Progresar), using a benchmark of 900 real assessment instances (630 used, together with a class-conditional augmentation procedure, to build a 2100-instance training set) with validation and test partitions (135 instances each) kept exclusively real. On this benchmark, the tuned model reached 85.33% accuracy, 85.90% macro-precision, 85.33% macro-recall, an F1 score of 85.25%, and an AUC of 0.95 on the real test set. SHAP attribution, attention weights, and the faithfulness test converge on the same two dominant indicators (response time and error count): removing them alone collapses accuracy to chance, while retaining only them recovers most of the model’s accuracy. We report this transparently alongside a comparison against ESN-only, Transformer-only, and tabular baselines (logistic regression, decision tree, random forest, XGBoost, and an MLP) on the same data and discuss what the hybrid architecture and its explainability pipeline add beyond what the two dominant indicators already explain and how the approach generalizes to other tabular and mixed-granularity assessment settings that require both predictive accuracy and a verifiable account of what drove each decision. Full article

Interpreting nonlinear seismic structural analysis results in nuclear power plants remains challenging when conventional post-processing tools are used, as these require analysts to reconstruct structural meaning from fragmented 2D or non-immersive 3D views. This limits spatial understanding, weakens traceability between global response and local damage mechanisms, and constrains the communication of findings in critical infrastructure contexts. In response, this paper proposes a human-centered methodological framework for integrating BIM models and nonlinear seismic simulation results into an immersive virtual reality environment for structural interpretation and risk-free inspection in nuclear power plants. The proposed workflow connects structural seismic analysis, result post-processing, the reference BIM model, and its deployment in a VR environment developed in Unreal Engine. The framework was implemented through a case study based on a generic nuclear power plant, resulting in a functional demonstrator. A qualitative evaluation based on an expert walkthrough showed the potential of the proposed workflow to enhance spatial understanding, simplify comparison between structural states, enable risk-free inspection environments, and facilitate technical communication. The main contribution of the study is to demonstrate how an integrated virtual reality environment can act as a complementary, human-centered interpretive interface that reduces cognitive fragmentation in conventional structural analysis workflows. Full article

This study examines the feasibility of predicting net repair costs (Estimated Cost, PLN) for multi-family residential buildings from data extracted from technical inspection reports. Rather than merely comparing algorithmic performance, the analysis was designed as a diagnostic sequence aimed at identifying the sources of prediction error. Four machine learning algorithms (Extra Trees, Random Forest, XGBoost, and GBM) were first applied to direct regression of repair cost. We then tested whether the difficulty of estimating exact cost values stems from the high variability of the target variable and whether this limitation can be mitigated by a two-stage approach: assigning observations to one of three cost-risk bands (Low, Moderate, High) and subsequently estimating cost within the assigned band. The empirical cost distribution was strongly right-skewed (median: 4500 PLN; mean: 63,402 PLN; maximum: 3,680,524 PLN). The best direct regression model achieved an R² of 0.452, while the best fully deployable two-stage model, combining an XGBoost classifier with a Random Forest regressor, achieved an R² of 0.392. When it was assumed that the actual cost-risk bands were known, an R² value of 0.839 was obtained, indicating that the main source of error is not regression within the bands, but rather the initial stage of assigning the bands. These results demonstrate that reporting a single global R² for highly skewed, weakly identifiable cost data can be misleading, and that decomposing predictive performance into band-assignment and within-band regression components provides a more informative evaluation. This article also points to concrete directions for improving the underlying database, particularly through the inclusion of variables describing repair quantity, unit of measure, and detailed repair scope. Full article

This paper proposes an urban-scale dynamic screening method for the preliminary seismic-risk prioritization of masonry buildings. The method is based on the integration of rapid surveys, relational databases, geographic information systems, and accelerometric data recorded during seismic events. It is not intended to replace comprehensive seismic vulnerability or risk assessment procedures. In its present formulation, it provides an event-specific preliminary dynamic-priority indicator, derived from recorded ground motions, to support post-event inspections and subsequent detailed analyses at the urban scale. The procedure combines a rapid visual survey (on average covering ca. 300 buildings per day) with a database management system (DBMS) linked to a three-dimensional cartographic database of the building stock. The geometric and structural information collected in the field is integrated with the processing of ground acceleration records, from which the pseudo-acceleration spectra, peak ground acceleration (PGA), and spectral amplification ratios (DAF) are derived. Through the relationship between spectral period and building height, the method identifies height classes and number of storeys corresponding to the highest spectral amplification ratios derived from the recorded ground motions. Buildings belonging to these classes are not classified as vulnerable in absolute terms, but are considered priority buildings for subsequent checks, inspections, or detailed analyses. The method was applied to the municipalities of Umbertide and Gubbio, in the province of Perugia, which were affected by the seismic sequence of 9 March 2023. The processed accelerometric records identified Classes I, II and III, corresponding to one-, two-, and three-storey masonry buildings. The illustrative GIS application focused on Classes II and III; 27 of the 33 masonry buildings included in the damaged-building sample (82%) belonged to these two classes. The main contribution of the study is the definition of an integrated, rapid, and replicable workflow that can support local authorities, technicians, and civil protection operators in the preliminary management of seismic risk at the urban scale. Full article

Integrated qiaopi office–residence heritage preserves the material setting of remittance-letter operations together with domestic, educational, and ritual activities. In Lingnan’s hot–humid and disaster-prone environment, condition records need to be repeatable, spatially traceable, and continuously updatable. Taking Jingzu Jiashu and Mingde Jiashu, two former qiaopi office sites in Chaoshan, as case studies, this research develops an evidence-traceable digital conservation workflow integrating multi-source documentation; an adopted YOLOv8 surface-deterioration baseline; qualitative Grad-CAM visualization; structured deterioration records; and semi-automatic, human-confirmed Revit/HBIM association. UAV and terrestrial photography, mobile LiDAR/scanning, handheld measurement, measured drawings, point-cloud and reality-based products, and geometric models were organized into case-specific HBIM environments. The adopted deterioration dataset comprised 362 original images at 512 × 512 pixels and 2024 bounding-box annotations for five visually identifiable categories: spalling, staining, plants, saltpetering, and crack. The original images were divided into 253 training, 72 validation, and 37 independent-test images, while augmentation was restricted to the training subset, increasing the training pool to 1600 images. The previously established YOLOv8 baseline achieved a Precision of 0.85, Recall of 0.72, mAP50 of 0.83, and mAP50–95 of 0.58. Grad-CAM heatmaps were used as qualitative aids to examine model-emphasized image regions. Retained detections associated with Jingzu Jiashu and Mingde Jiashu were converted into versioned records containing source-image identifiers, deterioration classes, detector confidence, survey information, spatial references, verification states, and revision histories. Candidate spatial associations were generated through case identifiers, façade or space zones, element identifiers, and available spatial evidence, while final M1–M3 associations required human confirmation. By preserving source provenance, spatial uncertainty, and record histories, the workflow provides an auditable information basis for routine inspection, post-event review, maintenance prioritization, repair interpretation, and resilience-oriented preventive conservation. The workflow supports screening-level deterioration recognition and information management but does not provide causal diagnosis, structural assessment, exact affected-area measurement, building-independent generalization, or automatic repair recommendations. Full article

Buildings have significant impacts on the environment throughout their life cycle in terms of energy and water consumption, material usage, and waste generation. This study aims to evaluate the Sivas Train Station and Sivas High-Speed Train Station buildings, which were constructed in different periods and with different construction techniques, within the scope of sustainable renovation. In the study, the literature on sustainable architecture and building renovation approaches was reviewed; field observations, archival documents, interviews, and on-site measurements of temperature, thermal transmittance, and lighting were utilized. The buildings were compared based on criteria such as energy and water efficiency, material selection, and waste management. The findings indicate that the lack of insulation, old joinery, and absence of windbreaks in the Sivas Train Station, as well as the extensive glass surfaces, high user traffic, entrance layout, and operational issues with technical systems in the High-Speed Train Station, lead to energy losses. The lack of independent monitoring of water consumption in both buildings, the absence of systems for using rainwater, snow and graywater, and the inadequacy of waste separation practices have been identified as significant deficiencies. As a result of the study, different renovation strategies were proposed, preserving the original values of the historical structure and adapting the new structure to real usage conditions. It was concluded that sustainable renovation should be considered a continuous and holistic process that includes not only physical interventions but also building management, user training, regular monitoring, inspection, and certification. Full article

Conveyor belt systems are commonly treated as industrial equipment, although their operational value, degradation, risk, and renewal potential are distributed along the route and evolve through identifiable belt sections, splices, inspections, repairs, inserts, and refurbishment cycles. This article redefines conveyor belt loops as digital twin-ready linear assets and proposes a transferable asset management framework integrating three coupled layers: performance and physics, condition data and belt passport, and renewal decisions. The study is designed as a conceptual engineering article based on targeted literature synthesis, structured cross-sector analogy and framework development, rather than as a bibliometric review or a new optimization model. The proposed framework builds on previous work on structure-aware segment renewal by positioning it within a broader data and governance architecture. The article shows that a conveyor digital twin becomes operationally meaningful only when physics-based interpretation, spatially anchored diagnostics, intervention history, operating context, residual value, and auditable decision rules are connected through a persistent belt passport. The framework supports more transparent life-cycle decisions and positions conveyor belt loops as a reference case for infrastructure asset management, condition traceability, and digital twin governance. Full article

Pipe leakage in water distribution networks causes water loss, pressure decline, energy waste, and reduced service reliability. This study aims to support early localization of two simultaneous pipe leaks using the evaluated 11-sensor pressure-monitoring layout. A topology-aware deep learning framework was proposed on the EPANET 2.0 (Build 2.00.12) Net2 network using WNTR 1.3.2-based hydraulic simulation. Two-point leakage scenarios were generated by pipe-splitting strategy, and a 4 h early-stage pressure-residual window from 11 pressure sensors was used as input. The task was formulated as joint multi-label pipe identification and intra-pipe position regression. A bidirectional long short-term memory (BiLSTM) branch learned the temporal pressure response, while a graph attention network version 2 (GATv2) branch represented sensor–topology relationships. On the validation set, the model achieved a Top-2 F1 score (F1@2) of 61.18%. Both leaking pipes were identified in 31.65% of samples, and at least one true leaking pipe was included in the Top-2 candidates for 90.72% of samples. For correctly matched leak-point instances, the physical mean absolute error was 41.91 m. The results indicate that topology-aware temporal learning can provide pipe-level candidates and intra-pipe inspection distances for early two-point leak localization, although nearby and weak simultaneous leaks remain challenging. Full article

Building maintenance is essential for extending service life and ensuring safety. However, conventional visual inspection and document-based record management can cause information omissions, inconsistent transfer, limited reproducibility, and delayed collaboration. Despite growing interest in digital maintenance, few studies have quantitatively examined how different implementation levels of a metaverse inspection system (MIS) affect long-term maintenance performance. This study evaluated four MIS levels using system dynamics (SD) and multi-criteria decision analysis (MCDA). Based on a literature review and requirements analysis, 24 influencing factors were represented through causal loop and stock–flow diagrams. The scenarios comprised non-implementation (Level 0), visualization-based initial implementation (Level 1), BIM- and data-linked intermediate implementation (Level 2), and a conceptually integrated system combining data management, real-time monitoring, stakeholder collaboration, and maintenance decision support (Level 3). A 100-year simulation was conducted, with the 50-year point used for comparison. At the 50-year point, Level 3 maintained building condition at 76.72% and reduced cumulative cost by 31.8%, from US$386,477 for Level 0 to US$263,729. MCDA and inspection frequency, weight, and coefficient sensitivity analyses identified Level 3 as the most favorable scenario within the tested conditions. Full article

With the large-scale deployment of smart electricity meters in China, power companies need online methods that can evaluate meter operating status and locate faulty units without field inspection. Meter data are inherently multimodal. They combine time-series measurements with textual event logs. The semantic gap between these modalities limits the accuracy of existing approaches. This paper proposes an online operational status evaluation method for smart meters based on cross-modal perception with large language models. A Bi-LSTM and TCN-Attention network with quantile regression first builds a robust district line-loss baseline that captures seasonal fluctuations and operational uncertainty. A cross-modal alignment module then fuses the two modalities. The measurement sequences are encoded by PatchTST, and the event logs are encoded by a LoRA-fine-tuned LLM. The fusion is performed through contrastive learning and gated fusion. A retrieval-augmented knowledge graph provides additional support. Finally, a multi-indicator health index grades meters into five condition levels, and a hidden Markov model estimates the remaining useful life. Case study results demonstrate that the proposed method achieves 85.7% accuracy, outperforming the strongest baseline. Full article

Energy Management Contracting (EMC) is important for scaling energy-saving retrofits in existing buildings, yet policy incentives often fail when stakeholders perceive costs, benefits, and enforcement risks differently. This study aims to examine how subjective cognitive biases affect strategic interactions among governments, energy service companies (ESCOs), and energy-consuming units (ECUs) in China’s existing-building EMC market. Prospect theory is introduced into a tripartite evolutionary game model, and replicator dynamics with MATLAB R2024a simulations are used to analyze policy incentives, regulatory constraints, and behavioral parameters. The simulation results show that, among conventional policy parameters, moderate incentives and bilateral penalties are more effective than simply increasing subsidies or imposing unilateral punishment; when regulatory costs exceed a sustainable range, active supervision becomes unstable and cooperative implementation is weakened. Among prospect-theory parameters, higher loss aversion increases the perceived burden of fiscal and regulatory costs, while lower probability weighting reduces the perceived certainty of inspection and punishment. Reducing γ from 0.69 to 0.4 substantially weakens deterrence, and increasing λ from 1.5 to 2.25 intensifies strategic fluctuations. These findings provide a behavioral basis for designing staged EMC governance policies and offer practical implications for improving the stability and effectiveness of existing-building EMC implementation. Full article

Aging residential buildings constructed under outdated design standards create an urgent need for scalable, evidence-based indoor safety assessment methods. Conventional manual inspections rely on subjective checklists, lack audit trails, and are impractical for widespread deployment. This study presents a vision-based digital twin and AI agent framework that converts a single continuous smartphone video into an explainable, evidence-constrained safety assessment. The pipeline employs MASt3R-SLAM to reconstruct a metric-scale 3D point cloud from monocular video, calibrated with AprilTag fiducials for absolute scale. SpatialLM parses the geometry to extract semantic entities and spatial relationships. Risk guidelines are formalized into a computable Risk Prototype structure, unified within a hierarchical SceneState data structure that binds geometric measurements, semantic labels, image observations, and regulatory knowledge. A LangGraph-based AI agent conducts a dual-pathway assessment: an initial whole-dwelling scan followed by iterative follow-up queries invoking tool calls for measurement, knowledge retrieval, or visual cross-checking. In a pilot validation across five heterogeneous residences, with detailed manual comparison in two representative cases, the framework achieved risk recall rates of 77.8–100% and precision rates of 45.0–70.0% against the single-assessor manual reference. The average judgment closure rate was 71.7%, with spatial granularity enhancement of up to 2.2× in complex environments. These results suggest that the framework can achieve risk coverage comparable to manual checklist inspection while offering enhanced granularity in complex environments and quantitative precision in well-defined spaces. Full article