Long-term data loss resulting from sensor malfunctions, communication interruptions, and other factors in Structural Health Monitoring (SHM) significantly undermines the reliability of damage identification and safety assessment. Existing methods—ranging from statistical approaches and low-rank matrix completion to traditional machine learning and deep learning imputation techniques—often suffer from either limited accuracy or excessive model size and slow inference, making deployment in resource-constrained scenarios difficult. To address these challenges, this paper proposes TEFN–Imputation, a lightweight and efficient time-series imputation model. This model utilizes observation-driven non-stationary normalization to mitigate the impact of time-varying characteristics and dimensional discrepancies. It employs linear projection for temporal length alignment and constructs BPA-style mass representations from dual perspectives of time and channel. Furthermore, it replaces strict Dempster–Shafer belief combination with an expectation-based evidential aggregation (readout), thereby significantly reducing computational overhead while enabling uncertainty-aware evidential indicators for interpretation rather than claiming a direct accuracy gain from uncertainty modeling. The observed accuracy and robustness improvements are primarily attributed to the normalization and dual temporal–channel modeling design under the same lightweight readout. Systematic experiments on two real-world bridge monitoring datasets, Z24 and Hell Bridge, demonstrate that TEFN consistently maintains low Mean Absolute Error (MAE) and minimal volatility across various combinations of training and testing missing rates, exhibiting high robustness against variations in missing rates and train–test mismatches. Concurrently, compared to RNN and large-scale Transformer baselines, TEFN reduces parameter count and CPU inference time by one to two orders of magnitude. Thus, it achieves a superior trade-off among accuracy, efficiency, and model scale, making it highly suitable for online SHM and imputation tasks in practical engineering applications. Across the settings on Z24, TEFN achieves a mean MAE of 0.218 with a standard deviation of 0.002, while using only 0.02 MB parameters and 2.73 ms per batch CPU inference.
The EPC model is currently the mainstream implementation approach for landscape projects, but fragmented management of the design–construction interface constrains project performance. Addressing issues such as cost overruns and schedule delays caused by ambiguous responsibility allocation, inefficient information transfer, and frequent design changes in EPC landscape projects, this paper focuses on the Xiaoyalong Wetland Park project in Kashi, Xinjiang, as a core case study. Combined with research on 12 representative projects, it identifies 16 interface management factors across four dimensions: contract management, organizational coordination, technical support, and ecological–artistic integration. Employing a mixed-methods approach combining questionnaire surveys (186 valid samples) and semi-structured interviews, validated through SPSS and structural equation modeling, this study confirms that early collaborative design serves as a core driver. Based on empirical findings, it derives and proposes a three-tiered optimization strategy: “foundation at the root layer, coordination at the transition layer, and assurance at the direct layer”. Pilot application of this strategy demonstrated significant effectiveness, reducing design change rates by 32%, shortening coordination time by 28%, and lowering cost overrun rates by 15%. This study enriches the theoretical framework of interface management in landscape engineering EPC projects and provides practical guidance for similar projects in arid regions.
Shriharsh Ashok Dixit, Rama Devi Nandineni, Somu G
et al.
Emergency Departments (EDs) are the primary hospital interface during disasters and mass-casualty incidents, yet surge capacity assessments predominantly emphasize workforce and logistics while overlooking measurable spatial determinants. Observations from five tertiary hospitals in India indicate that circulation bottlenecks, incompatible functional adjacencies, and contamination risks can compromise safety and operational performance. This study develops and validates the Spatial Surge Capacity Assessment Framework (SSCAF) to operationalize spatial resilience as a quantifiable dimension of healthcare infrastructure preparedness. A sequential mixed-methods design was applied across five tertiary hospitals using structured spatial walkthroughs; architectural and disaster-planning document review; and focus group discussions involving 81 clinicians, administrators, and facility planners. The outcome of this thematic analysis produced 42 spatial indicators, refined through three Delphi rounds with a multidisciplinary expert panel. Consensus retained 30 key performance indicators (median ≥ 4/5; IQR ≤ 1; Kendall’s W = 0.855; χ<sup>2</sup> = 297.42; <i>p</i> < 0.001). Content validity was strong (I-CVI 0.75–1.00; S-CVI/Ave = 0.93), and reliability was high (ICC 0.82–0.91), structured into eight operational domains. The resulting weighted scoring matrix standardizes the measurement of spatial surge preparedness. The SSCAF provides an evidence-based audit and planning tool supporting resilient hospital infrastructure. It aligns with the Sendai Framework, enabling governance audits, guiding ED retrofitting, and supporting performance-based evaluation for planners and architects.
The overuse of fossil resources, including oil and coal, has accelerated the energy crisis. The use of renewable energy, especially solar energy, is seen as a viable solution to this problem because fossil fuels pollute the environment and make it difficult for plants and animals to survive. The atmospheric vortex engine (AVE) and the solar vortex engine (SVE) are innovative ways to harness solar energy by mimicking the natural vortex dynamics to generate power. Important developments include the integration of solar collectors with vortex engines and the enhancement of design parameters, such as air inlets and turbine locations, as well as reducing the height of the chimney and reducing its construction area. These changes enhance its cost-effectiveness and do not require large building areas. This article provides a comprehensive analysis of current developments in vortex wind turbine systems for power generation. The articles studied consist of theoretical and experimental analyses. Furthermore, we conducted field research using short-term experimental designs. The research suggests that significant improvements in energy efficiency are possible. However, it emphasizes the need for further research and development to transform these technologies from small-scale prototypes to large-scale commercial applications. This would facilitate renewable energy generation and reduce our dependence on dirty energy sources.
The literature considers urban green spaces as a key to promoting physical activity and health. Despite the increasingly recognized walking exercise promotion benefits of urban green spaces, the relationship between walking exercise preferences and green space layout remains unclear. We extracted preferred routes for walking exercise and measured the route heat using a one-year shared data of walking exercise from a mobile outdoor assistant app in the old city of Fuzhou, China. We then evaluated green space layout features within a 500-m buffer zone along the preferred routes in dimensions of scale, form, and function. The relationship between the route heat and green space layout features was analyzed using a series of poisson regression models, with the influence of urban morphology and the built environment allocation being controlled. Results showed that there existed a significant association between preferred routes for walking exercise and the combination of green space layout, urban morphology, and the built environment allocation; the route heat had positive associations with NDVI and comprehensive park area density, and showed negative associations with patch shape index and patch density. This study illustrates the necessity for targeted intervention strategies to promote the development of walkable and healthy urban areas.
Urban blue–green spaces (UBGSs) are a significant avenue for addressing the worldwide mental health crisis. To effectively optimise landscape design and management for the promotion of health benefits from UBGS, it is crucial to objectively understand public preferences. This paper proposes a method to evaluate public landscape preference from the perspective of seeing and thinking, takes the examples of seven parks around the Dianchi Lake in Kunming, China, and analyses the social media data by using natural language processing technology and image semantic segmentation technology. The conclusions are as follows: (1) The public exhibits significantly high positive sentiments towards various UBGSs, with over 93% of comments expressed positive sentiments. (2) Differences exist in the frequency and perception of landscape features between image and text modalities. Landscape elements related to stability are perceived more in images than in text, while dynamic and experiential elements are perceived more in text than in images. (3) In both modalities, the distinctive landscape features of parks are more frequently perceived and preferred by the public. In the end, the intrinsic links between landscape elements and public sentiment and preferences are discussed, and suggestions for design and management improvements are made to consolidate their health benefits to the public.
Dharinee Ramamoorthy, Bingcheng Guo, Syed Minhaj Saleem Kazmi
et al.
Fibre-reinforced polymer (FRP) reinforcement has been employed as an alternative to conventional steel reinforcement in concrete structures, which is attributed to its excellent strength and corrosion resistance. However, one drawback is that FRP reinforcements are brittle and affect the ductility of concrete structures. One of the recent effective techniques proposed to overcome ductility issues is the compression yielding (CY) concept. The CY mechanism allows the structure to fail differently than the conventional FRP-reinforced concrete structure. Thus, the existing design recommendations as per the current codes for FRP-reinforced concrete structures are not appropriate. Hence, reliability studies are crucial for the development of a functional CY beam design in order to emphasise the structure’s lifetime performance and to guarantee safety requirements. In this study, a reliability-based design approach is developed for a compression-yielded FRP-reinforced concrete beam (CY beam) using load and resistance factor design (LRFD). Firstly, the flexural failure modes of CY beams are discussed. The uncertainties involved in the development of the probabilistic model for the CY beam are defined. A case study is consequently conducted for a CY beam with random variables that are associated with the statistical characteristics of material properties and load. The reliability analysis method employed in this research is the Hasofer–Lind method. The results suggest the importance of choosing appropriate design variables and stochastic parameters for CY blocks that contribute to a higher level of reliability. The reliability index and resistance factors of a CY beam are then evaluated using the Monte Carlo Simulation computational method. The reliability index value of 3.336 is obtained from the simulation, which indicates that the CY beam demonstrates ductile behaviour. The results not only demonstrate ductile behaviour but also contribute to a possible reduction in material costs and a substantial safety margin. When compared to conventional FRP-reinforced concrete beams, for different load ratios, CY beams showed higher resistance and better reliability levels.
La arquitectura valenciana vivió un momento de esplendor durante los siglos XIV, XV y XVI en un tiempo de desarrollo económico, en correspondencia con la expansión por el Mediterráneo de la Corona de Aragón. Personajes nobles construyen casas señoriales y palacios que destacan por su riqueza arquitectónica, y acabados decorativos. Entre ellos cabe señalar los dibujos geométricos de la carpintería de sus puertas y ventanas, que resultan característicos de este tipo de edificio en la época y entorno geográfico mencionado. En algunos casos pueden suponerse originales, pero dadas sus características, es un elemento que ha podido sufrir renovaciones y sustituciones, en algunos casos datadas en el siglo XX. Se presenta una aportación a su conocimiento, aplicando una metodología de análisis de los trazados decorativos, que puede ser complementaria a estudios de tipo histórico, dentro de un enfoque multidisciplinar. A partir de un número representativo de casos se analiza la generación de formas en base a las teselaciones regulares y semirregulares, descubriéndose un variado repertorio. Ello permite establecer una clasificación y descripción de los distintos tipos, que se definen gráficamente y se ilustran con ejemplos de aplicación concreta y de adaptación a las formas de las portadas y ventanales.
Engineering design, Architectural engineering. Structural engineering of buildings
Yrneh Zarit Ulloa-Torrealba, Andreas Schmitt, Michael Wurm
et al.
ABSTRACTFailures in urban areas’ solid waste management lead to clandestine garbage dumping and pollution. This affects sanitation and public human hygiene, deteriorates quality of life, and contributes to deprivation. This study aimed to test a combination of machine learning, high-resolution earth observation and GIS data to detect diverse categories of residual waste on the streets, such as sacks and construction debris. We conceptualised five different classes of solid waste from image interpretation: “Sure”, “Half-sure”, “Not-sure”, “Dispersed”, and “Non-garbage”. We tested a combination of k-means-based segmentation and supervised random forest to investigate the capabilities of automatic classification of these waste classes. The model can detect the presence of solid waste on the streets and achieved an accuracy of up from 73.95%–95.76% for the class “Sure”. Moreover, a building extraction using an EfficientNet deep-learning-based semantic segmentation allowed masking the rooftops. This improved the accuracy of the classes “Sure” and “Non-garbage”. The systematic evaluation of all parameters considered in this model provides a robust and reliable method of solid waste detection for decision-makers. These results highlight areas where insufficient waste management affects the citizens of a given city.