Search Results (104)

Structural durability and reliability are fundamental imperatives for the safe operation of engineering infrastructure, profoundly influencing both lifecycle asset economics and socio-environmental safety. A critical component of structural integrity engineering involves the precise spatial localization of defects, the continuous monitoring of their propagation kinetics, and the high-fidelity quantification of their impact on global structural health. To achieve this, advanced diagnostic methodologies are required to detect the earliest indicators of material degradation and track its evolution throughout the operational lifespan of the asset. Crucially, these non-destructive techniques must transcend reliance on subjective, localized visual inspections or unverified numerical models. The Acoustic Emission (AE) method represents a highly effective Non-Destructive Testing (NDT) paradigm that satisfies these requirements by performing real-time analysis of active degradation mechanisms coupled with specialized reference signal databases. This paper presents an integrated assessment framework developed within the SILDIG research initiative conducted between 2020 and 2025, introducing a novel framework founded on the methodological and technological fusion of two previously independent systems: the SONG diagnostic paradigm and the AE wave propagation methodology. The integrated framework uses a five-class anomaly classification scheme developed in accordance with the technical assessment framework applied in the investigated gas-network infrastructure. By operationalizing this unified framework within a fuzzy pattern recognition database—the Identifying Gas Network Anomalies (IGNA) methodology— the framework was evaluated on selected steel and PE100 pipeline sections representing different material and operational conditions. The proposed approach was deployed to evaluate the structural condition of 20 critical gas infrastructure components, specifically comprising 15 steel and 5 polyethylene (PE) pipelines. Laboratory tests and field monitoring campaigns were used to assess the capability of the AE framework to detect, classify, and localize anomalous signal patterns under controlled and operational loading conditions. The results indicate the potential of AE-based pattern recognition to support condition assessment and maintenance prioritization in gas pipeline infrastructure. Full article

Wood-processing residues can support both circular material use and bioenergy, but gross residue generation is not equivalent to additional energy availability. This review develops a literature-derived sequential framework for allocating four major fractions generated during primary mechanical wood processing—wood chips, solid offcuts, sawdust and shavings, and bark—among material uses, pellets and briquettes, industrial heat, and combined heat and power (CHP). A structured qualitative synthesis identified five decision criteria: feedstock quality and admissibility; retained material value; realistic material-use opportunity; suitability for a specific bioenergy pathway; and relevance to a fuel market or plant energy demand. The framework was applied illustratively to Polish data reporting 7.928 million m³ of residues and approximately 36.37 petajoules (PJ) of gross energy content. Results show that solid offcuts retain the strongest geometric value, chips face competition for fibre, sawdust and shavings can support both material and densified-fuel pathways, and bark combines specialised material opportunities with broader heat potential. Industrial heat has the broadest conditional suitability, whereas CHP is the most scale-dependent. The framework distinguishes technical feasibility from realistic availability and provides a screening step before detailed techno-economic, life-cycle, spatial, or optimisation analysis. Full article

Crop residues are important renewable biomass resources with considerable potential for regional energy supply and low-carbon transition. However, existing biomass resource datasets often lack sufficient spatial resolution, crop-level information, and long-term consistency, limiting their applications in regional resource assessments and bioenergy planning. This study develops a crop-residue biomass resource and bioenergy-potential dataset for Anhui Province, China, by systematically transforming agricultural production information into spatially explicit biomass resource indicators. The dataset comprises two complementary panels: a 2020–2024 province-wide city- and county-level panel covering rice, wheat, corn, beans, and tubers, and a 2015–2024 long-term panel covering rice, wheat, corn, and beans in 47 major grain-producing counties. Considering crop-specific characteristics, resource accessibility, and energy-conversion properties, four indicators were developed: theoretical residue biomass, collectible residue biomass, theoretical bioenergy potential, and collectible bioenergy potential. The dataset integrates spatial resolution, temporal continuity, and crop heterogeneity, providing fundamental data support for biomass resource assessment, bioenergy planning, life-cycle analysis, and low-carbon scenario studies. Full article

Field crops change continuously across growth stages, exhibit substantial spatial heterogeneity, and must be managed within short operational windows. This study presents a structured narrative review of information sensing and intelligent monitoring from pre-sowing conditions to stand establishment, growth and yield formation, biotic stress, maturity, lodging, and harvest readiness. The literature is organized by growth stage and analyzed through a common chain of agricultural need, observable variable, sensing platform, data processing method, validation design, state interpretation, and management or equipment output. Satellite remote sensing, unmanned aerial vehicle sensing, ground and proximal sensing, field Internet of Things, machinery-mounted sensors, multisource fusion, crop models, and machine learning methods are compared according to spatial support, temporal continuity, scale matching, field robustness, transfer conditions, uncertainty, and operational applicability. The reviewed studies report crop-phenotype retrieval, field-environment characterization, and biotic-stress identification under specified conditions, whereas cross-stage state inheritance, consistent reference measurements, independent validation, and conversion of monitoring results into executable tasks remain insufficiently established. The review therefore develops a lifecycle-oriented information-processing perspective in which multisource observations are quality-marked, interpreted as stage states, linked across time and scale, and checked against management and equipment records. Future work should strengthen cross-crop and cross-region validation, mechanistic and data-driven model coordination, uncertainty reporting, interoperability, and field feedback without presuming universally autonomous decision-making. Full article

The increasing availability of geospatial data and the rapid development of geospatial artificial intelligence (GeoAI) create new opportunities for modernizing cadastral and land administration systems. However, integrating probabilistic GeoAI outputs into systems structured according to the Land Administration Domain Model (LADM) remains conceptually, semantically and institutionally challenging. ISO 19152 provides standardized structures for land-administration objects, sources and lifecycle information, while ISO 19157 supports the description and evaluation of geographic data quality. A distinct issue nevertheless arises before these mechanisms can support authoritative cadastral information: the status and institutional treatment of model-dependent spatial observations. Using a conceptual framework-development approach based on a critical and integrative literature synthesis, this article proposes an AI-ready LADM framework organized into six interdependent layers: geospatial data acquisition, GeoAI inference, uncertainty representation, semantic mediation, candidate integration, and legal validation with feedback. The framework introduces external, non-authoritative candidate-evidence constructs that preserve source provenance, model information, uncertainty, possible LADM relevance and review history before any institutionally authorized cadastral action. It translates these constructs into a proposed procedural structure comprising source classification, uncertainty and risk assessment, conceptual semantic-mapping rules, validation procedures, audit trails and institutional roles. The contribution is domain-specific rather than a general claim about human oversight of AI. It distinguishes technical detection, candidate cadastral relevance and legally recognized cadastral status, while treating semantic mediation as one component of the broader process of epistemic translation. Scenario-based analysis illustrates the framework’s internal logic, but its effectiveness, usability and legal–institutional feasibility remain subject to subsequent expert assessment and jurisdiction-specific empirical evaluation. Full article

Bridge infrastructure worldwide is under increasing pressure from ageing assets, growing traffic demand, environmental deterioration, and the need for more effective lifecycle management. Although bridge-monitoring technologies have developed rapidly, much of the existing research has focused on individual sensing techniques, while the integration of heterogeneous information across different spatial scales has received comparatively less attention. This review takes an information-centric perspective on intelligent bridge monitoring and focuses on three representative technologies that provide complementary information at the material, structural, and network scales: Ground-Penetrating Radar (GPR) for assessing material condition, distributed optical-fibre sensing (DOFS) for monitoring structural response, and satellite remote sensing for providing network-scale information. Instead of comparing these technologies solely for their sensing principles, the review considers their complementary roles in bridge engineering and proposes a Material–Structural–Network (M–S–N) framework to organise monitoring information across scales. Recent developments in multi-source information fusion, AI-based interpretation, connected monitoring systems, autonomous monitoring, and self-evolving Digital Twins are also reviewed. Together, these advances indicate a shift from simply collecting monitoring data towards integrating information, extracting engineering knowledge, and supporting intelligent decisions. Based on this perspective, a Multi-Scale Integration Framework is proposed to illustrate how observations at different scales can be combined to provide a more complete understanding of bridge condition, structural performance, and infrastructure-network context. Future bridge monitoring should therefore move beyond isolated sensing and data collection towards multi-scale information generation and integration, supporting predictive maintenance, resilient asset management, and intelligent lifecycle decision-making across bridge networks. Full article

The deployment of renewable energy technologies is a key pathway for exploiting offshore resources and supporting the transition toward sustainable power generation. Floating offshore structures enable energy extraction in deep-water environments where conventional fixed systems are not feasible. This study presents an evidence-based assessment of the renewable energy potential associated with floating offshore structures in European seas and proposes an integrated framework for evaluating their sustainable design. The analysis is based on a curated dataset of European offshore energy projects, including installed capacity, spatial distribution, and technology-specific development indicators. A statistical approach is applied to characterize regional variability and identify dominant deployment trends. The results show a rapid expansion of floating offshore wind capacity, accompanied by the emergence of floating photovoltaic systems and hybrid wave–wind–solar configurations, while revealing significant regional differences in technological maturity, project scale, and growth rates. Sustainability indicators related to material efficiency, life-cycle performance, environmental impact mitigation, and circular-economy integration are further applied to assess the design of floating structures. The proposed framework highlights performance gaps and technological constraints and provides a structured basis for improving the efficiency and sustainability of future floating offshore systems under European offshore conditions. Full article

The transition to electric vehicles (EVs) is widely promoted as a strategy to reduce greenhouse gas emissions from transportation. However, the environmental benefits of EVs depend strongly on the electricity mix used for charging. If charging is predominantly supplied by fossil-fuel-based grid electricity, the resulting carbon dioxide (CO₂) emissions may remain substantial. Renewable-powered charging stations offer a solution, yet their spatial distribution creates a trade-off: if they are located further from the driver than their grid counterparts, then more CO₂ might be emitted along the way. This study developed and validated a framework for quantifying this trade-off. A mathematical model was first constructed, in which charging stations were spatially distributed following a Poisson process, and renewable availability was described by a Gaussian distribution. Emissions were measured in terms of mCO₂, the mass of CO₂ emitted by driving and charging. The model was then tested by comparing it with a MATLAB-based computer simulation incorporating stochastic station distributions and vehicle energy states. Both approaches identified a distinct minimum in the emission–distance curve. The mathematical model located the optimum at 3.737 km, while the computer simulation confirmed the result within an interval of 2.844–4.266 km. These findings prove the existence of an optimal charging distance. The results highlight the value of considering various parameters during EV infrastructure planning and offer practical guidance to reduce life-cycle emissions in sustainable mobility systems. Full article

This study explores the relationship between Artificial Intelligence (AI) and net-zero carbon buildings (NZCBs) and net-zero energy buildings (NZEBs) over the last decade. A thematic evolution has been observed in this research area, shifting from conventional optimization towards more advanced digital, intelligent, and decarbonized infrastructure. Co-occurrence, clustering, thematic evolution, network, and visualization justify this science mapping analysis at regular intervals (2015–2018, 2019–2022, and 2023–2026). Digital twins (DTs) have been identified as the dominant theme in strategic analysis, integrating Building Information Modeling (BIM), sensors, communication networks, and AI algorithms. In contrast, there has been the emergence of GIS as a complementary platform for extending AI applications beyond individual buildings to neighborhood, city, and regional scales through carbon mapping, life-cycle assessment, energy storage planning, and spatial decision-making. The analysis highlights AI as supporting technology rather than an isolated research theme, managing building information through digital twins and facilitating urban-scale decarbonization through GIS. The novelty of this study lies in proposing a dual framework that aligns digital twins and GIS as complementary implementation platforms for connecting AI with net-zero building objectives. The developed framework provides valuable insights into the intellectual structures creating intelligent, energy-efficient, and carbon-neutral built environments. Full article

The sustainability of marine ecological conservation and restoration, a key issue in the implementation of Sustainable Development Goal 14 (SDG 14), which aims at reducing marine pollution, protecting and restoring marine environments, promoting the sustainable use of marine resources, and strengthening scientific monitoring requirements, depends not only on policy design and financial investment, but more critically on effective coordination among policy implementation, resource management, and performance feedback. This paper analyzes the institutional evolution of marine ecological conservation and restoration audit in China and the main issues and their causes based on the text analysis and qualitative interpretation of the 302 marine ecological issues in the audit reports publicly released by Chinese audit authorities from 2008 to 2024. Audits found that the problems were predominantly in three areas: policy accountability and strategic implementation; project approval and implementation management; and fiscal management and fund coordination. The emergence of these is strongly linked to the fragmentation of department responsibilities, mismatch between project management cycles and ecological restoration cycles, and inadequate coordination between fund allocation and ecological performance. Although some problems were repeatedly reported across different years, their observed temporal and spatial distribution was influenced by the scope of audit coverage and the extent of public disclosure. On this basis, this paper proposes to improve audit oversight in the three areas of policy accountability coordination, project life-cycle management, and the performance of fiscal funds. This is to promote the implementation of SDG 14 requirements along the chain of “objective–resources–projects–monitoring–rectification”, serve as a reference for improving the Chinese supervision and accountability mechanisms for marine ecological conservation and restoration, and provide practical experience from China on the implementation of SDG 14 at the levels of national policy implementation and public accountability. Full article

Urban solid waste governance has become a key component of sustainable urban development. However, integrated analyses that combine thematic evolution and spatial heterogeneity remain scarce. This study applies an LDA-based systems analysis of USWG policies through a life-cycle governance perspective to examine the evolution of solid waste policies using a corpus of 13,161 policy documents from 35 major Chinese cities covering the period 2000–2025. The analysis compares thematic trends across three predefined policy phases grounded in major national policy milestones: Exploration period (2000–2010), Stabilization period (2011–2019), Transformation period (2020–2025). Six governance themes are identified relating to regulatory enforcement, procedural supervision, construction-site management, waste resource utilization, green innovation, and long-term institutional mechanisms. Based on these themes, the study examines changes in relative thematic shares over time and compares city-level theme intensity across urban tiers. The results show a gradual reconfiguration of policy attention, with pollution control and construction-site management remaining important while resource circulation, green innovation, and institutional coordination become more prominent. Spatially, Tier-1 cities exhibit relatively stronger attention to innovation and institutional coordination, Tier-2 cities show a more balanced profile across resource recovery and operational management, and Tier-3 cities display comparatively closer attention to enforcement and pollution control. By synthesizing temporal patterns, thematic structures, and urban-level differentiation, this study provides a systems-oriented account of policy attention in China’s urban solid waste governance. The findings offer a descriptive basis for understanding differentiated governance priorities across cities with different development conditions. Full article

The widening gap between planning blueprints and actual implementation outcomes in transit-oriented development (TOD) has become a critical concern in China’s rail transit expansion. Using Xiamen Metro Line 1 as a case study, this paper develops a three-tiered spatial framework (corridor, planning district, and station catchment) to evaluate plan conformance across land-use efficiency (LUE), category (LUC), and intensity (LUI). The results reveal that, despite generally high implementation levels, significant deviations from statutory targets persist, characterized by cross-island spatial polarization, structural imbalances, and localized extreme outliers. Key bottlenecks include delayed conversion of low-efficiency land, functional mismatches, and overall implementation stagnation in off-island areas. Crucially, these deviations do not merely denote planning failure but expose a profound governance gap between static blueprint regulations and the dynamic complexities of TOD execution. To bridge this divide, this study proposes an integrated governance framework emphasizing context-adaptive implementation models, synchronized “transit–land–planning” institutional mechanisms, and lifecycle-wide adaptive policies. Full article

To address the spatially concentrated loss of relative speed and spacing measurements in expressway tunnel clusters, this route-specific case study evaluates a Projector-Transformer Deep Learning (PTDL) pipeline that adds a linear reconstruction head to a Transformer following-speed predictor. Sixty trajectory segments came from two instrumented-vehicle trials on a 42.086 km route with 15 tunnels. Errors were summarized over tunnel cluster cells (Tce). Coupled variants share an encoder under a weighted dual-task loss. Decoupled variants train separate encoders sequentially, freezing reconstruction during forecasting. Under the reported development records, decoupling reduced missing-feature MSE by 40.7% (from 0.0246 to 0.0146), while complete-feature MSE was nearly identical (0.0075 versus 0.0074). With complete inputs, D-PTDL-S gave a lower ASE than the reported IDM, LSTM, and D-PTDL values over one and two Tce. In the naturally missing subset, its ASE was 45.0–52.6% lower than D-PTDL across one to five Tce. The 60 trajectory segments were partitioned segment-wise into disjoint training, validation and test subsets (80/10/10), with validation used solely for early stopping and the test subset read once after training. The baselines were neither information- nor calibration-matched, no repeated-seed inference was performed, and no ground truth is available within the naturally missing intervals. The results therefore establish route-specific feasibility rather than cross-route generalization. Safety, energy, emissions, and life-cycle outcomes were not measured. Sustainability relevance is limited to operational continuity under incomplete sensing. Full article

The availability of environmental indicators at the NUTS-2 level remains limited in European statistical sources, often resulting in regional sustainability analyses that reflect short-term policy dynamics rather than long-term conditions. To address this gap, this paper proposes a parsimonious framework based on a spatial stock indicator measuring the share of artificial land within each region (ENV_ARTIFICIAL_LAND), derived from CORINE Land Cover data and aggregated at the NUTS-2 level. Rather than serving as a short-term metric of policy performance, the indicator describes an inherited territorial stock reflecting historical land-use trajectories, consistent with path-dependent development processes. Using the Italian NUTS-2 regions as a case study, the indicator is analysed alongside key socio-economic variables covering economic capacity, social vulnerability, and human capital formation through a non-aggregative, quadrant-based trade-off framework. The results suggest pronounced regional asymmetries and structural mismatches, demonstrating that territorial rigidities and socio-economic outcomes follow differentiated, non-linear alignments. From a policy perspective, the analysis highlights the limits of uniform benchmarking and underscores the necessity of place-based strategies tailored to inherited spatial constraints. Future developments will include integration into territorialised lifecycle frameworks, to account for the cumulative effects of land occupation and environmental debt. While this framework offers a transparent screening tool for regional spatial rigidity, its convergent validity against high-resolution spatial datasets (such as HRL Imperviousness) and disaggregated land-use subclasses remains to be formally tested in future empirical research. Full article

Land consolidation (LC) is increasingly expected to support food security, ecological restoration, rural development, and climate mitigation, yet evidence on its carbon effects remains fragmented across engineering, ecological, spatial, and governance research. This review combines bibliometric mapping with structured evidence synthesis of 355 records from WoS and CNKI to examine knowledge evolution, analytical paradigms, and their integration. The field has expanded from component-based assessments of construction emissions, soil carbon, and land-cover change toward life-cycle accounting, carbon fractions, ecosystem-service interactions, spatial optimization, and policy evaluation. WoS-indexed and Chinese-language literature show distinct but increasingly convergent orientations shaped by differences in intervention contexts, disciplinary traditions, analytical scales, and available evidence. Four complementary paradigms are identified: carbon accounting, biogeochemical processes, spatial land systems, and decision support and governance. Together, these paradigms reveal interconnected carbon pathways but remain constrained by inconsistent accounting boundaries, weak process–scale–time linkages, and limited integration with land-governance decisions. Future research should therefore advance standardized life-cycle and multi-scale accounting, mechanism-based assessment of long-term carbon and ecosystem-service dynamics, and digitally and institutionally enabled low-carbon governance. Full article