Search Results (685)

Fracture in concrete is inherently a multi-stage process, yet traditional three-stage frameworks do not explicitly distinguish between micro-crack development and macro-crack propagation, particularly under varying ligament length conditions. The influence of ligament length (notch-to-depth ratios of 0.0, 0.2, 0.3, 0.4, and 0.5) on the crack propagation characteristics in notched concrete beams under three-point bending is investigated. Three-dimensional digital image correlation (3D DIC) was employed to monitor full-field displacement and strain, enabling the evaluation of key fracture parameters including horizontal displacement, crack mouth opening displacement (CMOD), horizontal strain, fracture process zone (FPZ) length, macro-crack length, and total fracture zone length. A high-magnification industrial camera (100×) was simultaneously used for real-time observation of the notch tip. Based on the evolution of these parameters, the fracture process was divided into four distinct stages: linear elastic stage, micro-crack initiation and propagation stage, macro-crack initiation and propagation stage, and complete failure stage. The industrial camera observations confirmed macro-crack initiation at approximately 60% of the post-peak load, validating the proposed four-stage division. Quantitative results show that increasing the notch depth ratio from 0.0 to 0.5 reduces the peak load by approximately 30–40% and decreases the nominal stress proportionally. The FPZ was found to be fully developed at the 60% post-peak load threshold, after which it diminished as macro-crack propagation dominated. Aggregate bridging, crack deflection, and crack branching were consistently identified as the primary toughening mechanisms governing the ligament effect. The crack propagation mechanisms in the four stages are controlled by the combined effects of front free boundary effect, stress concentration effect, ligament effect, and back free boundary effect. These findings provide a refined understanding of concrete fracture that can inform the safety assessment and design of concrete bending members in infrastructure construction. Full article

This study investigates hydrogen-assisted fatigue crack growth (FCG) in vintage pipeline steels to quantify grade-dependent degradation under hydrogen–natural gas blending conditions. Fatigue behavior was evaluated using compact-tension specimens extracted from API X52 and X70 pipeline steels and tested in natural gas–hydrogen mixtures at a total pressure of 6.9 MPa and ambient temperature. Hydrogen concentration was systematically varied from 0% to 100% H₂ to assess its influence on crack-length evolution, fatigue crack growth rate, and fracture morphology. Crack propagation was characterized as a function of the stress-intensity-factor range, and scanning electron microscopy was used to examine hydrogen-induced changes in fracture mechanisms. The results demonstrate that FCG accelerates as hydrogen concentration increases, with a strong dependence on steel grade. X70 exhibited substantially greater hydrogen-induced FCG acceleration than X52, despite showing better fatigue resistance under hydrogen-free conditions. Fatigue life reductions approached 60% for X70 at 100% hydrogen, compared with approximately 30% for X52 under the same conditions. Significant early-life sensitivity was observed in X70 even at low hydrogen concentrations, whereas X52 showed more pronounced acceleration during later stages of crack growth. The influence of hydrogen was nonlinear and tended to stabilize at elevated blend fractions, indicating a saturation-type response once hydrogen-assisted crack growth became dominant. Fractographic analyses revealed a transition from ductile tearing in natural gas environments to terrace- and facet-controlled crack propagation in hydrogen-rich environments, accompanied by secondary cracking and river-pattern features. These findings demonstrate that hydrogen–natural gas blending can significantly alter fatigue crack growth behavior and relative material performance in pipeline steels, highlighting the need for grade-specific integrity assessment of existing pipeline infrastructure. Full article

Rapid growth in connected devices, artificial intelligence applications, and the Internet of Things (IoT) is driving demand for ultra-high data rates, low latency, and energy-efficient communication infrastructure. Wireless optical communication (WOC), including free-space optical (FSO), is recognized as a disruptive technology for 6G and future-generation networks. However, atmospheric visibility critically affects WOC/FSO link availability, capacity, and reliability. This study proposes a machine learning (ML)-based low-visibility classification model that integrates Meteorological Aerodrome Reports (METARs) with microwave-link received-signal (Rx) features. Visibility below 6000 m is predicted at the 1 h, 3 h, and 6 h horizons using 18 months of data from Suvarnabhumi Airport (VTBS) and Don Mueang Airport (VTBD) in Bangkok, Thailand. Four ML algorithms, namely logistic regression, random forest, extreme gradient boosting, and light gradient boosting machine (LGBM), are evaluated against persistence and Terminal Aerodrome Forecast (TAF) baselines. In a 100-round block-bootstrap evaluation, LGBM with METAR-Rx achieved the highest mean F1 scores at the 1 h and 3 h horizons, outperforming TAF by 28 and 20 percentage points at the 1 h horizon for VTBS and VTBD, respectively. SHAP and ablation analyses suggested that current visibility is the dominant predictor, while Rx features provide complementary information and improve F1 performance by approximately 1–4 percentage points. Seasonal analysis shows stronger cool-season performance, while rainy-season prediction remains challenging. Adding visibility-trend features further improves performance, with the best combined model achieving 1 h F1 scores of 0.7253 for VTBS and 0.6495 for VTBD. These findings indicate that integrating the METAR-Rx feature set can support short-term low-visibility classification. Full article

Vertisols represent some of the most dynamic pedological systems due to the high content of smectitic clays and the intense shrinkage–swelling processes associated with seasonal variations in humidity. The present work analyzes the dynamics of pedoturbations and the rheological behavior of a smectitic Vertosol located in the Cheglevici experimental field (Aranca Plain, western Romania), continuously monitored for a period of over 50 years (1967–2020). In a stationary experiment, inert markers were buried at depths ranging from 25 to 150 cm to track the vertical displacement of the soil mass. Periodically collected samples were analyzed from a granulometric, mineralogical, chemical, and rheological point of view (plasticity limits, activity index, volumetric shrinkage, free swelling, deformation modulus, cohesion, conventional pressure). The results indicate a high smectite content (69–76%) and rheological indices specific to highly active soils (PI > 35%, A > 1.0, VS > 100%, FS > 140%). The progressive redistribution of the markers provides strong evidence of substantial profile-scale soil redistribution associated with long-term pedoturbation processes, supporting the hypothesis of a near-complete profile turnover over multidecadal timescales. A significant increase in apparent density and a tendency for granulometric homogenization across the profile, associated with structural reorganization, are also highlighted. The study provides long-term experimental evidence on the vertical dynamics of the soil mass in Smectitic Vertisols and reveals major implications for agricultural management, infrastructure stability, and water flow modeling in expansive soils. Full article

Accurate pavement crack segmentation is critical for road infrastructure assessment, yet it remains challenging due to complex background noise and highly variable crack topologies. While emerging visual Mamba models excel in long-range contextual modeling, their inherent 1D sequence flattening process compromises local 2D spatial continuity. To address this limitation, we propose GeoMamba, a geometric-prior-infused multi-scale deformable visual Mamba network for road crack semantic segmentation. First, we design a Multi-Scale Deformable Visual State Space (MDVSS) module to extract multi-scale contextual features and dynamically adapt to tortuous crack paths through a novel deformable scanning mechanism. Second, a Geometric-Topology Prior Injection (GTPI) module is introduced to mitigate serialization artifacts. By leveraging deterministic, parameter-free analytical operators (i.e., Sobel and Laplace), the GTPI module explicitly extracts and adaptively infuses multi-scale structural priors into the Mamba decoder via gated skip connections, intrinsically reconstructing crack typologies while suppressing pseudo-structural noise. Comprehensive experiments on DeepCrack and Concrete3K datasets demonstrate that GeoMamba outperforms nine state-of-the-art methods. Specifically, it achieves peak performance on the DeepCrack dataset with an mIoU of 83.79% and an F1 score of 89.27%, demonstrating exceptional semantic segmentation performance, superior topological continuity, and robust generalization across diverse pavement materials. Full article

Artificial intelligence (AI) agents and large language model (LLM) agents are beginning to move materials discovery beyond isolated prediction tasks and toward tool-grounded workflows that can retrieve prior knowledge, configure simulations, launch calculations, inspect outputs, and decide what to do next. However, adjacent reviews on materials informatics, self-driving laboratories, natural-language processing in materials science, and autonomous chemistry have not isolated simulation-driven materials workflows as a distinct evidence base. This review addresses that gap through PRISMA-guided searches in Scopus (8 May 2026) and Web of Science (15 June 2026) for English-language journal articles published between 2022 and 2026. The combined search returned 232 records; 27 full texts were assessed and 26 studies were included in the final qualitative synthesis after one full-text exclusion. No eligible study was published in 2022 or 2023, indicating that the field emerged only in 2024 and expanded rapidly in 2025–2026. Catalysis and adsorption tasks (n = 6) and alloy design or evaluation (n = 5) dominated the corpus, while specialized multi-agent architectures were the most common pattern (n = 13). Across the included studies, agentic reasoning was most often coupled to workflow orchestration or integration tools, molecular-dynamics or atomistic simulation environments, and materials-data or machine learning screening pipelines; public repositories or archival artifacts were reported in 18 of 26 studies, experimental validation in six, and robotic closed-loop execution in only one study. The strongest evidence came from workflows that grounded language model decisions in simulators, structured databases, or experimentally verifiable outputs rather than in free-form text alone. This review therefore establishes AI agent workflow orchestration as a distinct analytical category within materials discovery and identifies the reporting, validation, and reproducibility conditions required for these systems to function as credible scientific infrastructure rather than as conversational demonstrations. Full article

Climate risk assessments for critical infrastructure are essential to identifying and predicting vulnerabilities early in the asset life cycle, enabling proactive mitigation through the implementation of technical and nature-based solutions (NbS) before impacts occur. However, such assessments often rely on dense quantitative indices that are difficult for non-technical stakeholders to interpret. To address this challenge, this paper presents an open-source decision support platform that combines OpenStreetMap site characterization, qualitative pre-screening, a quantitative IPCC AR6-aligned risk chain, and a downstream NbS recommendation layer. The approach deploys Large Language Models (LLMs) to translate analytical outputs into accessible narrative explanations. End-to-end site-characterization processing across three European demonstration sites took between 29 and 70 s. An exploratory ablation study investigated the faithfulness of the AI-generated explanations using three complementary metrics, demonstrating that the generated hazard assessments remained factually grounded and free from fabricated numerical values. Introducing example reports (exemplars) into the prompt context further stabilized the reliability of the output for complex risk indicators. Finally, a small blind expert evaluation with six researchers from adjacent technical domains provided convergent evidence: five of six raters independently rated with-exemplar Hazard Reports higher on completeness; among the five raters who expressed a directional preference, all five favored the with-exemplar condition (sign test, p = 0.031). Furthermore, seven of eight aggregate dimension-level comparisons confirmed that with-exemplar reports scored at least as high as their ablated counterparts. Full article

Oil extraction and agricultural production are central to North Dakota’s economy, yet their spatial interactions remain poorly understood. This study conducts a statewide geospatial analysis integrating OpenStreetMap data, GIS processing, DBSCAN clustering, and spatial statistics to examine the colocation of 7102 oil well and 4277 grain silo sites. Hotspot and spatial heterogeneity tests using the Getis–Ord Gi* statistic and local Moran’s I reveal a pronounced spatial divide: oil activity is tightly clustered in the western Bakken region, whereas grain storage facilities concentrate across central and eastern counties. The limited geographic overlap suggests minimal systemic land-use conflict, though localized high-intensity interactions emerge in McKenzie, Dunn, and Mountrail counties. These patterns provide stakeholders with insight into potential shared logistics pressures and localized land-use tensions. More broadly, the study demonstrates the value of spatial data mining techniques applied to free, publicly available data for identifying intersectoral industrial patterns that inform policy and infrastructure planning across North Dakota. Full article

Rapid preliminary reconnaissance of Critical Transportation Hubs (CTHs) by UAV swarms is vital during post-disaster rescue operations. However, limited camera fields of view, large heading variances, and GNSS multipath errors near massive steel-concrete structures complicate multi-view cooperative perception. This paper introduces a discrete, vision-based cooperative perception framework utilizing a decentralized anchor-wingman architecture. The pipeline integrates a Prob-IoU-optimized YOLO26m-OBB detector to extract oriented infrastructure footprints. To handle severe rotational discrepancies without IMU priors, a global scene registration cascade—combining SuperPoint and an Optimal Transport-driven LightGlue—is employed to establish robust geometric correspondences. Furthermore, a Projected Polygon Intersection over Union (Proj-IoU) mechanism, coupled with an RMSE-weighted spatial fusion strategy, dynamically associates and deduplicates overlapping targets across distributed views. Experimental results indicate that the framework achieves a low pixel-level RMSE of 2.12 pixels on the source domain and maintains a highly stable 2.36 pixels during zero-shot cross-domain testing (SUES-200 dataset), successfully resolving extreme heading variances up to 270°. The Proj-IoU mechanism resolves multi-source redundancies—collapsing overlapping projections by over 50%—bounding the localization error to approximately 1.06 m. Operating at 6.7 FPS on edge hardware via low-bandwidth tensor transmission, this system provides a rigorous geometric foundation for autonomous swarms, enabling downstream collision-free trajectory planning and Multi-Target Task Allocation (MTTA) in GNSS-denied environments. Full article

Reliable communication infrastructure is a fundamental component of Intelligent Transport Systems (ITSs), particularly in scenarios involving maritime corridors and emergency traffic management. In locations where optical fiber deployment is geographically constrained, unmanned aerial vehicle (UAV)-assisted free-space optical (FSO) relay links provide a flexible and rapidly deployable alternative. However, atmospheric attenuation, turbulence-induced fading, and wind-induced UAV misalignment can severely degrade link reliability and disrupt real-time transport data streams. This study proposes a payload-efficient multiple-input multiple-output free-space optical (MIMO-FSO) relay architecture based on a multi-output/single-input (MOSI) uplink and a single-output/multi-input (SOMI) downlink. Here, MOSI denotes multiple ground-based transmit apertures directed toward a single UAV receiving aperture, whereas SOMI denotes one UAV transmitting aperture serving multiple ground-based receiving apertures. Unlike conventional symmetric UAV-assisted MIMO-FSO relays that may duplicate diversity hardware on the aerial node, the proposed design shifts the parallel optical branches to the ground stations and keeps only one optical receiver and one optical transmitter on board the UAV. Under the adopted 4 × 4 comparison assumption, this reduces the UAV-side optical branch count from eight to two, corresponding to a 75% branch-count reduction proxy. System performance is evaluated over a 1.54 km relay link. The analytical framework describes Beer–Lambert attenuation, log-normal/gamma–gamma turbulence, and statistical pointing errors; in the OptiSystem implementation, their combined effects are represented by equivalent aggregate losses of 25 dB/km for atmospheric absorption/scattering and 25.5 dB/km for turbulence- and pointing-related degradation. Comparative simulations for SISO, 2 × 2, and 4 × 4 configurations show that the proposed 4 × 4 architecture increases the Q-factor from 8.38 to 18.25 and changes the OptiSystem-reported minimum BER from 2.73 × 10⁻¹⁷ to 9.95 × 10⁻⁷⁵. Because a finite simulation cannot statistically validate error probabilities of this magnitude through raw error counting, values far below 10⁻¹² are interpreted primarily as comparative indicators of receiver decision margin. The findings provide simulation-based evidence that the proposed architecture is a scalable candidate for resilient optical wireless backhaul in smart transport corridors under adverse propagation conditions. Full article

Structural health monitoring (SHM) systems based on Internet of Things (IoT) technologies have become an effective approach for continuous monitoring of bridge infrastructures. However, many wireless monitoring systems relying on LoRaWAN or contention-based communication suffer from packet collisions, unpredictable latency, and limited scalability when multiple sensing nodes operate simultaneously. To address these limitations, this study proposes a soft real-time LoRa-based IoT architecture for bridge SHM using a time division multiple access (TDMA) communication framework implemented on an embedded real-time platform. The proposed system integrates distributed vibration sensing nodes, a TDMA-enabled LoRa communication layer, an ESP32-based gateway, and a web-based monitoring database for remote visualization and analysis. The architecture leverages FreeRTOS (v10.4.3) for system-level task scheduling, enabling concurrent execution of sensing, communication, and networking processes across the dual-core ESP32-WROOM-32D platform. Experimental results obtained using a laboratory-scale cable-stayed bridge model demonstrate stable multi-node communication with a packet delivery ratio exceeding 95% and predictable TDMA-scheduled transmission cycles with TDMA slots of 100–200 ms under the evaluated operating conditions. The experiments validate end-to-end operation using a representative three-node deployment, while broader scalability is evaluated analytically through the TDMA capacity model and identified as future work for larger physical deployments. Full article

Soil liquefaction is a major seismic hazard that threatens coastal infrastructure. Yet, accurate prediction of the liquefaction factor of safety (FS) remains challenging because of the complex nonlinear interactions among geotechnical and stress-state variables. This study proposes a novel leakage-aware Hybrid Stacking Ensemble (HSE) framework for the direct prediction and geotechnical interpretation of FS in the Port Sudan coastal plain. The proposed model combines Random Forest, Extra Trees, and Gradient Boosting Regressors through a RidgeCV meta-learner while restricting the predictor set to independent spatial, geotechnical, and stress-state variables to prevent data leakage. A database comprising 534 Standard Penetration Test (SPT)-based observations from 61 boreholes was used to evaluate the proposed model against K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), Random Forest, and XGBoost. The Hybrid Stacking Ensemble achieved the highest predictive performance, with a test RMSE of 0.058, an MAE of 0.032, and an R² of 0.985, demonstrating improved accuracy, robustness, and generalization over the benchmark models. SHAP-based interpretation identified N1(60)cs as the dominant predictor of FS, followed by relative density, depth, and stress-related variables, confirming that the proposed framework learned physically meaningful relationships consistent with established liquefaction mechanics. The contribution of this work lies in the development of a Hybrid Stacking Ensemble framework for the direct prediction of the liquefaction factor of safety (FS), integrated with a leakage-free modeling workflow and explainable machine learning techniques, rather than in the development of a new machine learning algorithm. Full article

Sustainable rural development requires households to move beyond defensive medical spending and emergency borrowing toward more productive, forward-looking resource allocation. This study uses panel data from the China Household Finance Survey (CHFS), covering the 2017, 2019, and 2021 waves plus a newly released 2023 green-channel wave. We examine whether improvements in safe drinking water, clean cooking energy, and sanitation are associated with lower rural household economic vulnerability. We employ a staggered difference-in-differences design with household and year fixed effects, complemented by event–study tests, mediation analysis, and robustness checks. Environmental infrastructure improvements are significantly associated with lower child hospitalization and out-of-pocket medical expenditure, reduced reliance on high-cost informal credit, and higher income-generating asset shares. Mechanism analysis supports a “health–credit–income” channel, in which environmental improvements reduce preventable health shocks, ease emergency borrowing, and relax liquidity constraints on productive asset allocation. Threshold results further show that these financial-resilience benefits are strongest among households with the lowest baseline resource endowments. The study focuses on rural China, yet the identified health–credit–income mechanism offers a broader, scalable framework. Environmental infrastructure first reduces preventable disease burden, then eases emergency informal borrowing, and finally frees liquidity for income-generating assets. This sequence helps explain how environmental investment can create the financial preconditions for sustainable consumption and investment across developing economies. These findings offer micro-level evidence for integrating environmental infrastructure, rural financial resilience, and ESG social-value assessment. Full article

Urban water security in many cities in the Global South is increasingly challenged by ageing infrastructure and the presence of persistent chemical contaminants. This study investigated the Harare metropolitan water continuum between 2020 and 2024 using a longitudinal, systems-oriented observational framework encompassing wastewater discharge, surface water reservoirs, drinking water treatment, and municipal distribution networks. A three-stage approach was employed, comprising qualitative screening for selected pharmaceuticals at the Lake Chivero water–sediment interface in 2020, spatial assessment of physicochemical stability across the treatment and distribution system in 2021, and targeted qualitative evaluation of pharmaceutical and agrochemical occurrence in wastewater-impacted matrices in 2024. Sulfamethoxazole and trimethoprim were qualitatively identified using high-performance liquid chromatography (HPLC), while atrazine was confirmed by gas chromatography–mass spectrometry (GC–MS). These analyses indicated the continued presence of pharmaceutical and agrochemical residues within wastewater-impacted aquatic compartments associated with the Harare water supply. Physicochemical monitoring revealed elevated ammonia concentrations and reduced free residual chlorine across sections of the distribution network. These conditions coincided with detectable heterotrophic bacterial regrowth at distal consumer endpoints. Phenotypic antimicrobial susceptibility testing of bacterial isolates recovered at the source interface showed limited inhibition responses to sulfamethoxazole and trimethoprim under the experimental conditions used. While the observational nature of this study precludes causal inference, the co-occurrence of chemical residues, physicochemical instability, and bacterial isolates exhibiting reduced inhibition responses highlights conditions of potential relevance for antimicrobial resistance risk within wastewater-influenced urban water systems. These findings underscore the importance of integrated water management strategies addressing wastewater control, source water protection, and distribution system integrity within a One Health context. Full article

Vehicular Ad Hoc Networks (VANETs) are mobile networks that offer new services and communication between moving vehicles, roadside infrastructure, and a trusted authority. With the development of autonomous and connected vehicles, the issue of authentication in VANETs has become increasingly prominent due to the lack of mutual trust among network entities. However, standard authentication models for VANETs must account for total computational and communication overhead, regardless of the timing of authentication message generation. To address this limitation, this work proposes an advanced authentication paradigm for VANETs called the online/offline VANET framework, and formalizes this novel framework to realize lightweight authentication by shifting heavy computational overhead to the offline phase. The proposed model is divided into an offline phase and an online phase. In the offline phase of the free time before the message becomes available, it allows more powerful trusted authority to pre-compute, and in the online phase, resource-constrained devices only execute a small set of residual operations. Based on this model and a new identity-based signature, we give an efficient instantiation and use a mobile platform to evaluate it. The experimental results demonstrate that our construction achieves low online computational and communication overhead. Full article