Search Results (1,054)

For an existing bridge constructed with separate spans, the ends of adjacent flanges are disconnected. The problem of separated driving may occur at the bridgehead position after traffic conversion. The idea of integral splicing two separate spans of the existing long-span bridge is proposed. Direct crossing of a vehicle between the two separate spans of the existing long-span bridge can be realized. Firstly, the demand for integral splicing of the existing box girder bridge is analyzed using different methods. Then, an integral splicing composite structure (ISC-Structure) is designed and tested, and the corresponding design method is summarized. Finally, the construction technology for the ISC-Structure is optimized based on the actual field conditions. This research shows that the integral splicing demand of the old bridge can be obtained through on-site monitoring at the splicing position. Furthermore, the proposed random traffic flow simulation method can be applied to expand the data volume and verify the validity of the monitoring data. The proposed ISC-Structure meets the transverse splicing requirements of the Xinfengjiang Bridge. It can effectively connect the two separate spans, enabling them to work compositely and improving longitudinal mechanical properties. A layered and segmented construction scheme is proposed, and the relevant construction technology is optimized for the target integral splicing project. The proposed integral splicing design and construction technology can sever as a reference for similar long-span bridge extension projects. Full article

Autonomous surgery involves surgical tasks performed by a robot with minimal or no human involvement. Thanks to its precise automation, surgical robotics offers significant benefits in enhancing the consistency, safety, and quality of procedures, driving its growing popularity. However, ensuring the safety of autonomous surgical robotic systems remains a significant challenge. To address this, we propose a simulation-based validation method to detect potential safety issues in the software of surgical robotic systems, complemented by a digital twin to estimate the gap between simulation and reality. The validation framework consists of a test case generator and a monitor for validating properties and evaluating the performance of the robotic system during test execution. Using a robotic arm for needle insertion as a case study, we present a systematic test case generation method that ensures effective coverage measurement for a three-dimensional, irregular model. Since no simulation can perfectly replicate reality due to differences in sensing and actuation, the digital twin bridges the gap between simulation and the physical robotic arm. This integration enables us to assess the discrepancy between virtual simulations and real-world operations by verifying whether the data from the simulation accurately predicts real-world outcomes. Through extensive experimentation, we identified several flaws in the robotic software. Co-simulation within the digital twin framework has highlighted these discrepancies that should be considered. Full article

The long-term safety and operational reliability of zoned earth dams depend on the structural integrity of their internal components, including core, filters, and shell zones. This is particularly relevant for old dams which have been operational for a long period of time. Such existing infrastructure systems are exposed to various loading types over time, including environmental, seepage-related, extreme event, and climate change effects. As a result, even when they look intact externally, changes might affect their internal structure, composition, and possibly functionality. Thus, it is important to delineate a comprehensive and cost-effective strategy to identify potential issues and derive the health status of existing earth dams. This paper outlines a systematic approach for conducting a comprehensive health check of these structures through the implementation of a multi-epoch geotechnical approach based on a variety of standard measured and monitored quantities. The goal is to compare current properties with baseline data obtained during pre-, during-, and post-construction site investigation and laboratory tests. Guidance is provided on how to judge such multi-epoch comparisons, identifying potential outcomes and scenarios. The proposed approach is tested on a well-documented case study in Southern Italy, an area prone to climate change and subjected to very high seismic hazard. The case study demonstrates how the integration of historical and contemporary geotechnical data allows for the identification of critical zones requiring attention, the validation of numerical models, and the proactive formulation of targeted maintenance and rehabilitation strategies. This comprehensive, multi-epoch-based approach provides a robust and reliable assessment of dams’ health, enabling better-informed decision-making workflows and processes for asset management and risk mitigation strategies. Full article

Atmospheric correction (AC) over inland and coastal waters remains a key challenge in ocean color remote sensing, often limiting the effective use of satellite data for aquatic monitoring. AC algorithm performance is highly sensitive to water type and optical properties. To address this, we systematically evaluated six state-of-the-art AC algorithms—ACOLITE, C2RCC, iCOR, L2GEN, OC-SMART, and POLYMER—using Landsat-8/9 OLI data. This study leverages 440 high-quality in situ radiometric matchups spanning a wide range of aquatic environments, including inland lakes from China’s Satellite-Ground Synchronous Campaign and coastal waters from the globally distributed GLORIA dataset. These complementary datasets provide a robust benchmark for evaluating AC algorithm performance. A unified Optical Water Type (OWT) classification framework ensured consistency across environmental conditions. Results highlight significant variability in algorithm performance based on water type. In coastal waters, L2GEN demonstrated the lowest errors in visible bands, whereas OC-SMART achieved superior overall accuracy in inland waters. Notably, ACOLITE exhibited better performance than other algorithms in the blue spectral region (443 and 482 nm) for inland waters. OWT-specific analysis showed that OC-SMART maintained robust accuracy across the turbidity gradient, while ACOLITE and iCOR excelled in highly turbid waters (OWTs 5–6). In contrast, L2GEN, C2RCC, and POLYMER were more effective in clearer waters (OWTs 3–4). The study further discusses the applicability of each algorithm and offers recommendations for mitigating adjacency effects (AE) to improve AC accuracy. These findings provide valuable guidance for selecting and optimizing AC strategies for inland and coastal water monitoring. Full article

This study presents a comprehensive analysis aimed at validating the use of an innovative nanosensor based on graphitic nanomembranes for the smart monitoring of industrial wastewater. The validation of the potential of the nanosensor was carried out through the development of advanced analytical methodologies, a direct experimental comparison with commercially available electrode sensors commonly used for the detection of chemical species, and the evaluation of performance under conditions very similar to real-world field applications. The investigation involved a series of controlled experiments using an organic pollutant—benzoquinone—at varying concentrations. Initially, data analysis was performed using classical linear regression models, representing a conventional approach in chemical analysis. Subsequently, a more advanced methodology was implemented, incorporating machine-learning techniques to train a classifier capable of detecting the presence of pollutants in water samples. The study builds upon an experimental protocol previously developed by the authors for the nanomembranes, based on electrochemical impedance spectroscopy. The results clearly demonstrate that integrating the nanosensor with machine-learning algorithms yields significant performance. The intrinsic properties of the nanosensor make it well-suited for potential integration into field-deployable platforms, offering a real-time, cost-effective, and high-performance solution for the detection and quantification of contaminants in wastewater. These features position the nanomembrane-based sensor as a promising alternative to overcome current technological limitations in this domain. Full article

Electromagnetic induction (EMI) devices have become increasingly popular for their soil bulk properties, soil nutrient status, and use in taking non-invasive soil salinity measurements. However, the high cost of data acquisition (DAQ) systems has been a significant barrier to the widespread adoption of these devices. In this study, we addressed this challenge by developing a cost-effective, easy-to-use, open-source DAQ system, transferable to the end user. This system employs a Raspberry Pi 4 model, paired with various components, to monitor the speed and position of the EM38 (Geonics Ltd, Mississauga, ON, Canada) and compare these with a proprietary CR1000 system. Through our results, we demonstrate that the low-cost DAQ system can successfully extract the analogical signal from the device, which is strongly responsive to the variation in the soil’s physical properties. This cost-effective system is characterized by increased flexibility in software processes and provides performance comparable to the proprietary system in terms of its geospatial data and ECb measurements. This was validated by the strong correlation (R² = 0.98) observed between the data collected from both systems. With our zoning analysis, performed using the Kriging technique, we revealed not only similar patterns in the ECb data but also similar patterns to the Normalized Difference Vegetation Index (NDVI) map, suggesting that soil physical characteristics contribute to variability in crop vigor. Furthermore, the developed web application enabled real-time data monitoring and visualization. These findings highlight that the open-source DAQ system is a viable, cost-effective alternative for soil property monitoring in precision farming. Future enhancements will focus on integrating additional sensors for plant vigor and soil temperature, as well as refining the web application, supporting zone classification based on the use of multiple parameters. Full article

In 2024, Brazil experienced record-breaking wildfire activity, underscoring the escalating influence of climate change. This study examines the long-range transport of wildfire-generated aerosol plumes to São Paulo, combining multi-platform observations to trace their origin and properties. During August and September—a period marked by intense fire outbreaks in Pará and Mato Grosso do Sul—lidar measurements performed at São Paulo detected pronounced aerosol plumes. To investigate their source and characteristics, we integrated data from the Earth Cloud Aerosol and Radiation Explorer (EarthCARE) satellite, HYSPLIT back-trajectory modeling, and ground-based AERONET and Raman lidar measurements. Aerosol properties were derived from aerosol optical depth (AOD), Ångström exponent, and lidar ratio (LR) retrievals. Back-trajectory analysis identified three transport pathways originating from active fire zones, with coinciding AOD values (0.7–1.1) and elevated LR (60–100 sr), indicative of dense smoke plumes. Compositional analysis revealed a significant black carbon component, implicating wildfires near Corumbá (Mato Grosso do Sul) and São Félix do Xingu (Pará) as probable emission sources. These findings highlight the efficacy of satellite-based lidar systems, such as Atmospheric Lidar (ATLID) onboard EarthCARE, in atmospheric monitoring, particularly in data-sparse regions where ground instrumentation is limited. Full article

With the increasing demand for urban rail transit capacity, shield tunneling has become the predominant method for constructing underground metro systems in densely populated cities. However, the spatial interaction between shield tunnels and adjacent retaining structures poses significant engineering challenges, potentially leading to excessive ground settlement, structural deformation, and even stability failure. This study systematically investigates the deformation behavior and associated risks of retaining systems during adjacent shield tunnel construction. An orthogonal multi-factor analysis was conducted to evaluate the effects of grouting pressure, grout stiffness, and overlying soil properties on maximum surface settlement. Results show that soil cohesion and grouting pressure are the most influential parameters, jointly accounting for over 72% of the variance in settlement response. Based on the numerical findings, a Bayesian network model was developed to assess construction risk, integrating expert judgment and field monitoring data to quantify the conditional probability of deformation-induced failure. The model identifies key risk sources such as geological variability, groundwater instability, shield steering correction, segmental lining quality, and site construction management. Furthermore, the effectiveness and cost-efficiency of various grouting reinforcement strategies were evaluated. The results show that top grouting increases the reinforcement efficiency to 34.7%, offering the best performance in terms of both settlement control and economic benefit. Sidewall grouting yields an efficiency of approximately 30.2%, while invert grouting shows limited effectiveness, with an efficiency of only 11.6%, making it the least favorable option in terms of both technical and economic considerations. This research provides both practical guidance and theoretical insight for risk-informed shield tunneling design and management in complex urban environments. Full article

Carbon fiber reinforced polymer (CFRP) composites are prone to developing localized material loss defects during long-term service, which can severely degrade their mechanical properties and structural reliability. To address this issue, this study proposes a multi-sensor synchronous monitoring method combining embedded fiber Bragg grating (FBG) sensors and surface-mounted electrical resistance strain gauges. First, finite element simulations based on the three-dimensional Hashin damage criterion were performed to simulate the damage initiation and propagation processes in CFRP laminates, revealing the complete damage evolution mechanism from initial defect formation to progressive failure. The simulations were also used to determine the optimal sensor placement strategy. Subsequently, tensile test specimens with prefabricated defects were prepared in accordance with ASTM D3039, and multi-sensor monitoring techniques were employed to capture multi-parameter, dynamic data throughout the damage evolution process. The experimental results indicate that embedded FBG sensors and surface-mounted strain gauges can effectively monitor localized material loss defects within composite laminate structures. Strain gauge measurements showed uniform strain distribution at all measuring points in intact specimens (with deviations less than 5%). In contrast, in defective specimens, strain values at measurement points near the notch edge were significantly higher than those in regions farther from the notch, indicating that the prefabricated defect disrupted fiber continuity and induced stress redistribution. The combined use of surface-mounted strain gauges and embedded FBG sensors was demonstrated to accurately and reliably track the damage evolution behavior of defective CFRP laminates. Full article

Annealing of steel coils in bell-type furnaces is a critical process in steel production, requiring precise temperature control to ensure desired mechanical properties and microstructure. However, direct measurement of inner coil temperatures is impractical in industrial conditions, necessitating model-based estimation. This study presents a comparative analysis of physics-based and machine learning (ML) approaches for predicting internal temperatures during annealing. A finite difference method (FDM) was developed as a physics-based model and validated against experimental data from both laboratory and industrial annealing cycles. Furthermore, several ML models, including support vector regression (SVR), neural networks (NN), multivariate adaptive regression splines (MARS), k-nearest neighbors (k-NN), and random forests (RFs), were trained on surface temperature measurements to predict inner temperatures. The results demonstrate that the MARS, k-NN, and RF models achieved high prediction accuracy with performance index (PI) values below 1.0 on unseen data, demonstrating excellent generalization capabilities. In contrast, SVR with polynomial kernels and NN exhibited poor performance in specific configurations, highlighting their sensitivity to overfitting and data variability. The findings suggest that combining physics-based models with data-driven techniques offers a robust framework for soft-sensing in annealing operations, enabling improved process monitoring and control. Full article

The estimation of soil hydraulic properties—such as water retention and hydraulic conductivity—is essential for irrigation management and agro-hydrological modeling. This study presents the development and application of SOILHP, a low-cost, IoT-integrated device designed to monitor laboratory evaporation experiments for the estimation of soil hydraulic properties using inverse modeling tools. SOILHP incorporates mini-tensiometers, a precision balance, microcontrollers, and cloud-based data logging via Google Sheets. SOILHP enables the remote, real-time acquisition of soil pressure head and mass variation data without the need for commercial dataloggers. Evaporation experiments were conducted using undisturbed soil samples, and inverse modeling with Hydrus-1D was used to estimate van Genuchten–Mualem parameters. The optimized parameters showed low standard errors and narrow 95% confidence intervals, demonstrating the robustness of the inverse solution, confirming the device’s sensors accuracy. Forward simulations of internal drainage were performed to estimate the field capacity under different drainage flux criteria. The field capacity results aligned with values reported in the literature for tropical soils. Overall, SOILHP proved to be a reliable and economically accessible alternative for monitoring evaporation experiments aimed at fitting parameters of analytical functions that describe water retention and hydraulic conductivity properties within the soil pressure head range relevant to agriculture. Full article

Earth Pressure Balance (EPB) shield machines have been widely used in subway construction due to their versatility and safety. During the shield tunneling process, the earth pressure in the shield machine chamber is crucial for controlling ground settlement and ensuring the safety of surrounding buildings. However, current research on the temporal and spatial evolution of earth pressure in water-rich sand layers and its relationship with ground settlement is relatively insufficient. This study focuses on the shield tunneling project between Liuzhou East Road and Puzhou Road on Nanjing Metro Line 11. First, laboratory and on-site tests were conducted to optimize the slump properties of the sediment. Then, based on Terzaghi’s theory and statistical methods, the temporal and spatial evolution trends of the earth pressure in the shield chamber under water-rich sand conditions were explored. Finally, by adjusting earth pressure control parameters on-site and monitoring ground settlement, the impact of earth pressure changes on ground settlement was analyzed. Results showed a linear correlation between the actual earth pressure and shield burial depth. For water-rich sand with medium permeability, the theoretical earth pressure was calculated using Terzaghi’s water-soil combined method in shallow sections, and the average of combined and separated methods in deep sections. The decay envelope showed an exponential downward trend, with rapid decay initially and slower decay later. As earth pressure control values increased, pre-consolidation settlement increased, instantaneous settlement decreased, pre-consolidation settlement rate slightly increased, and instantaneous settlement rate decreased. When excavation pressure was below theoretical pressure, higher instantaneous settlement rates could threaten surface structures. This research offers vital theoretical and data references for shield tunneling in water-rich sand layers and supports related EPB shield machine theory studies. Full article

Amid accelerating climate change, climate-related hazards—such as floods, wildfires, hurricanes, and sea-level rise—increasingly drive land transformations and pose growing risks to housing markets by affecting property valuations, insurance availability, mortgage performance, and broader financial stability. This review synthesizes recent progress in two distinct domains and their linkage: (1) assessing climate-related financial risks in housing markets, and (2) applying AI-driven remote sensing for hazard detection and land transformation monitoring. While both areas have advanced significantly, important limitations remain. Existing housing finance studies often rely on static models and coarse spatial data, lacking integration with real-time environmental information, thereby reducing their predictive power and policy relevance. In parallel, remote sensing studies using AI primarily focus on detecting physical hazards and land surface changes, yet rarely connect these spatial transformations to financial outcomes. To address these gaps, this review proposes an integrative framework that combines AI-enhanced remote sensing technologies with financial econometric modeling to improve the accuracy, timeliness, and policy relevance of climate-related risk assessment in housing markets. By bridging environmental hazard data—including land-based indicators of exposure and damage—with financial indicators, the framework enables more granular, dynamic, and equitable assessments than conventional approaches. Nonetheless, its implementation faces technical and institutional barriers, including spatial and temporal mismatches between datasets, fragmented regulatory and behavioral inputs, and the limitations of current single-task AI models, which often lack transparency. Overcoming these challenges will require innovation in AI modeling, improved data-sharing infrastructures, and stronger cross-disciplinary collaboration. Full article

The development of innovative and environmentally sustainable construction materials is a strategic priority in the context of the ecological transition and circular economy. Geopolymers and alkali-activated materials, derived from industrial and construction waste rich in aluminosilicates, are gaining increasing attention as low-carbon alternatives to ordinary Portland cement (OPC), which remains one of the main contributors to anthropogenic CO₂ emissions and landfill-bound construction waste. This review provides a comprehensive analysis of geopolymer-based solutions for building and architectural applications, with a particular focus on modular multilayer panels. Key aspects, such as chemical formulation, mechanical and thermal performance, durability, technological compatibility, and architectural flexibility, are critically examined. The discussion integrates considerations of disassemblability, reusability, and end-of-life scenarios, adopting a life cycle perspective to assess the circular potential of geopolymer building systems. Advanced fabrication strategies, including 3D printing and fibre reinforcement, are evaluated for their contribution to performance enhancement and material customisation. In parallel, the use of parametric modelling and digital tools such as building information modelling (BIM) coupled with life cycle assessment (LCA) enables holistic performance monitoring and optimisation throughout the design and construction process. The review also explores the emerging application of artificial intelligence (AI) and machine learning for predictive mix design and material property forecasting, identifying key trends and limitations in current research. Representative quantitative indicators demonstrate the performance and environmental potential of geopolymer systems: compressive strengths typically range from 30 to 80 MPa, with thermal conductivity values as low as 0.08–0.18 W/m·K for insulating panels. Life cycle assessments report 40–60% reductions in CO₂ emissions compared with OPC-based systems, underscoring their contribution to climate-neutral construction. Although significant progress has been made, challenges remain in terms of long-term durability, standardisation, data availability, and regulatory acceptance. Future perspectives are outlined, emphasising the need for interdisciplinary collaboration, digital integration, and performance-based codes to support the full deployment of geopolymer technologies in sustainable building and architecture. Full article

Accurate and spatially detailed soil information is essential for supporting sustainable land use planning, particularly in data-scarce regions such as Cyprus, where soil degradation risks are intensified by land fragmentation, water scarcity, and climate change pressure. This study aimed to generate national-scale predictive maps of key soil health descriptors by integrating satellite-based indicators with a recently released geo-referenced soil dataset. A machine learning model was applied to estimate a suite of soil properties, including organic carbon, pH, texture fractions, macronutrients, and electrical conductivity. The resulting maps reflect spatial patterns consistent with previous studies focused on Cyprus and provide high resolution insights into degradation processes, such as organic carbon loss, and salinization risk. These outputs provide added value for identifying priority zones for soil conservation and evidence-based land management planning. While predictive uncertainty is greater in areas lacking ground reference data, particularly in the northeastern part of the island, the modeling framework demonstrates strong potential for a national-scale soil health assessment. The outcomes are directly relevant to ongoing soil policy developments, including the forthcoming Soil Monitoring Law, and provide spatial prediction models and indicator maps that support the assessment and mitigation of soil degradation. Full article