Search Results (288)

Subsurface mass concrete infrastructure—including immersed tunnels, dams, and nuclear waste containment systems—frequently faces calcium-leaching risks from prolonged groundwater exposure. An anisotropic stress-leaching damage model incorporating microcrack propagation is developed for underground concrete’s chemo–mechanical coupling. This model investigates stress-induced anisotropy in concrete through the evolution of oriented microcrack networks. The model incorporates nonlinear anisotropic plastic strain from coupled chemical–mechanical damage. Unlike conventional concrete rheology, this model characterizes chemical creep through stress-chemical coupled damage mechanics. The numerical model is incorporated within COMSOL Multiphysics to perform coupled multiphysics simulations. A close match is observed between the numerical predictions and experimental findings. Under high stress loads, calcium leaching and mechanical stress exhibit significant coupling effects. Regarding concrete durability, chemical degradation has a more pronounced effect on concrete’s stiffness and strength reduction compared with stress-generated microcracking. Full article

Postharvest losses amongst small-scale farmers in developing countries are high due to inadequate resources and infrastructure. Among the various affected crops, tomatoes are particularly vulnerable; however, studies on postharvest losses of most fruits and vegetables are limited. Therefore, this study aimed to assess postharvest tomato losses under different production systems within the small-scale supply chain using the indirect assessment (questionnaires and interviews) and direct quantification of losses. Farmers reported tomato losses due to insects (82.35%), cracks, bruises, and deformities (70.58%), and diseases (64.71%). Chemical sprays were the main form of pest and disease control reported by all farmers. The direct quantification sampling data revealed that 73.07% of the tomatoes were substandard at the farm level, with 47.92% and 25.15% categorized as medium-quality and poor-quality, respectively. The primary contributors to the losses were decay (39.92%), mechanical damage (31.32%), and blotchiness (27.99%). Postharvest losses were significantly higher under open-field production systems compared to closed tunnels. The fungi associated with decay were mainly Geotrichum, Fusarium spp., and Alternaria spp. These findings demonstrate the main drivers behind postharvest losses, which in turn highlight the critical need for intervention through training and support, including the use of postharvest loss reduction technologies to enhance food security. Full article

Despite significant advancements in indoor navigation technology over recent decades, it still faces challenges due to excessive dependency on external infrastructure and unreliable positioning in complex environments. This paper proposes an autonomous localization system that integrates advanced adaptive pedestrian dead reckoning (APDR) and binocular vision, designed to provide a low-cost, high-reliability, and high-precision solution for rescuers. By analyzing the characteristics of measurement data from various body parts, the chest is identified as the optimal placement for sensors. A chest-mounted advanced APDR method based on dynamic step segmentation detection and adaptive step length estimation has been developed. Furthermore, step length features are innovatively integrated into the visual tracking algorithm to constrain errors. Visual data is fused with dead reckoning data through an extended Kalman filter (EKF), which notably enhances the reliability and accuracy of the positioning system. A wearable autonomous localization vest system was designed and tested in indoor corridors, underground parking lots, and tunnel environments. Results show that the system decreases the average positioning error by 45.14% and endpoint error by 38.6% when compared to visual–inertial odometry (VIO). This low-cost, wearable solution effectively meets the autonomous positioning needs of rescuers in disaster scenarios. Full article

The identification of structural damage types remains a key challenge in electromechanical impedance/admittance (EMI/EMA)-based structural health monitoring realm. This paper proposed a damage classification approach for concrete structures by using integrating discrete wavelet transform (DWT) decomposition of EMA signatures with supervised machine learning. In this approach, the EMA signals of arranged piezoelectric ceramic (PZT) patches were successively measured at initial undamaged and post-damaged states, and the signals were decomposed and processed using the DWT technique to derive indicators including the wavelet energy, the variance, the mean, and the entropy. Then these indicators, incorporated with traditional ones including root mean square deviation (RMSD), baseline-changeable RMSD named RMSDk, correlation coefficient (CC), and mean absolute percentage deviation (MAPD), were processed by a support vector machine (SVM) model, and finally damage type could be automatically classified and identified. To validate the approach, experiments on a full-scale reinforced concrete (RC) slab and application to a practical tunnel segment RC slab structure instrumented with multiple PZT patches were conducted to classify severe transverse cracking and minor crack/impact damages. Experimental and application results cogently demonstrated that the proposed DWT-based approach can precisely classify different types of damage on concrete structures with higher accuracy than traditional ones, highlighting the potential of the DWT-decomposed EMA signatures for damage characterization in concrete infrastructure. Full article

As the world’s deepest hydraulic tunnels, the Jinping ultra-deep tunnels provide world-class conditions for research on deep rock mechanics under extreme conditions. This study analyzed the time-dependent behavior of different tunneling sections in the Jinping tunnels using the Nishihara creep model implemented in Abaqus. Validated numerical simulations of representative cross-sections at 1400 m and 2400 m depths in the diversion tunnel reveal that long-term creep deformations (over a 20-year period) substantially exceed instantaneous excavation-induced displacements. The stress concentrations and strain magnitudes exhibit significant depth dependence. The maximum principal stress at a 2400 m depth reaches 1.71 times that at 1400 m, while the vertical strain increases 1.46-fold. Based on this, the long-term mechanical behavior of the surrounding rock during the expansion of the Jinping auxiliary tunnel was further calculated and predicted. It was found that the stress concentration at the top and bottom of the left sidewall increases from 135 MPa to 203 MPa after expansion, identifying these as critical areas requiring focused monitoring and early warnings. The total deformation of the rock mass increases by approximately 5 mm after expansion, with the cumulative deformation reaching 14 mm. Post-expansion deformation converges within 180 days, with creep deformation of 2.5 mm–3.5 mm observed in both sidewalls, accounts for 51.0% of the total deformation during expansion. The surrounding rock reaches overall stability three years after the completion of expansion. These findings establish quantitative relationships between the excavation depth, time-dependent deformation, and stress redistribution and support the stability design, risk management, and infrastructure for ultra-deep tunnels in a stress state at a 2400 m depth. These insights are critical to ensuring the long-term stability of ultra-deep tunnels and operational safety assessments. Full article

Waterway tunnels, a novel type of infrastructure designed for inland waterways in mountainous gorge regions, have seen rapid development in recent years. However, their unique structural characteristics and specific shipping activities pose significant risks in the event of an accident. To enhance the scientific rigor and efficiency of emergency responses to vessel incidents in tunnels, this study focuses on fire accidents in waterway tunnels. Considering the unique challenges of emergency response in such scenarios, we propose an emergency response framework using Business Process Modeling Notation (BPMN). The framework is mapped into a Petri net model encompassing three key stages: detection and early warning, emergency response actions, and recovery. A Colored Hierarchical Timed Petri Net (CHTPN) emergency response model is then developed based on fire incident data and emergency response time functions. Furthermore, a homomorphic Markov chain is employed to assess the network’s validity and performance. Finally, optimization strategies are proposed to improve the emergency response process. The results indicate that the emergency response network demonstrates strong accessibility, effectively mitigating information bottlenecks in critical stages of the response process. The network provides accurate and rapid decision support for different tunnel ship fire scenarios, efficiently and reasonably allocating emergency resources and response teams, and monitoring the operation of key emergency response stages. This enhances the efficiency of emergency operations and provides robust support for decision-making in waterway tunnel fire emergencies. Full article

This study presents a 3D fracture network modeling approach for designing curtain grouting systems in building foundations, utilizing geological mapping data from the Dongzhuang Project. A one-dimensional Markov chain model is applied to simulate the transitions in fracture density, while fracture orientation and size are characterized using Fisher and statistical distribution models. To enhance the prediction accuracy, a correction method is introduced to refine the transition matrices. The model’s reliability is validated using tunnel wall fracture data and borehole detection, demonstrating strong agreement in both trend and magnitude. In under 100,000 simulations, when the allowable absolute error is set to 1, the optimal accuracy can reach 80%. Reliability analysis confirms the robustness of the approach, with 99.91% of predictions within a ± 2 error margin. The final fracture network model effectively captures spatial heterogeneity and fracture penetration across various foundation layers; the spatial distribution density index of fractures can provide a reference basis for optimizing the layout of impermeable curtains in complex geological conditions. This integrated modeling approach offers a reliable tool for improving grouting strategies in building foundation projects and other civil infrastructure. Full article

The large-scale reuse of shield tunneling muck remains a major challenge in urban construction. This study proposes a geopolymeric-controlled low-strength material (GC-CLSM) utilizing shield tunneling muck as the primary raw material and a novel alkali-activated binder composed of ground granulated blast-furnace slag (GGBFS) and carbide slag (CS). Emphasis is placed on early-age strength development and its underlying mechanisms, which were often overlooked in previous CLSM studies. Among the tested mixtures, a GGBFS:CS ratio of 80:20 yielded the best balance between early and long-term strength. Its 1-day UCS reached 1.18–1.75 MPa, representing a 6.3–23.6-fold increase over the low-CS reference (90:10), which achieved only 0.05–0.31 MPa. However, excessive CS content (e.g., 60:40) led to a significant reduction in the 28-day strength—up to nearly 50% compared with the 90:10 mix—due to impaired microstructural densification. Microstructural analyses (pore-solution pH, SEM, EDS, XRD, FTIR, LF-NMR) confirmed that higher CS levels enhanced early C–A–S–H gel formation by increasing OH⁻ and Ca²⁺ availability while compromising long-term structure. Additionally, the GC-CLSM system reduced carbon emissions by 68.6–70.3% per ton of treated shield tunneling muck compared with conventional cement-based CLSM. Overall, this study offers a sustainable and performance-driven approach for the valorization of shield tunneling muck, enabling the development of early-strength controllable, low-carbon CLSM for infrastructure applications. Full article

The continuous and reliable operation of autonomous systems is important for farm management decision making, whether such systems perform crop monitoring using imaging systems or crop handling in pruning and harvesting applications using robotic manipulators. Autonomous systems, including robotic ground vehicles, drones, and tractors, are major research efforts of precision crop management. However, these systems may be less effective or require specific customizations for planting systems in low tunnels, high tunnels, or other environmentally controlled enclosures. In this work, a compact and lightweight crop management platform is developed that uses overhead power cables for continuous operation over row crops, requiring less human intervention and independent of the ground terrain conditions. The platform does not carry batteries onboard for its operation, but rather pulls power from overhead cables, which it also uses to navigate over crop rows. It is developed to be modular, with the top section consisting of mobility and power delivery and the bottom section addressing a custom task, such as incorporating additional sensors for crop monitoring or manipulators for crop handling. This prototype illustrates the infrastructure, locomotive mechanism, and sample usage of the system (crop imaging) in the application of low-tunnel-covered strawberries; however, there is potential for other row crop systems with regularly spaced support structures to adopt this platform as well. Full article

The spatiotemporal evolution of coastal rural settlements varies significantly across different geomorphic environments, yet this variation is underexplored in current research. Guided by Coupled Human and Natural Systems, this study examines the adaptation mechanisms between coastal rural settlements and landforms using an integrated framework that combines various bay types, spatiotemporal characteristics, and dynamic drivers. Four representative bay types along Guangdong’s coast were analyzed: Hilly Ria Coast, Platform Ria Coast, Barrier-Lagoon Coast, and Estuarine Delta Coast. Using multi-source remote sensing data and optimized Geodetector modeling (1972 vs. 2022), we identified the patterns of spatiotemporal evolution and their driving forces. The results reveal distinct adaptation pathways: Hilly Ria Coast settlements expanded in a constrained manner, supported by tunnel–bridge infrastructure; Platform Ria Coasts developed multi-nucleated, port-oriented clusters through harbor-linked road networks; Barrier-Lagoon Coasts achieved balanced growth through integrated land–river–sea governance; and Estuarine Delta Coasts experienced urban–rural restructuring accompanied by water network degradation. This study proposes governance strategies tailored to specific landforms to support sustainable coastal planning. Full article

Abrasive waterjet technology is a promising sustainable and green technology for cutting underground structures. Abrasive waterjet usage in demolition promotes sustainable and green construction practices by reduction of noise, dust, secondary waste, and disturbances to the surrounding infrastructure. In this study, a numerical framework based on a coupled Smoothed Particle Hydrodynamics (SPH)–Finite Element Method (FEM) algorithm incorporating the Riedel–Hiermaier–Thoma (RHT) constitutive model is proposed to investigate the damage mechanism of concrete subjected to abrasive waterjet. Numerical simulation results show a stratified damage observation in the concrete, consisting of a crushing zone (plastic damage), crack formation zone (plastic and brittle damage), and crack propagation zone (brittle damage). Furthermore, concrete undergoes plastic failure when the shear stress on an element exceeds 5 MPa. Brittle failure due to tensile stress occurs only when both the maximum principal stress (σ₁) and the minimum principal stress (σ₃) are greater than zero at the same time. The damage degree ($\chi$) of the concrete is observed to increase with jet diameter, concentration of abrasive particles, and velocity of jet. A series of orthogonal tests are performed to analyze the influence of velocity of jet, concentration of abrasive particles, and jet diameter on the damage degree and impact depth (h). The parametric numerical studies indicates that jet diameter has the most significant influence on damage degree, followed by abrasive concentration and jet velocity, respectively, whereas the primary determinant of impact depth is the abrasive concentration followed by jet velocity and jet diameter. Based on the parametric analysis, two optimized abrasive waterjet configurations are proposed: one tailored for rock fragmentation in tunnel boring machine (TBM) operations; and another for cutting reinforced concrete piles in shield tunneling applications. These configurations aim to enhance the efficiency and sustainability of excavation and tunneling processes through improved material removal performance and reduced mechanical wear. Full article

Harmful gases produced by diesel locomotives tend to accumulate within tunnels, posing risks such as dizziness, vomiting, coma, and even death to the working staff, particularly in long tunnels with high traffic density. As the number of such structures increases, ventilation in extra-long tunnels represents a critical challenge within the engineering area. In this study, the ventilation of an extra-long single-bore double-track tunnel operating with diesel locomotives is investigated. Through scale model tests and based on the inspection sensor data, the natural diffusion patterns of harmful gases under various operating conditions were elucidated. Based on the local resistance coefficient optimization theory and numerical simulations, the ventilation shafts of the tunnel were optimally designed, and an overall ventilation scheme was developed. The ventilation effect of the tunnel was verified through improved scale model tests. The results show that harmful gases primarily diffuse towards the higher elevation tunnel entrance, with only gases near the lower entrance escaping from it. Under the same operating conditions, NO₂ diffuses more slowly than CO, making it harder to discharge. Applying the local resistance coefficient optimization theory, the inclined and vertical shafts of the tunnel can be effectively optimized. The optimized ventilation shafts, coupled with jet fans, can reduce harmful gas concentrations below safety limits within one minute. The methodologies and findings presented here can offer valuable guidance for the ventilation design of similar infrastructures. Full article

Anchored slopes have been widely adopted globally, yet their design faces two critical challenges: the unreliable characterization of geomaterial variability and the urgent need for low-carbon. To address the first challenge, we propose a novel sensitivity index of variability (SIV) formulated through Monte Carlo Simulation (MCS), which systematically quantifies robustness within reliability-based design. Meanwhile, in response to global low-carbon initiatives, carbon emission metrics are innovatively established as the optimization objective for robust geotechnical design (RGD), replacing conventional cost-centric approaches. Building on these methodological advancements, we develop a framework that simultaneously optimizes engineering robustness and environmental sustainability. The operational efficacy of this framework is demonstrated through a case study of a rock slope at the tailwater tunnel outlet of a hydropower station in southwest China. Comparative analysis with the initial design solution reveals that our framework achieves higher robustness while reducing carbon emissions, demonstrating the superior performance of this framework. This study offers a systematic approach to advance both safety and sustainability in geotechnical infrastructure. The proposed methodology is readily adaptable to other earth structures facing similar reliability–environmental trade-offs. Full article

Submerged tunnels are an innovative infrastructure solution for connecting roads and railways, especially in areas where conventional bridge or overland tunnel construction is limited by deep waterways, narrow straits, or dense urban development. In such regions, submerged tunnels offer an efficient and less intrusive alternative that overcomes geographical constraints. However, unlike conventional ground-level or subsea tunnels, submerged tunnels have unique structural and environmental characteristics, which necessitate the development of a dedicated evaluation system for responding to fire and other disasters. In this study, a quantitative fire risk assessment program (SFT_QRA) was developed by reflecting the specific characteristics of submerged tunnels. The program was applied to both road and railway tunnels to obtain evaluation results. First, to more realistically reflect the fire risk within submerged tunnels, the latest statistical data were used to update fire occurrence probabilities and the proportion of vulnerable users. In addition, the optimal smoke control mode for structural stop zones in ultra-long tunnels was analyzed to derive strategies for establishing a safe evacuation environment. Second, an Excel VBA-based assessment program was developed to improve user convenience and was structured to enable fire analysis and evacuation simulations. Third, in order to verify the accuracy and reliability of the developed program, a comparative analysis was conducted against commercial quantitative risk assessment programs. As a result, the total risk error rate was 0.4% for road tunnels and within 5.0% for railway tunnels, showing similar levels of results. This study advances quantitative risk assessment methods by incorporating the unique features of submerged tunnels and implementing them in a validated program. Through this approach, it presents a practical solution that can contribute to the advancement of tunnel fire safety technologies and the overall enhancement of tunnel safety. Full article

Structural health monitoring (SHM) plays a vital role in ensuring the safety, durability, and performance of civil infrastructure. This review delves into the significant advancements in optical fiber sensor (OFS) technologies such as Fiber Bragg Gratings, Distributed Temperature Sensing, and Brillouin-based systems, which have emerged as powerful tools for enhancing SHM capabilities. Offering high sensitivity, resistance to electromagnetic interference, and real-time distributed monitoring, these sensors present a superior alternative to conventional methods. This paper also explores the integration of OFSs with Artificial Intelligence (AI), which enables automated damage detection, intelligent data analysis, and predictive maintenance. Through case studies across key infrastructure domains, including bridges, tunnels, high-rise buildings, pipelines, and offshore structures, the review demonstrates the adaptability and scalability of these sensor systems. Moreover, the role of SHM is examined within the broader context of civil and urban infrastructure, where IoT connectivity, AI-driven analytics, and big data platforms converge to create intelligent and responsive infrastructure. While challenges remain, such as installation complexity, calibration issues, and cost, ongoing innovation in hybrid sensor networks, low-power systems, and edge computing points to a promising future. This paper offers a comprehensive amalgamation of current progress and future directions, outlining a strategic path for next-generation SHM in resilient urban environments. Full article