Search Results (139)

Railway axles are among the most safety-critical components in rolling stock, as their failure can lead to catastrophic consequences. One of the most subtle damage mechanisms affecting these components is fretting fatigue, which is a particularly challenging damage mechanism in these components, as it can initiate cracks under real service conditions and is difficult to detect in its early stages, which is vital to ensure operational safety and to optimize maintenance strategies. This paper focuses on the development of fretting fatigue damage in solid railway axles under realistic service-like conditions. Full-scale axle specimens with artificially induced notches were subjected to loading conditions that promote fretting fatigue crack initiation and growth. Acoustic emission techniques were used to monitor the damage progression, and post-processing of the emitted signals, by using wavelet-based tools, was conducted to identify early indicators of crack formation. The experimental findings demonstrate that the proposed approach allows for reliable identification of fretting-induced crack initiation, contributing valuable insights into the in-service behavior of railway axles under this damage mechanism. Full article

To reveal the meso-mechanical essence of deep rock mass failure and capture precursor information, this study focuses on soft rock failure mechanisms. Based on the discontinuous medium discrete element method (DEM), we employed digital image correlation (DIC) technology, acoustic emission (AE) monitoring, and particle flow code (PFC) numerical simulation to investigate the failure evolution characteristics and AE quantitative representation of soft rocks. Key findings include the following: Localized high-strain zones emerge on specimen surfaces before macroscopic crack visualization, with crack tip positions guiding both high-strain zones and crack propagation directions. Strong force chain evolution exhibits high consistency with the macroscopic stress response—as stress increases and damage progresses, force chains concentrate near macroscopic fracture surfaces, aligning with crack propagation directions, while numerous short force chains coalesce into longer chains. The spatial and temporal distribution characteristics of acoustic emissions were explored, and the damage types were quantitatively characterized, with ring-down counts demonstrating four distinct stages: sporadic, gradual increase, stepwise growth, and surge. Shear failures predominantly occurred along macroscopic fracture surfaces. At the same time, there is a phenomenon of acoustic emission silence in front of the stress peak in the surrounding rock of deep soft rock roadway, as a potential precursor indicator for engineering disaster early warning. These findings provide critical theoretical support for deep engineering disaster prediction. Full article

The rise of hybrid electric vehicles (HEVs) marks a shift away from traditional engines driven by environmental and economic concerns. With the rapid growth of HEVs worldwide, their reliability becomes of utmost concern; thus, guaranteeing the proper operation of HEVs is a crucial quest. Condition-based monitoring (CBM), which intends to observe different kinds of parameters in the system to detect defects and reduce any unwanted breakdowns and equipment failure, plays an efficient role in enhancing HEVs’ reliability and ensuring their healthy operation. The permanent magnet machine (PMM) is the most used electric machine in the electric propulsion system of HEVs, as well as the most expensive. Hence, the condition monitoring of this machine is of great importance. The magnet crack is one of the most severe faults that may arise in this machine. Artificial intelligence (AI) is showing high capability in the field of CBM, fault detection, and fault identification and prevention. Hence, the aim of this paper is to present two data-based fault detection approaches, which are the support vector machine (SVM) and the Hidden Markov Model (HMM). Their capability to detect primitive faults like tiny cracks in the machine’s magnet will be shown. Applying and evaluating various CBM methods is essential to identifying the most effective approach to maximizing reliability, minimizing downtime, and optimizing maintenance strategies. A strategy to specify the remaining useful life (RUL) of the defected element is proposed. Full article

Simulation experiments are a crucial method for investigating overburden failure, strata movement, and strata control during coal mining. However, traditional similar materials struggle to effectively monitor internal damage, fracturing, and dynamic development processes within the strata during mining. To address this issue, carbon fibers were introduced into the field of similar material simulation experiments for mining. Leveraging the excellent conductivity and the sensitive feedback of resistivity changes in response to damage of this composite material enabled real-time monitoring of internal damage and fracture patterns within the mining strata during similar simulation experiments, leading to the development of a carbon fiber similar simulation composite material with damage self-sensing properties. This study found that as the carbon fiber content increased, the evolution patterns of the electrical resistance change rate and the damage coefficient of the similar material tended to coincide. When the carbon fiber content in the similar material exceeded 2%, the electrical resistance change rate and the damage coefficient consistently exhibited synchronized growth with identical increments. A similar simulation experiment revealed that after the completion of workface mining, the thick sandstone aquifer did not develop significant cracks and remained stable. In the early stages of mining, damage rapidly accumulated at the bottom of the thick aquifer, approaching the failure threshold. In the middle layers, a step-like increase in the damage coefficient occurred after mining reached a certain width, while the top region was less affected by mining activities, resulting in less significant damage development. The research findings offer new experimental insights into rock layer movement and control studies, providing theoretical guidance for the prediction, early warning, and prevention of dynamic disasters in mines with thick key layers. Full article

Water, an essential asset for life and growth, is under growing pressure due to climate change, overpopulation, pollution, and industrialization. At the same time, water distribution within cities relies on piping networks that are over 30 years old and thereby prone to leaks, cracking, and losses. Taking this into account, non-revenue water (i.e., water that is distributed to homes and facilities but not returning revenues) is estimated at almost 50%. To this end, intelligent water management via computational advanced tools is required in order to optimize water usage, to mitigate losses, and, more importantly, to ensure sustainability. To address this issue, a case study was developed in this paper, following a step-by-step methodology for the city of Heraklion, Greece, in order to introduce an intelligent water management system that integrates advanced technologies into the aging water distribution infrastructure. The first step involved the digitalization of the network’s spatial data using geographic information systems (GIS), aiming at enhancing the accuracy and accessibility of water asset mapping. This methodology allowed for the creation of a framework that formed a “digital twin”, facilitating real-time analysis and effective water management. Digital twins were developed upon real-time data, validated models, or a combination of the above in order to accurately capture, simulate, and predict the operation of the real system/process, such as water distribution networks. The next step involved the incorporation of a hydraulic simulation and modeling tool that was able to analyze and calculate accurate water flow parameters (e.g., velocity, flowrate), pressure distributions, and potential inefficiencies within the network (e.g., loss of mass balance in/out of the district metered areas). This combination provided a comprehensive overview of the water system’s functionality, fostering decision-making and operational adjustments. Lastly, automatic meter reading (AMR) devices could then provide real-time data on water consumption and pressure throughout the network. These smart water meters enabled continuous monitoring and recording of anomaly detections and allowed for enhanced control over water distribution. All of the above were implemented and depicted in a web-based environment that allows users to detect water meters, check water consumption within specific time-periods, and perform real-time simulations of the implemented water network. Full article

Foamed asphalt cold recycled mixtures can provide an effective approach for the reutilization of reclaimed asphalt pavement (RAP), but conventional asphalt foaming technology primarily exploits matrix asphalt as the raw material. To address this issue, this study explores rubberized asphalt with cold recycling technology to develop a foamed rubber asphalt cold recycled mixture (FRCM). The semi-circular bending (SCB) test was employed to investigate its cracking resistance. Load–crack mouth opening displacement (CMOD)–time curves under various temperatures were analyzed, and digital image technique was resorted to monitor crack propagation and growth rates. Fracture toughness, fracture energy, and flexibility index were compared with those of traditional foamed matrix asphalt cold recycled mixture (FMCM). The results show that, under the same test temperature, the FRCM exhibits slower crack propagation; larger peak load; and higher fracture toughness, fracture energy, and flexibility index in comparison with the FMCM. These improvements are more pronounced at low temperatures. For both mixtures, fracture toughness and fracture energy are decreased with increasing the temperature, while the flexibility index shows the opposite trend. The rigid zone accounts for a larger portion of fracture energy at low temperatures. The findings provide technical references for improving the cracking resistance of cold recycled asphalt layers using rubberized asphalt. Full article

This study examines the impact of laser power on the microstructure and fracture behavior of IN718 specimens fabricated using laser powder bed fusion. Single-edge notched bend specimens were fabricated with varying laser power from 140 W to 260 W, and their fracture behavior was analyzed following the ASTM E1820-23b standard. The porosity and grain morphology remained unaffected by the presence of a notch parallel to the build direction. An elastic–plastic fracture mechanics approach was used to measure J-R curves, which quantify the energy required for crack propagation. Crack initiation and growth during quasistatic loading were monitored using image analysis. The results revealed a strong correlation between crack initiation and propagation, type of porosity, and relative density. The specimen printed with the optimal laser power of 180 W demonstrated the highest relative density and the greatest resistance to crack propagation. Large non-spherical defects formed due to lack-of-fusion at lower laser power are more detrimental to the crack propagation resistance. Full article

This study established a dynamic impact simulation system for a coral limestone cement composite subjected to bidirectional stress confinement conditions by using a coupled method of continuous medium FDM (a coupled continuum-discontinuum approach integrating finite difference continuum modeling (FDM) and the discrete element method (DEM) granular analysis), and verified its accuracy through indoor experiments. The study first conducted dynamic mechanical performance tests on reef limestone concrete using an SHPB experimental device, exploring the effects of the strain-rate governed high-rate response, energy evolution, and failure modes. Subsequently, an FDM-DEM coupled model was used to simulate the impact-induced behavior of concrete at multiaxial stress conditions and constraint conditions, analyzing the strain-rate dependent performance of concrete exposed to biaxial monotonic loading. Test outcomes indicate that the increase in strain rate significantly enhanced the dynamic peak stress, and the collapse behavior shifted from type I to type II. As static loading in the σ₂ direction increased, the dynamic peak stress in the σ₁ direction decreased, while the dynamic peak stress in the σ₂ direction increased, indicating that the constraint stress in the σ₂ direction had an inhibitory effect on the sample’s failure. Through the time-history monitoring and analysis of cracks, it was found that the internal crack growth rate accelerated as the stress increased, while the crack growth tended to stabilize when the stress decreased. Additionally, this study explored the effect of stress constraints on the fragmentation patterns, revealing changes in the failure modes and crack distributions of the sample under different stress states, providing a theoretical basis and technical support for island and reef construction and engineering protection. Full article

The Institute of Plasma Physics Chinese Academy of Sciences (ASIPP) is currently engaged in the design of a compact fusion device with a fusion power gain (Q) exceeding one. Due to space limitation for the device, the conductor jacket of the central solenoid (CS) magnet experiences significant electromagnetic stress. Therefore, a higher strength stainless steel known as modified N50 (CHSN01) is utilized for manufacturing the jacket. To effectively heat the plasma, the CS magnet within the device requires operation with alternating current. It is crucial to monitor fatigue crack growth caused by stress cycles in the CS jacket and assess its severity in order to ensure the safety and reliability of the fusion device. In this study, a finite element method is applied to establish a functional relationship between the stress intensity factor range ∆K and the jacket defect depth a precisely based on actual cyclic loads experienced by CS magnet operation. Experimental investigations are conducted to determine fatigue crack growth rates (FCGRs) at 4.2 Kelvin (K) for the CHSN01 jacket. The maximum likelihood estimation method is employed to calculate the probability equations of FCGRs with a random variable description. Consequently, it is possible to determine the maximum allowable initial defect size for a jacket to withstand 60,000 plasma pulses, which will serve as an input parameter for non-destructive testing of jackets. Full article

Non-isothermal differential scanning calorimetry (DSC) and Raman microscopy were used to study the crystallization behavior of the 20–50 μm amorphous nifedipine (NIF) powder. In particular, the study was focused on the diffusionless glass-crystal (GC) growth mode occurring below the glass transition temperature (T_g). The exothermic signal associated with the GC growth was indeed directly and reproducibly recorded at heating rates q⁺ ≤ 0.5 °C·min⁻¹. During the GC growth, the α_p polymorphic phase was exclusively formed, as confirmed via Raman microscopy. In addition to the freshly prepared NIF samples, the crystallization of the powders annealed for 7 h at 20 °C was also monitored—approx. 50–60% crystallinity was achieved. For the annealed NIF powders, the confocal Raman microscopy verified a proportional absence of the crystalline phase on the sample surface (indicating its dominant formation along the internal micro-cracks, which is characteristic of the GC growth). All DSC data were modeled in terms of the solid-state kinetic equation paired with the autocatalytic model; the kinetic complexity was described via reaction mechanism based on the overlap of 3–4 independent processes. The kinetic trends associated with decreasing q⁺ were identified, confirming the temperature-dependent kinetic behavior, and used to calculate a theoretical kinetic prediction conformable to the experimentally performed 7 h annealing at 20 °C. The theoretical model slightly underestimated the true extent of the GC growth, predicting the crystallinity to be 35–40% after 7 h (such accuracy is still extremely good in comparison with the standard kinetic approaches nowadays). Further research in the field of kinetic analysis should thus focus on the methodological ways of increasing the accuracy of considerably extrapolated kinetic predictions. Full article

The rapid growth of solar photovoltaic (PV) installations worldwide has increased the need for the effective monitoring and maintenance of these vital renewable energy assets. PV systems are crucial in reducing greenhouse gas emissions and diversifying electricity generation. However, they often experience faults and damage during manufacturing or operation, significantly impacting their performance, while thermal infrared imaging provides a promising non-invasive method for detecting common defects such as hotspots, cracks, and bypass diode failures, current deep learning approaches for fault classification generally rely on computationally intensive architectures or closed-source solutions, constraining their practical use in real-time situations involving low-resolution thermal data. To tackle these challenges, we introduce SlantNet, a lightweight neural network crafted to classify thermal PV defects efficiently and accurately. At its core, SlantNet incorporates an innovative Slant Convolution (SC) layer that utilizes slant transformation to enhance directional feature extraction and capture subtle thermal gradient variations essential for fault detection. We complement this architectural advancement with a thermal-specific image enhancement augmentation strategy that employs adaptive contrast adjustments to bolster model robustness under the noisy and class-imbalanced conditions typically encountered in field applications. Extensive experimental validation on a comprehensive solar panel defect detection benchmark dataset showcases SlantNet’s exceptional performance. Our method achieves a 95.1% classification accuracy while reducing computational overhead by approximately 60% compared to leading models. Full article

The swift advancement in monitoring and evaluation systems for road pavement conditions highlights the crucial role that this field plays in ensuring the sustainability of roads. This, in turn, contributes to the growth and prosperity of nations and enables users to enjoy the highest levels of luxury and comfort. Despite numerous studies and ongoing research, finding the most precise and efficient monitoring systems to determine the type and severity of road defects, their causes, and appropriate treatments remains a challenge. This study proposes a system that employs a camera to create an application capable of evaluating road conditions with ease by taking images while driving over the road. Based on the results, the application was accurate in identifying road defects of different severity within the same category. The proposed method was compared to the Pavement Condition Index (PCI) method, and a significant match was found in determining the type and severity of each defect on the selected road sections. More clearly, the overall accuracy of detecting and classifying block cracks, alligator cracks, longitudinal cracks, and potholes was significant for detecting and classifying the patches. Full article

Navigable waterways play a vital role in the efficient transportation of millions of tons of cargo annually. Inland traffic must pass through a lock, which consists of miter gates. Failures and closures of these gates can significantly disrupt waterborne commerce. Miter gates often experience fatigue cracking due to their loading and welded connections. Repairing every crack can lead to excessive miter gate downtime and serious economic impacts. However, if the rate of crack growth is shown to be sufficiently slow, e.g., using Paris’ law, immediate repairs may be deemed unnecessary, and this downtime can be avoided. Paris’ law is often obtained from laboratory testing with detailed crack measurements of specimens with relatively simple geometry. However, Paris’ law parameters for an in situ structure will likely deviate from those predicted from physical testing due to variations in loading and materials and a far more complicated geometry. To improve Paris’ law parameter prediction, this research proposes a framework that utilizes (1) convenient vision-based tracking of crack evolution both in the laboratory and the field and (2) numerical model estimation of stress intensity factors (SIFs). This study’s methodology provides an efficient tool for Paris’ law parameter prediction that can be updated as more data become available through vision-based monitoring and provide actionable information about the criticality of existing cracks. Full article

Roads are a form of critical infrastructure, influencing economic growth, mobility, and public safety. However, the management, monitoring, and maintenance of road networks remain a challenge, particularly given limited budgets and the complexity of assessing widespread infrastructure. This Special Issue on “Road Detection, Monitoring, and Maintenance Using Remotely Sensed Data” presents innovative strategies leveraging remote sensing technologies, artificial intelligence (AI), and non-destructive testing (NDT) to optimize road infrastructure assessment. The ten papers published in this issue explore diverse methodologies, including novel deep learning algorithms for road inventory, novel methods for pavement crack detection, AI-enhanced ground-penetrating radar (GPR) imaging for subsurface assessment, high-resolution optical satellite imagery for unpaved road assessment, and aerial orthophotography for road mapping. Collectively, these studies demonstrate the transformative potential of remotely sensed data for improving the efficiency, accuracy, and scalability of road monitoring and maintenance processes. The findings highlight the importance of integrating multi-source remote sensing data with advanced AI-based techniques to develop cost-effective, automated, and scalable solutions for road authorities. As the first edition of this Special Issue, these contributions lay the groundwork for future advancements in remote sensing applications for road network management. Full article

The particle size-dependent processes of structural relaxation and crystal growth in amorphous nifedipine were studied by means of non-isothermal differential scanning calorimetry (DSC) and Raman microscopy. The enthalpy relaxation was described in terms of the Tool–Narayanaswamy–Moynihan model, with the relaxation motions exhibiting the activation energy of 279 kJ·mol⁻¹ for the temperature shift, but with a significantly higher value of ~500 kJ·mol⁻¹ being obtained for the rapid transition from the glassy to the undercooled liquid state (the latter is in agreement with the activation energy of the viscous flow). This may suggest different types of relaxation kinetics manifesting during slow and rapid heating, with only a certain portion of the relaxation motions occurring that are dependent on the parameters of a given temperature range and time frame. The DSC-recorded crystallization was found to be complex, consisting of four sub-processes: primary crystal growth of α_p and β_p polymorphs, enantiotropic β_p → β_p′ transformation, and β_p/β_p′ → α_p recrystallization. Overall, nifedipine was found to be prone to the rapid glass-crystal growth that occurs below the glass transition temperature; a tendency of low-temperature degradation of the amorphous phase markedly increased with decreasing particle size (the main reason being the increased number of surface and bulk micro-cracks and mechanically induced defects). The activation energies of the DSC-monitored crystallization processes varied in the 100–125 kJ·mol⁻¹ range, which is in agreement with the microscopically measured activation energies of crystal growth. Considering the potential correlations between the structural relaxation and crystal growth processes interpreted within the Transition Zone Theory, a certain threshold in the complexity and magnitude of the cooperating regions (as determined from the structural relaxation) may exist, which can lead to a slow-down of the crystal growth if exceeded. Full article