With the increasing demand for materials capable of withstanding extreme service environments in fields such as advanced manufacturing, aerospace, and nuclear energy, the development of materials combining high strength, hardness, and thermal stability has become highly significant. Chromium monoboride(CrB), owing to its unique crystal structure and excellent mechanical properties, has attracted considerable attention; however, its deformation and failure mechanisms under complex stress states remain unclear. In this work, first-principles calculations are employed, combined with electronic structure analysis, to investigate the mechanical response and microstructural evolution of CrB under uniaxial tension, pure shear, and shear coupled with normal stress. The results reveal pronounced tensile anisotropy: the tensile strength is highest along the [100] direction (69.92 GPa) and lowest along the [010] direction (44.69 GPa). The minimum pure shear strength (35.68 GPa) occurs along the (010)[100] direction. Under pure shear and low normal stress, the Cr-Cr bimetallic layers undergo interlayer slip at the critical shear strain, leading to a sudden stress drop. In contrast, under high normal compressive stress coupled with shear, the interlayer spacing between Cr-Cr bimetallic layers is significantly reduced, which enhances interlayer bonding and suppresses interlayer slip. As a result, strain energy accumulates within the crystal lattice, eventually causing an abrupt structural collapse and catastrophic failure. Further analysis shows that the effect of normal stress on shear strength is non-monotonic: it increases with pressure at low stresses but softens under high pressures. The sensitivity to normal stress varies significantly with crystallographic orientation, and the anisotropy is further amplified as pressure increases. This study elucidates the instability mechanisms of CrB under multiaxial stress, providing theoretical guidance and design reference for its applications in extreme environments.

Accident prevention in electric vehicles through battery state-of-health estimation based on GRU-HSIC.

Optimizing delamination imaging via full wavefield segmentation using augmented simulated wavefield data.

The catastrophic failure of thick composite cylindrical pressure hulls: Analytical, numerical and experimental investigations

ABSTRACT A strategy is proposed to enhance the mechanical properties of metallic glasses using multilayered composites with various initial free-volume gradient interfaces and validated by finite element modelling. We found that the ductility of the composites improves significantly with the increasing number of layers. The main factors and the underlying mechanisms are (a) the gradient interface with varying free volume densities that can reduce the local stress concentration, (b) size effects imposed by the layer thickness that limits the local shear and shear bands to grow critically longer and thicker to cause catastrophic failure, (c) the presence of interface barriers to increase the probability of blocking and retarding the shear banding, and (d) the heterogeneity introduced by the statistical distribution of free volumes. The results demonstrate that the multilayered composites are promising in solving the strength-ductility tradeoff in metallic glasses.

Structural Health Monitoring (SHM) of pipe infrastructures is of paramount importance to prevent catastrophic failures induced by defects such as corrosion. Conventional damage identification methodologies are frequently faced with challenges, including baseline dependency, limitations inherent in single-sensor data, and considerable economic expenditure. This paper presents a novel, baseline-free, multi-modal damage identification methodology developed for Level 3 assessment of multiple damages, encompassing their detection, localisation, and quantification. Initially, Level 1 damage identification is accomplished through observation of the Regional Resonance Pair (RRP) phenomenon. Subsequently, potential damage regions are predicted by a Multilayer Perceptron (MLP) model that uses vibration modal frequencies, generating a Macro-F1 score of 0.8131 on the test set; this prediction is then integrated with a high-precision local point cloud, acquired via Line Structured Light (LSL) technology, to achieve precise Level 2 damage location, with a reported error as low as 1.78%. Following localisation, Level 3 quantification of the damage is performed using point cloud registration, fusion, and voxelisation techniques, enabling accurate prediction of damage volume with a quantification error of merely 2.47%.

Abstract. The monitoring of large infrastructures such as bridges, dams, and Tailings Storage Facilities (TSFs) is critical for ensuring structural safety and preventing catastrophic failures. Traditional geodetic monitoring approaches, while accurate, are often labour-intensive, expensive, and impractical for large-scale or remote deployments. This study evaluates the capability of dual-frequency low-cost GNSS receivers (ublox ZED-F9R) integrated with a minicomputer to measure millimeter-scale movements over extended monitoring periods. Two measurement campaigns are conducted: a 16-hour short-term test and a 60-day long-term deployment. A rigid aluminium beam with photogrammetrically measured baseline served as ground truth for assessing positioning accuracy. Short-term experiments demonstrated sub-millimeter accuracy while the 60-day campaign achieved 3D baseline measurement accuracy and precision below 2 mm despite significant environmental variations. The results confirm that low-cost dual-frequency GNSS systems can reliably detect centimeter/year-level deformations, making them suitable for monitoring slow-moving processes in critical infrastructure. The collected data, including raw GNSS observations, processed coordinates, and meteorological data, is publicly available for research purposes at https://doi.org/10.5281/zenodo.17378723.

Background: Aboveground Storage Tanks (ASTs) are critical assets in the oil and gas sector, where maintaining their structural integrity is essential for operational safety, environmental protection, and cost-efficiency. In Kazakhstan, traditional time-based inspection (TBI) methods dominate, despite their inefficiency and inflexibility. The integration of Risk-Based Inspection (RBI) with advanced Non-Destructive Testing (NDT) technologies offers a promising alternative to optimize inspection intervals and improve asset management, especially considering regulatory limitations and economic pressures that intensified during the COVID-19 pandemic. Aim: To optimize re-inspection intervals for ASTs in Kazakhstan’s oil and gas industry by integrating RBI methodologies with advanced NDT technologies, particularly ROSEN TBIT Ultra, and to compare these with traditional inspection methods. Materials and methods: RBI methodology outlined in API RP 580 and 581, industrial data for the given tank X. Results: The integration of RBI and advanced NDT enabled prioritization of high-risk tanks, identification of localized corrosion mechanisms, and optimization of inspection intervals. Compared to the rigid TBI schedule, the proposed approach demonstrated higher inspection efficiency, lower resource wastage, and reduced risk of catastrophic failure, while aligning with global standards and local legal frameworks. Conclusion: By adopting RBI methodologies supported by technologies like ROSEN TBIT Ultra, Kazakhstan’s oil and gas industry can transition from fixed-interval inspections toward a predictive, risk-prioritized approach. This transition supports better asset integrity management, enhances safety, and contributes to long-term infrastructure reliability, especially critical for aging storage systems.

The methodology proposes an approach to the adaptive renovation of centrifugal multicyclone separators during declining production, without capital expenditure or hot work. The problem is demonstrated to be relevant, driven by the mismatch between gas-dynamic regimes and apparatus geometry, the growth of liquid carryover, and the risk of catastrophic failure of compressor equipment. The objective of the study is to develop and verify a computational–experimental procedure for selecting the active separation area by selectively plugging part of the centrifugal elements to maintain the operating point within the zone of maximum efficiency. The scientific novelty lies in introducing the efficiency triangle concept (velocity–pressure–design), using modified similarity correlations to determine critical velocities, and combining isokinetic probing with algorithmic tray configuration, implemented as a Python module. It is shown that application of the methodology makes it possible to restore the regulatory level of carry-over (<5 mg/m³), extend the service life of separation equipment, and reduce total costs by eliminating the need for separator replacement, with the intervention cost being less than 1 % of the price of a new separator. The methodology is intended for engineering and technical personnel at gas production and gas processing enterprises, as well as for design and service organizations involved in modernizing integrated gas treatment units.

To address the challenges of high cost, complex deployment, and difficulties in real-time early warning for small landslides near residential areas, a portable and low-cost high-frequency monitoring device based on Micro-Electro-Mechanical Systems (MEMSs) was developed, and an advanced warning algorithm based on anomaly intensity factors was constructed. The device is easy to install and can collect and transmit data to the cloud in real time. Equipped with edge computing capabilities, it can independently analyze data in the absence of network connectivity and transmit real-time early warning information to terminals within a range of 5 km. To verify the performance of the system, a large-scale outdoor landslide simulation test site was constructed, where slope failure was induced through artificial rainfall to obtain the complete process data from deformation to failure. The experimental results demonstrate that the proposed early warning algorithm effectively identified different stability levels, providing warnings up to 13 s prior to catastrophic failure, confirming the high sensitivity and reliability of the device. This study offers a cost-effective and efficient approach to landslide monitoring and early warning, with notable prospects for broader implementation in practice.

Gears, as critical components of rotating machinery, are prone to wear and fracture due to their complex structural dynamics and harsh operating conditions, leading to catastrophic failures, economic losses, and safety risks. AE technology enables real-time fault diagnosis by capturing stress wave emissions from material defects with high sensitivity. However, mechanical background noise significantly corrupts AE signals, while optimal selection of gear health indicators remains challenging, critically impacting fault feature extraction accuracy. This study develops an adaptive feature extraction method for fault diagnosis using AE. Through gear fault simulation experiments, VMD analyzes mode number and penalty factor effects on signal decomposition. Correlation coefficient-based reconstruction optimization is implemented. For feature selection challenges, SVM-RFE enables adaptive parameter ranking. Finally, SVM with optimized kernel parameters achieves effective fault classification. Optimized VMD enhances signal decomposition, while SVM-RFE reduces feature dimensionality, addressing manual selection uncertainty and computational redundancy. Experimental results demonstrate superior accuracy in gear fault classification. This study proposes an AE-based adaptive feature extraction method with three innovations: (1) establishing VMD parameter–decomposition quality relationships; (2) developing an SVM-RFE feature selection framework; (3) achieving high-accuracy gear fault classification. The method provides a novel technical approach for rotating machinery diagnostics with significant engineering value.

Transmission tower guy wires are critical flexible tension members ensuring the stability and safe operation of overhead power transmission networks. However, these components are vulnerable to external impacts from falling rocks, ice masses, and other natural hazards, which can cause excessive deformation, anchorage loosening, and catastrophic failure. Current design standards primarily consider static loads, lacking comprehensive models for predicting dynamic impact responses. This study presents a theoretical model for predicting the peak impact response of guy wires by modeling the impact process as a point mass impacting a nonlinear spring system. Using an energy-based elastic potential method combined with cable theory, analytical solutions for axial force, displacement, and peak impact force are derived. Newton–Cotes numerical integration solves the implicit function to obtain closed-form solutions for efficient prediction. Validated through finite element simulations, deviations of peak displacement, peak impact force, and peak axial force between theoretical and numerical results are within ±4%, ±18%, and ±4%, respectively. Using the validated model, parametric studies show that increasing the inclination angle from 15° to 55° slightly reduces peak displacement by 2–4%, impact force by 1–13%, and axial force by 1–10%. Higher prestress (100–300 MPa) decreases displacement and impact force but increases axial force. Longer lengths (15–55 m) cause linear displacement growth and nonlinear force reduction. Impacts near anchorage points help control displacement risks, and impact velocity generally has a more significant influence on response characteristics than impactor mass. This model provides a scientific basis for impact-resistant design of power grid infrastructure and guidance for optimizing de-icing strategies, enhancing transmission system safety and reliability.

Fatigue-related ‘rotor crack’ can cause catastrophic failure if neglected. Thus, IoT-enabled AI-based predictive maintenance for fault detection and diagnosis is explored. Training and testing AI models under similar conditions improves their prediction performance. On variable speed machines, loss of performance occurs when the testing speed differs from the training speed. This research addresses significant performance loss issues using convolutional neural network (CNN)-based transfer learning models. The main causes of performance loss are domain shift, overfitting, data class imbalance, low fault data availability, and biassed prediction. All the above difficult issues make CNN-based fault prediction systems function badly under varying operating conditions. The proposed methodology addresses all domain adaptation challenges. The proposed methodology was tested by collecting vibration data from an experimental rotor system under varied operating conditions. The proposed methodology outperforms classical machine learning (ML) and deep learning (DL) models, overcoming the overfitting issue with optimised hyperparameters, achieving a prediction accuracy of 99.5%. Under varying operating conditions, it outperforms with a prediction accuracy of 93.2%, and in the ‘data class imbalanced’ scenario, the maximal transfer learning capability achieved was 84.4% with the highest F1-Score. Thus, CNN-based transfer learning enables industrial variable speed machines diagnose rotor crack flaws better than ML and DL models.

Financial technology systems demand unprecedented reliability standards where downtime directly impacts revenue, regulatory compliance, and customer trust. Traditional disaster recovery mechanisms, characterized by lengthy failover procedures and manual interventions, fail to meet modern distributed architecture requirements composed of hundreds of interdependent microservices. This article presents a transformative disaster recovery paradigm leveraging microservice state snapshots, declarative infrastructure patterns, and automated orchestration to achieve near-instantaneous recovery during catastrophic regional failures. The method treats infrastructure and application state as versioned, immutable artifacts, enabling deterministic reconstruction across heterogeneous cloud environments. Storage networking technologies enable continuous state synchronization across geographically distributed regions. Blue-green deployment patterns maintain continuously validated standby environments that eliminate infrastructure provisioning delays during emergencies. Database shadowing through logical replication preserves transactional consistency while enabling flexible failover topologies. Chaos engineering practices systematically validate recovery mechanisms through controlled failure injection across distributed system layers. Multi-cloud architectures reduce correlated failure modes by distributing workloads across independent infrastructure providers. Continuous validation frameworks transform disaster recovery from periodic compliance exercises into engineering disciplines with measurable reliability characteristics. This paradigm fundamentally reconceptualizes disaster recovery as an automated, continuous operational concern rather than an emergency response procedure, enabling financial services systems to achieve transparent regional failover capabilities where outages become imperceptible to end users while maintaining strict transactional consistency and regulatory compliance requirements.

Machining nickel-based superalloys, such as Inconel 718, poses a significant technological challenge due to their high-temperature strength and low thermal conductivity, leading to rapid tool wear. This paper presents a comprehensive comparative analysis of two roughing strategies: high-feed milling and plunge milling, utilizing a unique custom-designed milling head. The primary objective was to evaluate the impact of tool material reinforcement on the process by comparing SiC whisker-reinforced ceramic inserts (CW100) with non-reinforced inserts (CS300). The experiment involved measuring cutting force components, power consumption, and analyzing tool wear progression (VBB) and mechanisms. Results showed that the presence of the reinforcing phase is critical for reducing the axial force component (Fz), particularly in plunge milling, where CW100 inserts achieved a 30–35% force reduction and avoided the catastrophic failure observed in non-reinforced ceramics. Microscopic analysis confirmed that composite inserts undergo predictable abrasive wear, whereas CS300 inserts are prone to brittle fracture and spalling. Multi-criteria optimization using Grey Relational Analysis (GRA) identified high-feed milling with reinforced inserts as the most efficient strategy, while also positioning plunge milling with composites as a competitive, less energy-intensive alternative.

Abstract We introduce a framework for the enhanced estimation of photometric redshifts using Self-Organising Maps (SOMs). Our method projects galaxy Spectral Energy Distributions (SEDs) onto a two-dimensional map, identifying regions that are sparsely sampled by existing spectroscopic observations. These under-sampled areas are then augmented with simulated galaxies, yielding a more representative spectroscopic training dataset. To assess the efficacy of this SOM-based data augmentation in the context of the forthcoming Legacy Survey of Space and Time (LSST), we employ mock galaxy catalogues from the OpenUniverse2024 project and generate synthetic datasets that mimic the expected photometric selections of LSST after one (Y1) and ten (Y10) years of observation. We construct 501 degraded realisations of synthetic spectroscopic surveys by sampling galaxy colours, magnitudes, redshifts, and spectroscopic success rates, in order to emulate the diverse compilation of spectroscopic datasets that may exist for LSST analysis. Augmenting the degraded mock datasets with simulated galaxies from the independent CosmoDC2 catalogues significantly improves the performance of our photometric-redshift estimates – particularly at high redshift (ztrue ≳ 1.5) – even in the presence of differences in the underlying galaxy SED modelling between the two catalogues. This improvement is manifested in notably reduced systematic biases and a decrease in catastrophic failures by up to approximately a factor of 2, along with a reduction in information loss in the conditional density estimations. These results underscore the effectiveness of SOM-based augmentation in refining photometric redshift estimation, thereby enabling more robust analyses in cosmology and astrophysics for the NSF-DOE Vera C. Rubin Observatory.

The Gomit Earth Dam, constructed for irrigation, is currently in a critical state due to structural damage and exposure of the clay core, posing a significant risk of catastrophic failure. This study simulates the potential breach flood under probable maximum flood (PMF) conditions and delineates flood inundation extents to assess impacts on downstream areas and inform mitigation strategies. The research employs five key software tools: Global Mapper, ArcGIS, HEC-RAS, HEC-GeoRAS, and RAS Mapper to model dam breach hydraulics and map flood inundation. Field-surveyed topographic data with 20-m interval cross-sections were used to create accurate terrain representations. Simulations were conducted for two scenarios: sunny day failure and PMF failure, with detailed flood hazard analysis focusing on the PMF scenario. Results indicate a peak breach outflow of 1914.26 m<sup>3</sup>/s, 1.28 times greater than sunny day failure and 18.65 times the PMF inflow, with flood depths ranging from 7.06 m near the dam to 0.72–1.58 m across overbanks downstream. Flow velocities reached up to 12.32 m/s, and the flood wave arrival time varied from 0.077 to 0.386 hours after breach initiation. The inundated area totals approximately 38.92 hectares, representing 32.44% of the irrigated command area, with significant implications for agriculture, infrastructure, and community safety. Approximately 26 households, totaling over 100 people, are at high risk of life-threatening impacts, food insecurity, and property damage. This study underscores the urgent need for structural maintenance, early warning systems, and community-based flood risk management. Limitations include a lack of observed flood data for model calibration and consideration of a single flood scenario. Future research should incorporate multiple breach scenarios, long-term monitoring, and the impacts of climate variability to enhance the preparedness and resilience of irrigation infrastructure.

Abstract Understanding the fracture toughness of weathered selenite is critical for assessing the stability of stone cultural heritage under environmental degradation. The three-point bending test is a key method for evaluating the mode I fracture toughness in such brittle materials. This work investigates the influence of weathering degree on the mechanical response, nonlinear deformation behavior, and acoustic emission (AE) characteristics of selenite rock under mode I loading conditions by three-point bending test. A three-dimensional grain-based model (3D-GBM), incorporating 3D Voronoi tessellation and a flat-joint contact model with unbonded contacts, was employed to simulate the heterogeneous crystalline microstructure and pre-existing microcracks typical of weathered selenite. Slightly weathered selenite samples collected from the Garisenda Tower in Bologna were utilized for microparameter calibration. Numerical models representing moderately and highly weathered conditions were developed by systematically increasing microcrack density and width. Numerical results demonstrate that increased weathering intensifies the nonlinear deformation behavior, manifested by more pronounced strain-softening and strain-hardening phases, larger failure displacements, and a marked reduction in mode I fracture toughness from 0.181 to 0.052 MPa·m 1/2 . Force chain analyses reveal a transition toward more heterogeneous stress transmission in highly weathered samples, where load-bearing is concentrated on fewer contacts, thereby promoting dispersed microcrack initiation and coalescence. AE analysis indicates that, with increasing weathering, the spatial distribution of AE events evolves from a localized fracture plane to a more diffuse and random pattern. Concurrently, the maximum AE magnitude decreases, and the b -value increases from 2.01 in slightly weathered samples to 3.22 in highly weathered ones, reflecting a shift from brittle to ductile failure mechanisms. A sharp decline in the b -value is observed near peak loading, serving as a potential precursor to impending catastrophic failure in weathered selenite. This work underscores the necessity of capturing microstructural heterogeneity and progressive damage processes to better understand weathering-induced degradation in crystalline rocks. The combined application of micromechanical modeling and AE monitoring provides a robust framework for evaluating and preserving stone cultural heritage materials subjected to natural weathering.

Abstract The main cable is the primary load-bearing component of a suspension bridge, continuously exposed to harsh environmental conditions, such as wind and rain, throughout the year. These adverse conditions contribute to varying degrees of degradation and damage to the main cable, necessitating regular inspections to prevent catastrophic failures. Traditional manual inspection methods not only suffer from low efficiency but also pose significant safety risks to personnel. To address these challenges and ensure the safe and effective inspection of suspension bridge main cables, this study introduces a novel cooperative climbing robot, designated as Main Cable Robot Version II (CCRobot-M-II), inspired by the locomotion of the inchworm. The robot employs an alternating opening and closing mechanism of four gripper sets, mimicking the inchworm’s movement to achieve efficient crawling along the suspension bridge handrails. This paper provides a comprehensive analysis of the structural design, key components, and motion mechanisms of CCRobot-M-II. A detailed force analysis of the robot’s crawling process is also presented, followed by the design of the control system and the development of an efficient motion control algorithm. Laboratory experiments demonstrate that the robot achieves a positional error of 0–0.64% during crawling, with a maximum average crawling speed of 7.6 m/min. Furthermore, the biomimetic design enables the robot to overcome obstacles up to 30 mm in height and possess the capability to handle suspension bridge cables with spans ranging from 740 to 1100 mm. Finally, CCRobot-M-II successfully conducted an inspection of the main cable on a suspension bridge, marking the world’s first successful deployment of a climbing robot for main cable inspection on a suspension bridge.

The durability and safety of concrete structures are crucial in civil engineering, requiring regular inspection and maintenance to prevent catastrophic failures. Traditional crack detection methods rely on manual visual inspections, which are time-consuming, labor-intensive, and susceptible to human errors. To overcome these limitations, this study presents an AI-driven autonomous crack detection and failure prediction system based on Convolutional Neural Networks (CNNs). The proposed deep learning model is trained on a dataset comprising four distinct categories: Without Crack, Longitudinal Crack, Oblique Crack, and Transverse Crack. By leveraging CNN-based feature extraction and classification, the system accurately identifies different crack types and provides predictive insights into structural health. The experimental results demonstrate that the model achieves high precision and recall, making it a reliable tool for real-time monitoring and preventive maintenance of concrete infrastructure. This research contributes to the advancement of structural health monitoring (SHM) by integrating artificial intelligence (AI) with civil engineering practices, thereby reducing human dependency, enhancing inspection efficiency, and ensuring long-term structural safety. DOI : https://doi.org/10.52783/pst.2868